code
stringlengths 1
13.8M
|
|---|
read_spectra = function(path,
format = NULL,
type = "target_reflectance",
extract_metadata = FALSE,
exclude_if_matches = NULL,
ignore_extension = FALSE) {
path_and_format = i_verify_path_and_format(path = path,
format = format,
exclude_if_matches = exclude_if_matches,
ignore_extension = ignore_extension)
path = path_and_format$i_path
format = path_and_format$i_format
if(format == "sig"){
if(type == "target_reflectance"){
refl_cols = 4
sample_type = "target"
} else if (type == "target_radiance") {
refl_cols = 3
sample_type = "target"
} else if (type == "reference_radiance") {
refl_cols = 2
sample_type = "reference"
} else {
stop("type must be either target_reflectance, target_radiance or reference_radiance")
}
result = i_read_ascii_spectra(path,
skip_until_tag = "data=",
sep_char = "",
header = FALSE,
wl_col = 1,
refl_cols = refl_cols,
divide_refl_by = 100)
spec = lapply(result, function(x) {
rf = sapply(x, `[`, "value")
rf = do.call(rbind, rf)
wl = x[[1]][ , "band" ]
nm = basename(names(x))
spectra(rf, wl, nm)
})
if(extract_metadata){
svc_meta_tags = c("name=", "instrument=", "integration=",
"scan method=", "scan coadds=", "scan time=",
"scan settings=", "optic=", "temp=",
"battery=", "error=", "units=", "time=",
"longitude=", "latitude=", "gpstime=",
"memory slot=", "inclinometer x offset=",
"inclinometer y offset=")
metadata = lapply(result, function(x){
i_read_ascii_metadata(file_paths = names(x),
sample_type = sample_type,
sep_char = ",",
max_lines = 40,
meta_tags = svc_meta_tags,
tag_sep = "=")
})
for(i in seq_along(spec)){
meta(spec[[i]]) = metadata[[i]]
}
}
}
if(format == "sed"){
if(type == "target_reflectance"){
refl_cols = c("Reflect. %", "Reflect. [1.0]")
divide_refl_by = c(100, 1)
sample_type = "target"
} else if (type == "target_radiance") {
refl_cols = "Rad. (Target)"
divide_refl_by = 1
sample_type = "target"
} else if (type == "reference_radiance") {
refl_cols = "Rad. (Ref.)"
divide_refl_by = 1
sample_type = "reference"
} else {
stop("type must be either target_reflectance, target_radiance or reference_radiance")
}
result = i_read_ascii_spectra(path,
skip_until_tag = "Data:",
sep_char = "\t",
header = TRUE,
wl_col = "Wvl",
refl_cols = refl_cols,
divide_refl_by = divide_refl_by,
check.names = FALSE)
spec = lapply(result, function(x) {
rf = sapply(x, `[`, "value")
rf = do.call(rbind, rf)
wl = x[[1]][ , "band" ]
nm = basename(names(x))
spectra(rf, wl, nm)
})
if(extract_metadata){
psr_meta_tags = c("Version:", "File Name:", "Instrument:", "Detectors:",
"Measurement:", "Date:","Time:", "Temperature (C):",
"Battery Voltage:", "Averages:", "Integration:", "Dark Mode:",
"Foreoptic:", "Radiometric Calibration:", "Units:", "band Range:",
"Latitude:", "Longitude:", "Altitude:", "GPS Time:", "Satellites:",
"Calibrated Reference Correction File:", "Channels:")
metadata = lapply(result, function(x){
i_read_ascii_metadata(file_paths = names(x),
sample_type = sample_type,
sep_char = ",",
max_lines = 40,
meta_tags = psr_meta_tags,
tag_sep = ":")
})
for(i in seq_along(spec)){
meta(spec[[i]]) = metadata[[i]]
}
}
}
if(format == "asd"){
spec = i_read_asd_spectra(path,
type = type,
divide_refl_by = 1)
}
if(length(spec) > 1){
message("Returning a list of `spectra` because some files had different number of bands or band values. If you want to make those data compatible, consider resampling (with resample) and then combining them (with combine)")
return(spec)
} else {
return(spec[[1]])
}
}
i_verify_path_and_format = function(path,
format = NULL,
exclude_if_matches = NULL,
ignore_extension = FALSE) {
i_path = path
f_exists = file.exists(i_path)
if(!all(f_exists)){
stop("Path(s) not found ", i_path[!f_exists])
}
is_dir = dir.exists(i_path)
if(any(is_dir)){
if(any(!is_dir)){
stop("Cannot mix directory and file paths.")
}
if(length(is_dir) > 1 ){
stop("Cannot read multiple directories at once. Please use a single directory as your path.")
}
i_path = dir(path = i_path,
full.names = TRUE)
if(length(i_path) == 0){
stop("The directory is empty.")
}
i_path = i_path[ ! dir.exists(i_path) ]
if(length(i_path) == 0){
stop("The directory only includes other directories. `path` should be the directory that includes the spectral files themselves.")
}
}
file_extensions = tolower(tools::file_ext(i_path))
format_lookup = c("sig", "sed", "asd")
if( ! is.null(format) ){
format_match = pmatch(tolower(format), format_lookup)
if(length(format_match) == 0){
stop("Files did not match any known format")
}
format = format_lookup[format_match]
} else {
extensions = file_extensions[file_extensions %in% format_lookup]
extensions = names(sort(table(extensions), decreasing = TRUE))
if(length(extensions) > 1){
stop("Multiple file formats found. Spectrolab can only read one file format at a time.")
}
if(length(extensions) == 0){
stop("Files did not match any known format")
}
format = extensions[1]
}
if(! ignore_extension ){
i_path = i_path[ grepl(format, file_extensions) ]
if(length(i_path) == 0){
stop("No files have the extension ", format)
}
}
if(!is.null(exclude_if_matches)){
bad_tag = exclude_if_matches
m = grepl(paste(bad_tag,collapse = "|"), i_path)
i_path = i_path[!m]
}
if(length(i_path) == 0){
stop("No paths left after removing bad spectra. Check your `exclude_if_matches` parameter")
}
list(i_path = i_path,
i_format = format)
}
i_read_ascii_spectra = function(file_paths,
skip_until_tag = NULL,
sep_char,
header,
wl_col,
refl_cols,
divide_refl_by,
...){
parse = function(x, tag = skip_until_tag) {
max_l = 40
skip = grep(tag, trimws(readLines(x, n = max_l)), fixed = TRUE)
if(length(skip) == 1){
return(utils::read.delim(x, skip = skip, sep = sep_char, header = header, check.names = FALSE))
} else if (length(skip) == 0){
stop(paste0("No match found for skip_until_tag in the first ", max_l, " lines"))
} else {
stop(paste0("More than one match found for skip_until_tag in the first ", max_l, " lines"))
}
}
if(length(refl_cols) > 1 && any(i_is_whole(refl_cols)) ){
stop("refl_cols cannot be a vector of indices.")
}
if(length(refl_cols) < length(divide_refl_by)) {
warning("Length of divide_refl_by should be either 1 or equals to the length of refl_cols. divide_refl_by has been prunned to length", length(refl_cols), ".")
divide_refl_by = rep(divide_refl_by, length.out = length(refl_cols))
}
data = lapply(file_paths, parse)
names(data) = file_paths
if(length(refl_cols) > 1) {
d = data[[1]]
m = colnames(d) %in% refl_cols
n = which(refl_cols %in% colnames(d))
if(all( !m )){
stop("refl_cols did not match any columns.")
}
if( sum(m) > 1 ){
stop("refl_cols matched more than one column.")
}
refl_cols = which(m)
divide_refl_by = divide_refl_by[n]
}
data = lapply(data, function(x){
data.frame("band" = x[ , wl_col],
"value" = x[ , refl_cols] / divide_refl_by )
})
wl_factor = unlist(
lapply(data, function(x){
paste0(x[ , "band"], collapse = "")
})
)
data = unname(split(data, wl_factor))
return(data)
}
i_read_ascii_metadata = function(file_paths,
sample_type,
max_lines,
sep_char,
meta_tags,
tag_sep){
message("reading metadata may take a while...")
mat = sapply(file_paths, function(x){
f_lines = trimws(readLines(x, n = max_lines))
pick = ifelse(test = sample_type == "target", yes = 2, no = 1)
meta_tags = setNames(meta_tags, meta_tags)
data = lapply(meta_tags, function(x){
y = f_lines[grep(paste0("^", x), f_lines)]
if(length(y) == 0){
return(NULL)
}
y = strsplit(gsub(x, "", y), sep_char)[[1]]
s = sort(rep( c(1,2) , length.out = length(y)))
if(length(s) == 1){
return(trimws(y))
} else {
return(trimws(split(y, s)[[pick]]))
}
})
names(data) = gsub(tag_sep, "", names(data))
unlist(data)
}, USE.NAMES = FALSE)
mat = as.data.frame( t(mat),
stringsAsFactors = FALSE,
row.names = FALSE,
check.names = FALSE)
mat = lapply(mat, type.convert, as.is = TRUE)
as.data.frame(mat, stringsAsFactors = FALSE, check.names = FALSE)
}
i_read_asd_spectra = function(file_paths,
type = "target_reflectance",
divide_refl_by){
ENDIAN = "little"
TYPES = c("raw", "reflectance", "radiance", "no_units", "irradiance",
"qi", "transmittance", "unknown", "absorbance")
DATA_FORMAT = c("numeric", "integer", "double", "unknown")
result = lapply(file_paths, FUN = function(f){
con = file(f, open = "rb")
seek(con, 186)
data_type = readBin(con, integer(), size = 1)
data_type = TYPES[data_type + 1L]
seek(con, 191)
band_start = readBin(con, numeric(), size = 4, endian = ENDIAN)
seek(con, 195)
band_step = readBin(con, numeric(), size = 4, endian = ENDIAN)
seek(con, 204)
n_bands = readBin(con, integer(), size = 2, endian = ENDIAN)
bands = seq(from = band_start,
to = band_start + n_bands * band_step - 1L,
by = band_step)
seek(con, 199)
data_format = readBin(con, integer(), size = 1)
data_format = DATA_FORMAT[data_format + 1L]
seek(con, 390)
integration_time = readBin(con, integer(), size = 4, endian = ENDIAN)
seek(con, 436)
swir1_gain = readBin(con, integer(), size = 2, endian = ENDIAN)
seek(con, 438)
swir2_gain = readBin(con, integer(), size = 2, endian = ENDIAN)
seek(con, 444)
splice1 = readBin(con, numeric(), size = 4, endian = ENDIAN)
seek(con, 448)
splice2 = readBin(con, numeric(), size = 4, endian = ENDIAN)
seek(con, where = 484)
spectrum = readBin(con, what = data_format, n = n_bands, endian = ENDIAN)
seek(con, 17710)
comment_nchar = readBin(con, integer(), size = 2, endian = ENDIAN)
seek(con, 17712 + comment_nchar)
white_ref = readBin(con, what = data_format, n = n_bands, endian = ENDIAN)
close(con)
if(data_type == "raw"){
s1 = bands <= splice1
s2 = bands > splice1 & bands <= splice2
s3 = bands > splice2
spectrum[s1] = spectrum[s1] / integration_time
spectrum[s2] = spectrum[s2] * swir1_gain / 2048
spectrum[s3] = spectrum[s3] * swir2_gain / 2048
white_ref[s1] = white_ref[s1] / integration_time
white_ref[s2] = white_ref[s2] * swir1_gain / 2048
white_ref[s3] = white_ref[s3] * swir2_gain / 2048
}
relative_reflectance = spectrum / white_ref
spec_name = gsub(pattern = ".asd$",
replacement = "",
x = basename(f),
ignore.case = TRUE)
if(type == "target_reflectance"){
result = cbind(bands, relative_reflectance, spec_name)
} else if (type == "target_radiance") {
result = spectra(bands, spectrum, spec_name)
} else if (type == "reference_radiance") {
result = spectra(bands, white_ref, spec_name)
} else {
stop("type must be either target_reflectance, target_radiance or reference_radiance")
}
})
wl = lapply(result, function(x){ x[ , 1] })
wl_factor = unlist(
lapply(wl, function(x){
paste0(x, collapse = "")
})
)
data = unname(split(result, wl_factor))
spec = lapply(data, function(x) {
rf = lapply(x, function(y){ y[ , 2 ] })
rf = do.call(rbind, rf)
wl = x[[1]][ , 1 ]
nm = sapply(x, function(y){ y[1 , 3 ] })
spectra(rf, wl, nm)
})
if(length(spec) > 1){
warning("Returning a list of `spectra` because some files had different number of bands or band values. If you want to make those data compatible, consider resampling (with resample) and then combining them (with combine)")
return(spec)
} else {
return(spec[[1]])
}
}
|
gap_cases <- function(alignment) {
m <- remove_align_mat_class(alignment)
any_gaps <- any(m[, 2] == '-')
if(!any_gaps) return('case 1')
is_again_gapped <- again_gapped(m)
if(!is_again_gapped) return('case 2')
else return('case 3')
}
|
context("test-plot_egor.R")
pdf(NULL)
test_that("plot plots egor objects", {
expect_error({
e <- make_egor(5, 5)
plot(
x = e,
x_dim = 2,
y_dim = 2,
ego_no = 1,
vertex_size_var = "age.years",
vertex_color_var = "age.years",
vertex_color_palette = "Greys",
vertex_color_legend_label = "Age",
edge_width_var = "weight",
edge_color_var = "weight",
edge_color_palette = "Greys",
highlight_box_col_var = "sex",
res_disp_vars = c("sex", "age")
)
plot(
x = e,
x_dim = 2,
y_dim = 2,
ego_no = 1,
vertex_size_var = "age.years",
vertex_color_var = "age.years",
vertex_color_palette = "Greys",
vertex_color_legend_label = "Mushi",
edge_width_var = "weight",
edge_color_var = "weight",
edge_color_palette = "Greys",
highlight_box_col_var = "sex",
res_disp_vars = c("sex", "age")
)
plot(
x = e,
ego_no = 1,
venn_var = "age",
pie_var = "country",
venn_colors = c("blue", "lightblue", "mistyrose",
"lightcyan"),
show_venn_labels = TRUE,
type = "egogram"
)
}, NA)
})
test_that("plot_egograms works with minimal arguments", {
expect_error({
e <- make_egor(net.count = 5, max.alters = 12)
plot_egograms(
x = e,
ego_no = 2,
venn_var = "sex",
pie_var = "country",
vertex_size_var = "age"
)
plot_egograms(
x = e,
ego_no = 2,
venn_var = "sex",
pie_var = "country"
)
}, NA)
})
test_that("plot_ego_graphs works with minmal arguments", {
expect_error({
e <- make_egor(5, 15)
plot_ego_graphs(e)
}, NA)
})
test_that("plot_ego_graphs works with vertex_color_var", {
expect_error({
e <- make_egor(15, 15)
plot_ego_graphs(e, vertex_color_var = "sex")
plot_ego_graphs(e,
vertex_color_var = "sex",
vertex_color_legend_label = "Sex")
}, NA)
})
test_that("plot_egograms doesn't fail on empty alters or aaties", {
e <- make_egor(5, 5)
e$aatie <-
e$aatie %>%
filter(.egoID != 1)
expect_error(plot_egograms(
x = e,
venn_var = "sex",
pie_var = "country",
show_venn_labels = TRUE
),
NA)
e <- make_egor(5, 5)
e$alter <-
e$alter %>%
filter(.egoID != 1)
expect_error(plot_egograms(
x = e,
venn_var = "sex",
pie_var = "country",
show_venn_labels = TRUE
),
NA)
})
test_that("plot_egograms plots with and without venn labels", {
expect_error({
e <- make_egor(5, 5)
plot_egograms(
e,
venn_var = "sex",
pie_var = "country",
show_venn_labels = FALSE
)
plot_egograms(
e,
venn_var = "sex",
pie_var = "country",
show_venn_labels = TRUE
)
}, NA)
})
test_that("plotting works when active data level is not ego",
{
expect_error({
e <- make_egor(5, 15) %>%
activate(alter)
plot_egograms(
e,
1,
x_dim = 1,
y_dim = 1,
"sex",
"country",
show_venn_labels = FALSE
)
plot_ego_graphs(e, 1)
e <- make_egor(5, 15) %>%
activate(aatie)
plot_egograms(
e,
1,
x_dim = 1,
y_dim = 1,
"sex",
"country",
show_venn_labels = FALSE
)
plot_ego_graphs(e, 1)
}, NA)
})
test_that("plot_ego_graphs is fast", {
expect_error({
plot_ego_graphs(make_egor(12, 16))
plot_ego_graphs(make_egor(120, 16))
}, NA)
})
test_that("plot_ego_gram adjusts node size according to venn count", {
expect_error({
e <- make_egor(5, 5)
plot_egograms(
x = e,
ego_no = 2,
venn_var = "sex",
pie_var = "country"
)
plot_egograms(
x = e,
ego_no = 2,
venn_var = "country",
pie_var = "age"
)
plot_egograms(
x = e,
ego_no = 2,
venn_var = "age",
pie_var = "age"
)
plot_egograms(
x = e,
ego_no = 2,
venn_var = "age",
pie_var = "age",
vertex_zoom = 2,
edge_zoom = 2
)
}, NA)
})
test_that("plot_ego_gram works with edge arguments", {
expect_error({
e <- make_egor(5, 32)
plot_egograms(
e,
ego_no = 1,
venn_var = "sex",
pie_var = "country",
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3
)
plot_ego_graphs(
x = e,
ego_no = 1,
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 2
)
}, NA)
})
test_that("plot_ego_gram works without pie_var/venn_var", {
expect_error({
e <- make_egor(5, 32)
plot_egograms(
e,
ego_no = 1,
venn_var = "sex",
pie_var = NULL,
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3
)
plot_egograms(
e,
ego_no = 1,
venn_var = NULL,
pie_var = "sex",
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3
)
}, NA)
expect_warning({
plot_egograms(
e,
ego_no = 1,
venn_var = NULL,
pie_var = NULL,
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3
)
})
})
test_that("plot_ego_gram plots empty levels of a factor variables for pies and venns",
{
expect_error({
e <- make_egor(50, 12)
e <- e %>%
activate(alter) %>%
mutate(age2 = as.character(age),
rating = sample(1:5, n(), replace = TRUE))
e <-
e %>%
mutate(rating.f = factor(rating, levels = 1:5))
plot_egograms(
e,
ego_no = 4,
venn_var = "rating",
pie_var = "age2",
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3,
venn_gradient_reverse = FALSE
)
plot_egograms(
e,
ego_no = 1,
venn_var = "rating",
pie_var = "sex",
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3,
)
plot_egograms(
e,
ego_no = 1,
venn_var = "rating",
pie_var = "sex",
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3,
vertex_label_var = NULL
)
}, NA)
layout_egogram(e$alter$.altID, e$alter$age, e$alter$rating.f)
expect_warning({
plot_egograms(
e,
ego_no = 1,
venn_var = NULL,
pie_var = NULL,
vertex_color_var = "sex",
edge_color_var = "weight",
edge_width_var = "weight",
edge_zoom = 3
)
})
})
test_that("ego-alter weights are plotted",
{
e <- make_egor(5, 12)
e$alter$weight <-
sample(1:5 / 5, nrow(e$alter), replace = TRUE)
expect_error({
plot_ego_graphs(e, include_ego = TRUE)
plot_ego_graphs(x = e, edge_width_var = "weight", include_ego = TRUE)
}, NA)
expect_error({
plot_ego_graphs(e, include_ego = TRUE)
plot_ego_graphs(e, edge_color_var = "weight", include_ego = TRUE)
}, NA)
})
test_that("egograms with many venns produce adequatly sized nodes",
{
data("transnat")
transnat <-
transnat %>%
activate(alter) %>%
mutate(test_var = sample(1:12, nrow(.$alter), replace = TRUE))
expect_error(
plot_egograms(
transnat,
venn_var = "test_var",
pie_var = "sex",
vertex_zoom = 1,
vertex_label_var = NULL
),
NA
)
})
test_that("egograms with `include_ego = TRUE` work properly", {
expect_error(plot_egograms(x = egor32,
venn_var = "age",
pie_var = "country",
include_ego = TRUE), NA)
expect_error(plot_egograms(x = egor32,
venn_var = "age",
pie_var = "country",
include_ego = FALSE), NA)
})
test_that("egograms with reverse ordered alters plot correctly", {
plot_egograms(egor32, venn_var = "age", pie_var = "country")
egor32 %>%
activate(alter) %>%
arrange(.egoID, desc(.altID)) %>%
plot_egograms(venn_var = "age", pie_var = "country")
ego <-
tibble(egoID = c("Hans", "Peter", "Klaus"),
var = c(1, 2, 3))
alter <-
tibble(
egoID = c(rep("Hans", 3), rep("Peter", 3), rep("Klaus", 3)),
alterID = c(
"Mary",
"Paul",
"Susana",
"Irna",
"Laser3000",
"Pferd",
"Ross",
"Ricky",
"Herald"
),
var1 = sample(1:3, 9, replace = TRUE),
var2 = sample(1:3, 9, replace = TRUE)
)
e1 <- egor(alter, ego)
plot_egograms(e1,
venn_var = "var1",
pie_var = "var2",
ego_no = 2)
expect_error(e1 %>%
activate(alter) %>%
arrange(.egoID, desc(.altID)) %>%
plot_egograms(venn_var = "var1",
pie_var = "var2",
ego_no = 2), NA)
})
test_that("plot_egograms removes and warns for alters with missing data in pie/venn_var", {
t1 <- make_egor(5, 5) %>%
activate(alter) %>%
mutate(test = c(NA, sample(c("test", "test2"), nrow(.$alter)-1, replace = TRUE)))
expect_warning(plot_egograms(t1, 1, venn_var = "country", pie_var = "test"))
expect_warning(plot_egograms(x = t1, ego_no = 1, venn_var = "test", pie_var = "country"))
})
test_that("plot_egograms() argument ascending_inwards works", {
e1 <- make_egor(15, 15)
expect_error(plot_egograms(e1, 1, venn_var = "age", pie_var = "sex", ascending_inwards = FALSE), NA)
expect_error(plot_egograms(e1, 1, venn_var = "age", pie_var = "sex", ascending_inwards = TRUE), NA)
})
dev.off()
|
bedr.setup <- function(datasets = "all", data.dir = paste0(Sys.getenv("HOME"),"/bedr/data")) {
config.file <- paste0(Sys.getenv("HOME"),"/bedr/config.txt");
if (!file.exists(data.dir)) dir.create(data.dir, recursive = TRUE);
if (!file.exists(config.file)) file.create(config.file);
config.bedr$data.dir <- data.dir;
cat(as.yaml(config.bedr), file = config.file, append = TRUE);
download.datasets(datasets, data.dir);
return();
}
|
expected <- eval(parse(text="structure(\"range specified by \", Rd_tag = \"TEXT\")"));
test(id=0, code={
argv <- eval(parse(text="list(\"\\\\\", \"\\\\bsl\", structure(\"range specified by \", Rd_tag = \"TEXT\"), FALSE, FALSE, TRUE, TRUE)"));
.Internal(gsub(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]]));
}, o=expected);
|
library(knotR)
filename <- "8_15.svg"
a <- reader(filename)
Mver <- matrix(c(
04,16,
15,05,
09,11,
06,14,
08,12,
03,01,
07,13
),ncol=2,byrow=TRUE)
sym815 <- symmetry_object(a,Mver=Mver,xver=c(2,10))
ou815 <- matrix(c(
16,05,
12,01,
02,14,
07,03,
04,09,
06,15,
10,07,
14,11
),ncol=2,byrow=TRUE)
jj <-
knotoptim(filename,
symobj = sym815,
ou = ou815,
prob = 0,
iterlim=1000,print.level=2,hessian=FALSE
)
write_svg(jj, filename,safe=FALSE)
dput(jj,file=sub('.svg','.S',filename))
|
kRp_corp_freq <- setClass("kRp.corp.freq",
representation=representation(
meta="data.frame",
words="data.frame",
desc="data.frame",
bigrams="data.frame",
cooccur="data.frame",
caseSens="logical"
),
prototype(
)
)
setMethod("initialize", "kRp.corp.freq",
function(
.Object,
meta=data.frame(
meta=character(),
value=character()
),
words=data.frame(
num=numeric(),
word=character(),
lemma=character(),
tag=character(),
wclass=character(),
lttr=numeric(),
freq=numeric(),
pct=numeric(),
pmio=numeric(),
log10=numeric(),
rank.avg=numeric(),
rank.min=numeric(),
rank.rel.avg=numeric(),
rank.rel.min=numeric(),
inDocs=numeric(),
idf=numeric()
),
desc=data.frame(
tokens=character(),
types=character(),
words.p.sntc=numeric(),
chars.p.sntc=numeric(),
chars.p.wform=numeric(),
chars.p.word=numeric()
),
bigrams=data.frame(
token1=character(),
token2=character(),
freq=numeric(),
sig=numeric()
),
cooccur=data.frame(
token1=character(),
token2=character(),
freq=numeric(),
sig=numeric()
),
caseSens=FALSE
){
slot(.Object, "meta") <- meta
slot(.Object, "words") <- words
slot(.Object, "desc") <- desc
slot(.Object, "bigrams") <- bigrams
slot(.Object, "cooccur") <- cooccur
slot(.Object, "caseSens") <- caseSens
validObject(.Object)
return(.Object)
}
)
setValidity("kRp.corp.freq", function(object){
meta <- slot(object, "meta")
words <- slot(object, "words")
desc <- slot(object, "desc")
bigrams <- slot(object, "bigrams")
cooccur <- slot(object, "cooccur")
meta.names <- dimnames(meta)[[2]]
words.names <- dimnames(words)[[2]]
desc.names <- dimnames(desc)[[2]]
bigrams.names <- dimnames(bigrams)[[2]]
cooccur.names <- dimnames(cooccur)[[2]]
if(!identical(meta.names, c("meta", "value"))){
stop(simpleError("Invalid object: Wrong column names in slot \"meta\"."))
} else {}
if(!identical(words.names, c(
"num", "word", "lemma", "tag", "wclass", "lttr", "freq", "pct", "pmio", "log10",
"rank.avg", "rank.min", "rank.rel.avg", "rank.rel.min", "inDocs", "idf"))){
stop(simpleError("Invalid object: Wrong column names in slot \"words\"."))
} else {}
if(!identical(desc.names, c("tokens", "types", "words.p.sntc", "chars.p.sntc", "chars.p.wform", "chars.p.word"))){
stop(simpleError("Invalid object: Wrong column names in slot \"desc\"."))
} else {}
if(!identical(bigrams.names, c("token1", "token2", "freq", "sig"))){
stop(simpleError("Invalid object: Wrong column names in slot \"bigrams\"."))
} else {}
if(!identical(cooccur.names, c("token1", "token2", "freq", "sig"))){
stop(simpleError("Invalid object: Wrong column names in slot \"cooccur\"."))
} else {}
return(TRUE)
})
|
probs.l1mstate = function(object, longdt, tmat, predt, direction=c("forward","fixedhorizon")){
transit = tmat2(tmat)
numtrans = nrow(transit)
x1 = object$Haz
unt = sort(unique(x1$time))
K = unique(x1$trans)
k=min(K)
xk = x1[x1$trans==k,]
xk_new = matrix(rep(0,3*length(unt)), nrow = length(unt))
xk_new[,1] = unt
xk_new[,2] = ifelse(xk_new[,1] %in% xk$time, xk$Haz, 0)
xk_new[,2] = cumsum(xk_new[,2])
xk_new[,3] = rep(k, length(unt))
x1_new = xk_new
for(k in K){
if(k!=min(K)){
xk = x1[x1$trans==k,]
xk_new = matrix(rep(0,3*length(unt)), nrow = length(unt))
xk_new[,1] = unt
xk_new[,2] = ifelse(xk_new[,1] %in% xk$time, xk$Haz, 0)
xk_new[,2] = cumsum(xk_new[,2])
xk_new[,3] = rep(k, length(unt))
x1_new = rbind(x1_new, xk_new)
}
}
x1_new = data.frame(x1_new)
names(x1_new)=c("time", "cumHaz", "trans")
stackhaz = x1_new
for (i in 1:numtrans)
stackhaz$dhaz[stackhaz$trans==i] = diff(c(0,stackhaz$cumHaz[stackhaz$trans==i]))
if (direction=="forward"){
stackhaz = stackhaz[stackhaz$time > predt,]
}else{
stackhaz = stackhaz[stackhaz$time <= predt,]
}
untimes = sort(unique(stackhaz$time))
TT = length(untimes)
S = nrow(tmat)
if (direction=="forward") {
res = array(0,c(TT+1,S+1,S))
res[1,1,] = predt
for (j in 1:S) res[1, 1+j,] = rep(c(0,1,0), c(j-1,1,S-j))
}else{
if (predt %in% untimes) {
res = array(0,c(TT+1,S+1,S))
res[TT+1,1,] = predt
for (j in 1:S) res[TT+1, 1+j,] = rep(c(0,1,0), c(j-1,1,S-j))
}else{
res = array(0,c(TT+2,S+1,S))
res[TT+1,1,] = max(untimes)
for (j in 1:S) res[TT+1, 1+j,] = rep(c(0,1,0), c(j-1,1,S-j))
res[TT+2,1,] = predt
for (j in 1:S) res[TT+2, 1+j,] = rep(c(0,1,0), c(j-1,1,S-j))
}
}
P = diag(S)
for (i in 1:TT)
{
idx = ifelse(direction=="forward",i,TT+1-i)
tt = untimes[idx]
Haztt = stackhaz[stackhaz$time==tt,]
lHaztt = nrow(Haztt)
IplusdA = diag(S)
for (j in 1:lHaztt){
from = transit$from[transit$transno==Haztt$trans[j]]
to = transit$to[transit$transno==Haztt$trans[j]]
IplusdA[from, to] = Haztt$dhaz[j]
IplusdA[from, from] = IplusdA[from, from] - Haztt$dhaz[j]
}
if (any(diag(IplusdA)<0))
warning("Warning! Negative diagonal elements of (I+dA); the estimate may not be meaningful. \n")
if (direction=="forward")
{
P = P %*% IplusdA
}else{
P = IplusdA %*% P
}
if (direction=="forward") {
res[idx+1,1,] = tt
res[idx+1,2:(S+1),] = t(P)
}else{
res[idx,1,] = ifelse(i==TT,0,untimes[TT-i])
res[idx,2:(S+1),] = t(P)
}
}
res2 = vector("list", S)
for (s in 1:S) {
tmp = as.data.frame(res[,,s])
if (min(dim(tmp))==1) tmp = res[,,s]
names(tmp) = c("time",paste("pstate",1:S,sep=""))
res2[[s]] = tmp
}
res2$trans = x1$trans
res2$tmat = tmat
return(res2)
}
tmat2 = function(tmat)
{
dm = dim(tmat)
mx = max(tmat,na.rm=TRUE)
res = matrix(NA,mx,3)
res[,1] = 1:mx
transvec = as.vector(tmat)
for (i in 1:mx) {
idx = which(transvec==i)
res[i,2:3] = c(idx %% dm[2],idx %/% dm[2] + 1)
}
res = data.frame(res)
names(res) = c("transno","from","to")
if (!is.null(dimnames(tmat))) {
states = dimnames(tmat)[[1]]
res$fromname = states[res$from]
res$toname = states[res$to]
}
return(res)
}
|
print.boot.two <-
function(x, ...)
{
test <- !is.null(x$Null)
hist(x$Boot.values,breaks=20,xlab=paste("bootstrap",x$Statistic),
main=paste("Histogram of bootstrap ",x$Statistic,"s",sep=""))
abline(v=x$Observed,col="2")
abline(v=x$Mean,col="3")
abline(v=c(x$Confidence.limits),col="4")
if (test) abline(v=x$Null,col="5")
leg.text <- if (test) expression(Observed,Mean.boots,Confidence.interval,Null.value)
else expression(Observed,Mean.boots,Confidence.interval)
legend("topright",leg.text,col=2:5,lwd=2,cex=.6)
cat("\n\n",x$Header,"\n\n")
if (x$Stacked || !is.null(x$Variable))
{if (identical("proportion",x$Parameter))
print(data.frame(SUMMARY="STATISTICS",Variable=x$Variable,
Pop.1=x$Pop.1,Pop.2=x$Pop.2,n.1=x$n.1,
x.1=x$Observed.1*x$n.1,n.2=x$n.2,x.2=x$Observed.2*x$n.2,
Statistic=x$Statistic,Observed=x$Observed),row.names=FALSE)
else
print(data.frame(SUMMARY="STATISTICS",Variable=x$Variable,
Pop.1=x$Pop.1,Pop.2=x$Pop.2,n.1=x$n.1,
n.2=x$n.2,Statistic=x$Statistic,Observed=x$Observed),row.names=FALSE)}
else
{if (identical("proportion",x$Parameter))
print(data.frame(SUMMARY="STATISTICS",Pop.1=x$Pop.1,
Pop.2=x$Pop.2,n.1=x$n.1, x.1=x$Observed.1*x$n.1,
n.2=x$n.2,x.2=x$Observed.2*x$n.2,Statistic=x$Statistic,
Observed=x$Observed),row.names=FALSE)
else
print(data.frame(SUMMARY="STATISTICS",Pop.1=x$Pop.1,
Pop.2=x$Pop.2,n.1=x$n.1,n.2=x$n.2,Statistic=x$Statistic,
Observed=x$Observed),row.names=FALSE)}
cat("\n")
print(data.frame(BOOTSTRAP="SUMMARY",Replications=x$Replications,Mean=x$Mean,
SE=x$SE,Bias=x$Bias,Percent.bias=x$Percent.bias),row.names=FALSE)
cat("\n")
if (test) print(data.frame(HYPOTHESIS="TEST",Null=x$Null,
Alternative=x$Alternative,P.value=x$P.value),row.names=FALSE)
if (test) cat("\n")
print(data.frame(CONFIDENCE="INTERVAL",Level=x$Level,Type=x$Type,
Confidence.interval=x$Confidence.interval),row.names=FALSE)
cat("\n\n")
}
|
HMR.fit <- function (t, C, A = 1, V, serie = "", k = log(1.5), verbose = TRUE, plot = FALSE, maxiter = 100, ...) {
tryCatch({
stopifnot(length(t) > 3)
fit <- withWarnings(nls(C ~ cbind(1, exp(-exp(k)*t)/(-exp(k)*V/A)),
start= list(k=k), algorithm = "plinear",
control=nls.control(maxiter=maxiter, minFactor=1e-10)))
w <- if (is.null(fit$warnings)) "" else fit$warnings[[1]]$message
fit <- fit$value
fitsum <- withWarnings(summary(fit))
if (w == "") w <- if (is.null(fitsum$warnings)) "" else fitsum$warnings[[1]]$message
fitsum <- fitsum$value
fitsumCoef <- fitsum$coef
try({
if (plot) {
curve(predict(fit, newdata = data.frame(t = x)),
from = min(t), to = max(t), add = TRUE, col = "red")
}}, silent = TRUE)
res <- list(
f0 = fitsumCoef[".lin2", "Estimate"],
f0.se = fitsumCoef[".lin2", "Std. Error"],
f0.p = fitsumCoef[".lin2", "Pr(>|t|)"],
kappa=exp(fitsumCoef["k", "Estimate"]),
phi=fitsumCoef[".lin1", "Estimate"],
AIC=AIC(fit),
AICc=AICc(fit),
RSE=fitsum$sigma,
diagnostics = w)
if (verbose) message(serie, if (w == "") ": HMR fit successful" else ": HMR fit warning")
res
},
error = function(cond) {
if (verbose) message(serie, ": HMR fit failed")
list(
f0 = NA_real_,
f0.se = NA_real_,
f0.p = NA_real_,
kappa=NA_real_,
phi=NA_real_,
AIC=NA_real_,
AICc=NA_real_,
RSE=NA_real_,
diagnostics=cond$message)
}
)
}
utils::globalVariables("x")
|
impute.MinDet = function(dataSet.mvs,q = 0.01){
nSamples = dim(dataSet.mvs)[2]
dataSet.imputed = dataSet.mvs
lowQuantile.samples = apply(dataSet.imputed,2,quantile,prob = q,na.rm = T)
for (i in 1:(nSamples)){
dataSet.imputed[which(is.na(dataSet.mvs[,i])),i] = lowQuantile.samples[i]
}
return(dataSet.imputed)
}
|
data(dataEP05A2_1)
data(dataEP05A2_2)
data(dataEP05A2_3)
cat("\n\n***********************************************************************")
cat("\nVariance Component Analysis (VCA) - test cases defined in runit.misc.R.")
cat("\n***********************************************************************\n\n")
TF001.CovarianceMatrix <- function()
{
data(VCAdata1)
sample1 <- VCAdata1[which(VCAdata1$sample==1),]
sample1$device <- gl(3,28,252)
set.seed(505)
sample1$y <- sample1$y + rep(rep(rnorm(3,,.25), c(28,28,28)),3)
res1 <- anovaVCA(y~(lot+device)/day/run, sample1)
inf1 <- VCAinference(res1, VarVC=TRUE, constrainCI=FALSE)
checkEquals(round(inf1$VCAobj$aov.tab[2, "Var(VC)"], 6), 0.000301)
checkEquals(round(inf1$VCAobj$aov.tab[3, "Var(VC)"], 6), 0.004091)
checkEquals(round(inf1$VCAobj$aov.tab[4, "Var(VC)"], 6), 0.000071)
checkEquals(round(inf1$VCAobj$aov.tab[5, "Var(VC)"], 6), 0.000084)
checkEquals(round(inf1$VCAobj$aov.tab[6, "Var(VC)"], 11), 2.108e-8)
}
TF002.marginal_residuals <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
fit.R <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, order.data=FALSE)
sas.raw <- c(3.384375,0.815625,3.246875,3.678125,-1.115625,-1.684375,-2.753125,-0.821875,0.384375,-1.684375,-1.753125,0.178125,2.884375,3.315625,0.746875,-0.321875,-2.615625,-0.684375,-3.253125,-1.321875,1.884375,1.315625,1.246875,1.178125,-0.615625,-2.184375,-1.253125,-0.821875,1.384375,-2.684375,-1.253125,-1.821875,0.384375,-3.684375,5.246875,-1.321875,4.884375,3.815625,5.246875,4.178125,0.384375,-1.184375,-2.253125,-2.321875,-1.115625,-0.684375,-1.753125,0.678125,-5.615625,0.315625,0.246875,2.178125,-0.115625,1.315625,-0.253125,-1.321875,0.384375,0.315625,0.246875,2.678125,-0.615625,-2.684375,-2.253125,-2.321875,-0.209090909,-2.168181818,-1.627272727,-1.086363636,-0.209090909,-0.668181818,0.8727272727,1.4136363636,-0.709090909,1.8318181818,1.3727272727,1.9136363636,2.2909090909,2.3318181818,1.8727272727,2.4136363636,0.2909090909,0.8318181818,-0.627272727,-0.586363636,-1.209090909,-1.168181818,-2.127272727,-1.586363636,0.2909090909,0.3318181818,-0.127272727,0.9136363636,1.7909090909,0.8318181818,0.3727272727,-0.086363636,-1.209090909,-1.168181818,-1.127272727,-2.586363636,-4.709090909,-3.168181818,-4.127272727,-4.586363636,3.2909090909,2.8318181818,4.8727272727,3.9136363636)
sas.stu <- c(1.5050944191,0.37015662,1.473535357,1.6357304998,-0.496139158,-0.764423058,-1.249455871,-0.365503077,0.1709387013,-0.764423058,-0.795623999,0.0792154957,1.2827351328,1.5047362981,0.3389556788,-0.143143791,-1.163217016,-0.310591187,-1.476371806,-0.587862363,0.8380165602,0.5970725556,0.5658716145,0.5239340683,-0.273779871,-0.991338994,-0.568708064,-0.365503077,0.6156572739,-1.218254929,-0.568708064,-0.810221649,0.1709387013,-1.672086801,2.3811990995,-0.587862363,2.1721722779,1.7316522338,2.3811990995,1.858089786,0.1709387013,-0.537507123,-1.022539935,-1.032580936,-0.496139158,-0.310591187,-0.795623999,0.301574782,-2.497372734,0.1432406844,0.1120397432,0.9686526409,-0.051420585,0.5970725556,-0.114876192,-0.587862363,0.1709387013,0.1432406844,0.1120397432,1.1910119272,-0.273779871,-1.218254929,-1.022539935,-1.032580936,-0.094278468,-0.99540562,-0.747075916,-0.489838127,-0.094278468,-0.306760222,0.400666413,0.6374044247,-0.319726978,0.8409821065,0.6302148788,0.8628529351,1.0329640838,1.0705305723,0.8597633446,1.0883014454,0.1311700424,0.3818851749,-0.287978984,-0.264389617,-0.545175489,-0.536308688,-0.976624382,-0.715286637,0.1311700424,0.1523367091,-0.058430519,0.4119559144,0.8075155734,0.3818851749,0.1711179472,-0.038941106,-0.545175489,-0.536308688,-0.51752745,-1.166183658,-2.123315061,-1.454502551,-1.894818245,-2.067977699,1.4838611045,1.3000790381,2.2370541394,1.7646469765)
sas.pea <- c(1.4619609019,0.3612064332,1.4379060694,1.5888531685,-0.48192063,-0.745939722,-1.219244704,-0.355028363,0.1660398808,-0.745939722,-0.776386242,0.0769453106,1.245974065,1.4683525887,0.3307599139,-0.139041526,-1.12988114,-0.30308126,-1.440673935,-0.5710152,0.8140003914,0.5826356643,0.552189145,0.5089189843,-0.265933793,-0.967368953,-0.55495701,-0.355028363,0.5980135545,-1.188798184,-0.55495701,-0.787002037,0.1660398808,-1.631656647,2.3236229938,-0.5710152,2.1099214124,1.6897818198,2.3236229938,1.8048400054,0.1660398808,-0.524510491,-0.997815473,-1.002988874,-0.48192063,-0.30308126,-0.776386242,0.2929321475,-2.425802161,0.1397772021,0.1093306829,0.940892658,-0.049946956,0.5826356643,-0.112098548,-0.5710152,0.1660398808,0.1397772021,0.1093306829,1.1568794948,-0.265933793,-1.188798184,-0.997815473,-1.002988874,-0.090321768,-0.960197666,-0.720651498,-0.469280491,-0.090321768,-0.295909972,0.3864946579,0.6106536933,-0.306308605,0.811236183,0.607923889,0.8266405301,0.9896124161,1.0326654141,0.8293531201,1.0426273669,0.1256650687,0.3683777208,-0.277793035,-0.253293654,-0.522295442,-0.517339204,-0.942080729,-0.685267328,0.1256650687,0.1469484897,-0.056363804,0.3946668564,0.7736255792,0.3683777208,0.1650654268,-0.037306817,-0.522295442,-0.517339204,-0.499222266,-1.117241001,-2.0342033,-1.403056128,-1.827797653,-1.981188349,1.4215860898,1.2540946452,2.1579285066,1.6905878775)
checkEquals(round(as.numeric(resid(fit.R, "marginal", "raw")), 6), round(sas.raw, 6))
checkEquals(round(as.numeric(resid(fit.R, "marginal", "stud")), 10), sas.stu)
checkEquals(round(as.numeric(resid(fit.R, "marginal", "pear")), 10), sas.pea)
}
TF003.conditional_residuals <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.R <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, order.data=FALSE)
sas.raw <- c(1.0554503692,-1.604344233,0.7358611644,1.0760665621,0.2894545536,-0.274141978,-1.33773851,0.5986649577,0.9931804233,-1.056673527,-1.106527478,0.843618572,0.9136978046,1.6799232283,-0.553851348,-1.287625924,-0.816278005,1.0788977187,-1.525926558,0.3692491659,0.5475924658,0.0389662598,0.0303400538,0.0217138478,0.4776426009,-1.099696512,-0.177035626,0.2456252606,2.0163981647,-1.827317063,-0.171032292,-0.51474752,0.4030450154,-3.770492168,5.0559706477,-1.617566536,0.8392188753,-0.210635075,1.2395109744,0.1896570239,1.1967876368,-0.119442428,-0.935672494,-0.751902559,-0.300680854,0.0120393803,-1.175240385,1.1374798497,-4.01735371,1.2731484286,0.5636505667,1.8541527049,-0.246387468,1.3274418148,-0.098728902,-1.024899619,-0.138123401,-0.394116074,-0.650108746,1.5938985808,0.9363555284,-1.00355777,-0.443471069,-0.383384367,0.8329387278,-1.068683204,-0.470305136,0.1280729319,-0.257137352,-0.892383169,0.4723710135,0.8371251962,-1.380761257,0.9565077625,0.2937767823,0.6310458021,0.3127531847,0.3561609268,-0.100431331,0.4429764109,0.142397156,0.79574555,-0.550906056,-0.397557662,0.0545559443,0.1529340124,-0.74868792,-0.150309852,0.0367239257,0.0389039233,-0.458916079,0.5432639185,0.8950900173,0.1034087373,-0.188272543,-0.479953823,-0.027899545,0.1254488494,0.2787972434,-1.067854363,-1.056621665,0.5005286586,-0.442321018,-0.885170694,0.1479608631,-0.418572047,1.5148950435,0.4483621337)
sas.stu <- c(1.0130072422,-1.40421926,0.6440702679,1.0327943903,0.277814635,-0.239945666,-1.170869781,0.5745906729,0.9532413755,-0.924864682,-0.968499879,0.8096938975,0.876955014,1.4703705752,-0.484764251,-1.235846255,-0.783452784,0.9443166405,-1.33558336,0.3544004438,0.5255719736,0.034105631,0.026555453,0.0208406626,0.4584350227,-0.962521005,-0.154952304,0.2357478619,1.9353121698,-1.599378588,-0.149697822,-0.494047831,0.3868372511,-3.300163153,4.4252917896,-1.552518871,0.8054711271,-0.184360578,1.0848950914,0.1820302919,1.148660874,-0.104543249,-0.818957247,-0.721666086,-0.288589489,0.010537595,-1.028641578,1.0917380478,-3.855802718,1.114336629,0.4933411206,1.7795911329,-0.236479419,1.1618574896,-0.086413516,-0.983685038,-0.132569005,-0.344954262,-0.569014557,1.5298026822,0.8987015962,-0.878374554,-0.388152744,-0.367967222,0.8058885013,-0.936507499,-0.412137372,0.1239136802,-0.248786649,-0.782012412,0.4139477394,0.8099390114,-1.335920138,0.8382060174,0.2574422042,0.6105521795,0.3025963217,0.3121106213,-0.088009893,0.4285904641,0.1377727158,0.6973270207,-0.482769497,-0.384646719,0.0527842046,0.1340189954,-0.656089521,-0.145428441,0.0355312923,0.0340922509,-0.402156923,0.5256210697,0.8660213873,0.0906190511,-0.164986824,-0.464367011,-0.026993489,0.1099332223,0.2443153482,-1.033175098,-1.02230719,0.4386228215,-0.387614354,-0.856424201,0.1431557379,-0.366802677,1.3275314545,0.433801282)
sas.pea <- c(0.805662962,-1.224653252,0.561709108,0.8214000384,0.2209509986,-0.209262363,-1.021143582,0.4569823435,0.7581300884,-0.806596642,-0.844651943,0.6439641857,0.6974581668,1.2823453984,-0.422774515,-0.982890855,-0.623094154,0.8235611613,-1.16479424,0.281861076,0.4179968863,0.0297443378,0.0231596466,0.0165749555,0.3646016562,-0.839437623,-0.135137616,0.1874945339,1.5391887345,-1.394856377,-0.130555057,-0.392925166,0.3076586549,-2.878151335,3.8594029686,-1.234746308,0.6406057401,-0.160785276,0.9461629957,0.1447719799,0.9135507463,-0.091174671,-0.714232232,-0.573954077,-0.229520435,0.009190089,-0.897102959,0.8682789942,-3.066589566,0.9718396658,0.4302546083,1.4153409804,-0.188076354,1.0132837466,-0.075363297,-0.782342484,-0.105434525,-0.300842875,-0.496251225,1.2166797125,0.7147536166,-0.766051488,-0.338517305,-0.29265098,0.6358118792,-0.815764058,-0.359000707,0.097762643,-0.196282124,-0.681187944,0.3605776652,0.6390075601,-1.053984381,0.7301365366,0.2242503101,0.4816997984,0.2387356758,0.2718703556,-0.076662822,0.3381397153,0.1086968349,0.6074209982,-0.420526268,-0.303469962,0.0416445008,0.1167399937,-0.571500228,-0.114736878,0.028032684,0.0296967541,-0.350307033,0.4146927516,0.6832541782,0.0789355824,-0.143715156,-0.366365894,-0.021296719,0.0957595871,0.2128158931,-0.815131373,-0.806557054,0.3820714014,-0.33763943,-0.675682405,0.1129438112,-0.319510993,1.1563734909,0.3422508298)
checkEquals(round(as.numeric(resid(fit.R, "conditional", "raw")), 10), sas.raw)
checkEquals(round(as.numeric(resid(fit.R, "conditional", "stud")), 10), sas.stu)
checkEquals(round(as.numeric(resid(fit.R, "conditional", "pear")), 10), sas.pea)
}
TF004.ddfm.LMM.unbalanced <- function()
{
data(dataEP05A2_2)
dat2ub <- dataEP05A2_2[-c(11,12,23,32,40,41,42),]
fit2ub <- anovaMM(y~day/(run), dat2ub, VarVC.method="scm")
L <- getL(fit2ub, c("day1-day2", "day2-day3", "day3-day6", "day14-day20"), "fixef")
tst.con <- test.fixef(fit2ub, L=L, ddfm="contain")
checkEquals(as.numeric(tst.con[,"DF"]), c(18, 18, 18, 18))
tst.res <- test.fixef(fit2ub, L=L, ddfm="residual")
checkEquals(as.numeric(tst.res[,"DF"]), c(53, 53, 53, 53))
tst.satt <- test.fixef(fit2ub, L=L, ddfm="satt")
checkEquals(as.numeric(tst.satt[,"DF"]), c(17.83, 17.83, 19.6, 17.83), tolerance=1)
}
TF005.ddfm.LMM.unbalanced <- function()
{
data(VCAdata1)
datS2 <- VCAdata1[VCAdata1$sample == 2, ]
datS2ub <- datS2[-c(15, 32, 33, 60, 62, 63, 64, 65, 74),]
fitS2ub <- anovaMM(y~(lot+device)/(day)/(run), datS2ub)
L <- getL(fitS2ub, c("lot1-lot2", "device1-device2"), "fixef")
tst.con <- test.fixef(fitS2ub, L=L, ddfm="contain")
checkEquals(as.numeric(tst.con[,"DF"]), c(58, 58))
tst.res <- test.fixef(fitS2ub, L=L, ddfm="residual")
checkEquals(as.numeric(tst.res[,"DF"]), c(238, 238))
fit <- fitS2ub
fit$VarCov <- matrix(c(0.004773, -0.00009, -5.14E-6, -0.00009, 0.000211, -0.00002, -5.14E-6, -0.00002, 0.000035), 3, 3)
tst.satt <- test.fixef(fit, L=L, ddfm="satt")
checkEquals(as.numeric(round(tst.satt[,"DF"], c(1,1))), c(56.4, 55.9), tolerance=0.1)
}
TF006.stepwiseVCA.fully_nested <- function()
{
data(VCAdata1)
datS7L1 <- VCAdata1[VCAdata1$sample == 7 & VCAdata1$lot == 1, ]
fit0 <- anovaVCA(y~device/day/run, datS7L1)
Ci <- getMat(fit0, "Ci.MS")
tab <- fit0$aov.tab
sw.res <- stepwiseVCA(fit0)
nr <- nrow(fit0$aov.tab)
for(i in 1:length(sw.res))
{
checkEquals(sum(fit0$aov.tab[(nr-i):nr, "VC"]), sw.res[[i]]$aov.tab[1,"VC"])
tmpTotDF <- VCA:::SattDF(tab[(nr-i):nr, "MS"], Ci[(nr-i-1):(nr-1), (nr-i-1):(nr-1)], tab[(nr-i):nr, "DF"], "total")
checkEquals(tmpTotDF, sw.res[[i]]$aov.tab[1,"DF"])
}
}
TF007.getL.simple_contrasts <- function()
{
data(VCAdata1)
dat1 <- VCAdata1[VCAdata1$sample==1,]
fit <- anovaMM(y~(lot+device)/day/(run), dat1)
L <- getL(fit, c("lot1-lot2", "1lot1-1*lot2"))
checkEquals(L[1,], L[2,])
tmp <- rep(0,70)
tmp[2:3] <- c(1,-1)
checkEquals(as.numeric(L[1,]), tmp)
}
TF008.getL.complex_contrasts <- function()
{
data(VCAdata1)
dat1 <- VCAdata1[VCAdata1$sample==1,]
fit <- anovaMM(y~(lot+device)/day/run, dat1)
L <- getL(fit, c("lot1:device1:day1-lot1:device1:day2", "lot2:device1:day2-lot3:device1:day4"))
L0 <- matrix(0, nrow=2, ncol=ncol(L))
colnames(L0) <- colnames(L)
rownames(L0) <- rownames(L)
L0[1,c("lot1:device1:day1", "lot1:device1:day2")] <- c(1, -1)
L0[2,c("lot2:device1:day2", "lot3:device1:day4")] <- c(1, -1)
checkEquals(L, L0)
}
TF009.getL.complex_contrasts <- function()
{
data(VCAdata1)
dat1 <- VCAdata1[VCAdata1$sample==1,]
fit <- anovaMM(y~(lot+device)/day/run, dat1)
L <- getL(fit, c("Custom Linear Hypothesis"="0.25lot1-.75*lot2"))
checkEquals(as.numeric(L[2:3]), c(.25, -.75))
}
TF010.ranef.balanced.nested <- function()
{
old.opt <- options("scipen" = 21)
SASre <- c(0.000201,0.002605,-0.00054,-0.00182,-0.00119,0.000152,0.001162,-0.00122, 0.000293,-0.00087,-0.00009,0.000457,-0.00079,-0.00031,0.001124,0.00062,0.00058,0.000421,-0.00096,0.000188,0.23,1.7866,0.4298,-2.2927,-0.5359,0.3628,0.1836,-1.8719,0.7564,-1.6238,-0.6192,0.7676,-0.3797,-0.7438,-0.09473,0.6637,0.1288,0.5929,-1.4274,0.5381,0.01714,1.4101,-1.0957,0.05366,-0.9255,-0.1757,1.242,0.3798,-0.3971,0.5512,0.5044,-0.2064,-0.5919,0.3678,1.4737,0.09764,0.5825,-0.07603,0.245,-0.3075)
digits <- nchar(gsub(".*\\.", "", as.character(SASre)))
fit <- anovaVCA(y~day/run, dataEP05A2_1)
fit <- solveMME(fit)
checkEquals(as.numeric(round(ranef(fit)[,1], digits)), SASre)
options(old.opt)
}
TF011.ranef.balanced.nested <- function()
{
old.opt <- options("scipen" = 21)
SASre <- c(-1.3614,0.9946,-0.3492,2.2449,-1.0212,-0.2419,-0.09628,-1.1657,1.4828,-0.08096,-0.4642,-0.473,-0.2088,0.4715,1.0583,-0.17,-0.07924,-0.1185,0.4657,-0.8874,-1.536,-0.3423,-0.06817,0.5967,0.7873,-0.3391,-0.3554,0.1041,0.07095,-0.03257,-2.3438,1.1429,-0.4562,0.2562,1.6627,-0.9192,-1.2352,-0.6738,0.7737,-0.4168,-0.5393,1.8585,-0.4641,2.8256,-2.3442,-0.02973,0.2087,-1.8812,2.1895,-0.09085,1.6362,-1.864,0.1378,0.4626,-0.04928,0.6601,1.1144,0.4931,-0.06373,-0.9361)
digits <- nchar(gsub(".*\\.", "", as.character(SASre)))
fit <- anovaVCA(y~day/run, dataEP05A2_2)
fit <- solveMME(fit)
checkEquals(as.numeric(round(ranef(fit)[,1], digits)), SASre)
options(old.opt)
}
TF012.ranef.balanced.nested <- function()
{
old.opt <- options("scipen" = 21)
SASre <- c(0.6753,-2.1525,0.595,4.8068,1.9164,1.6353,-2.7515,-3.2263,0.2208,1.6819,0.005058,4.8909,0.9292,3.971,-0.2123,-5.0957,-3.8365,-1.7749,-1.7071,-0.5711,0.5098,-0.07488,1.0364,3.7443,0.2138,-1.9382,-2.065,-2.4335,-0.3697,0.008577,0.4418,2.8435,-1.7807,2.4792,2.1879,-0.1801,-1.2571,-0.6198,0.1537,-0.6488,-0.1216,-1.1625,-0.6944,-0.981,0.8879,2.8783,0.4832,0.5788,0.4966,0.9583,-0.4389,-0.03192,2.3149,-0.1964,-2.31,-2.7492,-0.9484,-0.4006,-1.1351,0.3206)
digits <- nchar(gsub(".*\\.", "", as.character(SASre)))
fit <- anovaVCA(y~day/run, dataEP05A2_3)
fit <- solveMME(fit)
checkEquals(as.numeric(round(ranef(fit)[,1], digits)), SASre)
options(old.opt)
}
f <- function(x)
{
nam <- unlist(strsplit(as.character(x[1]), "\\*"))
if(x[3] == "_")
nam <- paste(nam, sub(" ", "", x[2]), sep="")
else
nam <- paste(nam, gsub(" ", "", x[2:3]), sep="")
nam <- paste(nam, collapse=":")
return(nam)
}
TF013.ranef.unbalanced.nested <- function()
{
old.opt <- options("scipen" = 21)
SASre <- c(-0.04937,-0.6243,0.1375,0.3915,0.2943,0.03382,-0.275,0.3004,-0.06473,0.2177,0.02878,-0.1045,0.07342,0.08029,-0.2658,-0.144,-0.1341,-0.09579,0.2393,-0.03934,0.2539,2.2287,0.3491,-2.3509,-0.7547,0.3425,-2.1535,0.813,-1.8442,-0.6803,0.8489,0.2013,-0.8418,0.04628,0.7644,0.1997,0.6611,-1.6523,0.5696,0.06753,1.8355,-1.244,-0.1982,-1.1615,-0.2202,1.4478,0.198,-0.3916,0.4271,0.493,-0.1683,-0.6793,0.3191,1.6842,0.1732,0.6735,-0.03746,0.09424,-0.3134)
digits <- nchar(gsub(".*\\.", "", as.character(SASre)))
fit <- anovaVCA(y~day/run, dataEP05A2_1[-c(3,13,21,22,50), ], NegVC=TRUE)
fit <- solveMME(fit)
checkEquals(as.numeric(round(ranef(fit)[,1], digits)), SASre)
options(old.opt)
}
TF014.ranef.unbalanced.nested <- function()
{
old.opt <- options("scipen" = 21)
SASre <- c(-1.1456,0.6634,0.463,1.9848,-0.8504,-0.1739,-0.04745,-0.9272,0.9797,-0.03415,-0.3668,-0.3744,-0.2066,0.329,0.9548,-0.1114,-0.4822,-0.06678,0.1464,-0.7342,-1.6664,-0.08874,1.0566,0.8392,0.7674,-0.3348,-0.3397,0.01771,0.4464,-0.00412,-2.4289,1.1808,-0.4149,0.03393,1.8452,-0.9296,-0.9685,-0.6712,0.1018,-0.4684,-0.6344,1.4211,-0.1266,3.1471,-2.4752,-0.01445,0.2444,-1.8798,1.5212,-0.06447,1.6922,-1.9328,0.6268,0.07245,0.7058,0.5371,0.1922,-1.0062)
digits <- nchar(gsub(".*\\.", "", as.character(SASre)))
fit <- anovaVCA(y~day/run, dataEP05A2_2[-c(8, 9, 12, 32, 35, 51, 52, 53, 67, 68, 73), ], NegVC=TRUE)
fit <- solveMME(fit)
checkEquals(as.numeric(round(ranef(fit)[,1], digits)), SASre)
options(old.opt)
}
TF015.ranef.unbalanced.nested <- function()
{
old.opt <- options("scipen" = 21)
SASre <- c(0.6958,-2.1514,0.3809,4.7516,1.8846,1.4953,-2.7456,-3.3254,0.2027,1.652,0.1019,4.8351,0.9999,3.9225,-0.2269,-5.2604,-3.8218,-1.0984,-1.7096,-0.5828,0.5248,-0.08342,1.068,3.5725,0.2055,-1.9519,-1.9777,-2.2817,-0.3553,0.009593,0.5286,2.7164,-1.7359,2.3667,2.0749,-0.3834,-1.2134,-0.899,0.1355,-0.6235,-0.1364,-1.1175,-0.8554,-0.9202,0.8465,2.7865,0.4451,0.4255,0.4684,0.9125,-0.4717,-0.01747,2.2941,-0.1771,-2.2015,-2.553,-0.9199,0.2859,-1.0898,0.2982)
digits <- nchar(gsub(".*\\.", "", as.character(SASre)))
fit <- anovaVCA(y~day/run, dataEP05A2_3[-c(1, 11, 21, 31, 41, 51, 61, 71 ), ], NegVC=TRUE)
fit <- solveMME(fit)
checkEquals(as.numeric(round(ranef(fit)[,1], digits)), SASre)
options(old.opt)
}
TF016.as.matrix.anovaVCA <- function()
{
data(dataEP05A2_2)
fit <- anovaVCA(y~day/run, dataEP05A2_2)
checkEquals(c(as.matrix(fit)), c(fit$aov.tab))
}
TF017.as.matrix.anovaMM <- function()
{
data(dataEP05A2_2)
fit <- anovaMM(y~day/(run), dataEP05A2_2)
checkEquals(c(as.matrix(fit)), c(fit$aov.tab))
}
NWformula <- function(input=NULL)
{
if( is.null(input) )
stop("'input' is NULL, cannot compute Satterthwaite approximation of total degrees of freedom!")
out <- input
out$DF <- NA
out$M <- NA
out$a <- NA
term1 <- 1
for(i in 1:nrow(out))
{
out$DF[i] <- out$N[i]-1
for(j in (i+1):nrow(out))
{
if(j > nrow(out))
break
else
out$DF[i] <- out$DF[i] * out$N[j]
}
if(i == 1)
out$M[i] <- out$VC[i]
else
{
n <- 1
for(j in 1:(i-1))
n <- n * out$N[j]
out$M[i] <- out$VC[i]*n + out$M[i-1]
}
if(i < nrow(out))
{
out$a[i] <- term1 * (1-1/out$N[i])
term1 <- term1*(1/out$N[i])
}
else
out$a[i] <- term1
}
DFtotal <- eval(parse(text=paste(paste(out$N, collapse="*"), "-1", sep="")))
numer <- 0
denom <- 0
for(i in 1:nrow(out))
{
numer <- numer + out$a[i] * out$M[i]
denom <- denom + (out$a[i] * out$M[i])^2 / out$DF[i]
}
DFsatt <- numer^2 / denom
return(DFsatt)
}
data(VCAdata1)
datS1 <- VCAdata1[VCAdata1$sample==1,]
TF018.SattDF_total_vs_Neter_Wasserman <- function()
{
fit <- anovaVCA(y~day/run, datS1)
input <- data.frame(VC=rev(fit$aov.tab[-1,"VC"]), N=c(6,2,21))
NWdf <- NWformula(input)
checkEquals(NWdf, fit$aov.tab[1,"DF"])
}
TF019.SattDF_total_vs_Neter_Wasserman <- function()
{
fit <- anovaVCA(y~device/day/run, datS1)
input <- data.frame(VC=rev(fit$aov.tab[-1,"VC"]), N=c(6,2,7,3))
NWdf <- NWformula(input)
checkEquals(NWdf, fit$aov.tab[1,"DF"])
}
TF020.SattDF_total_vs_Neter_Wasserman <- function()
{
fit <- anovaVCA(y~lot/device/day/run, datS1)
input <- data.frame(VC=rev(fit$aov.tab[-1,"VC"]), N=c(2,2,7,3,3))
NWdf <- NWformula(input)
checkEquals(NWdf, fit$aov.tab[1,"DF"], tol=1e-7)
}
TF021.lsmeans.balanced <- function()
{
data(VCAdata1)
datS1 <- VCAdata1[VCAdata1$sample == 1, ]
fit <- anovaMM(y~(lot+device)/(day)/(run), datS1)
lsm <- lsmeans(fit, type="c", ddfm="cont")
checkEquals(round(as.numeric(lsm[,"Estimate"]), 4), c(2.8138, 2.5507, 2.6719, 2.7887, 2.7136, 2.5341))
checkEquals(round(as.numeric(lsm[,"SE"]), 5), rep(0.04266, 6))
checkEquals(round(as.numeric(lsm[,"DF"]), 5), rep(58, 6))
checkEquals(round(as.numeric(lsm[,"t Value"]), 2), c(65.96, 59.79, 62.64, 65.37, 63.61, 59.41))
}
TF022.lsmeans.unbalanced <- function()
{
data(VCAdata1)
datS1 <- VCAdata1[VCAdata1$sample == 1, ]
datS1ub <- datS1[-c(1,11,12,20,55,56,57,103,121,122,179),]
fit <- anovaMM(y~(lot+device)/(day)/(run), datS1ub)
lsm <- lsmeans(fit)
checkEquals(round(as.numeric(lsm[,"Estimate"]), 4), c(2.8062, 2.5549, 2.6718, 2.7778, 2.7207, 2.5343))
checkEquals(round(as.numeric(lsm[,"SE"]), 5), c(0.04106, 0.04062, 0.04019, 0.04063, 0.04105, 0.04019))
}
TF023.ddfm_fixef.all_methods.Orthodont.balanced <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
fit.Ortho <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2-1, Ortho)
L <- getL(fit.Ortho, "SexMale-SexFemale", "fixef")
tst.con <- test.fixef(fit.Ortho, L=L, ddfm="contain")
checkEquals(as.numeric(tst.con[,"DF"]), 54)
tst.res <- test.fixef(fit.Ortho, L=L, ddfm="residual")
checkEquals(as.numeric(tst.res[,"DF"]), 104)
fit <- fit.Ortho
fit$VarCov <- matrix(c(1.1494, 0.001364, -0.02727, 0.001364, 0.001393, -0.00545, -0.02727, -0.00545, 0.1091), 3, 3)
tst.satt <- test.fixef(fit, L=L, ddfm="satt")
checkEquals(as.numeric(tst.satt[,"DF"]), 25, tolerance=0.1)
}
TF024.ddfm_lsmeans.all_methods.Orthodont.unbalanced <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho.ub <- Ortho[-c(5,7,23,33,51,72,73,74,90),]
fit.Ortho <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2-1, Ortho.ub)
L <- getL(fit.Ortho, "SexMale-SexFemale", "lsmeans")
tst.con <- test.lsmeans(fit.Ortho, L=L, ddfm="contain")
checkEquals(as.numeric(tst.con[,"DF"]), 45)
tst.res <- test.lsmeans(fit.Ortho, L=L, ddfm="residual")
checkEquals(as.numeric(tst.res[,"DF"]), 95)
fit <- fit.Ortho
fit$VarCov <- matrix(c(1.4381, 0.002238, -0.05008, 0.002238, 0.001532, -0.00800, -0.05008, -0.00800, 0.1572), 3, 3)
tst.satt <- test.lsmeans(fit, L=L, ddfm="satt")
checkEquals(round(as.numeric(tst.satt[,"DF"]),2), 23.35, tolerance=0.1)
}
TF025.lsmeans.including_covariate <- function()
{
data(VCAdata1)
datS1 <- VCAdata1[VCAdata1$sample == 1, ]
set.seed(20140608)
datS1$cov <- 20 + rnorm(252)
fit <- anovaMM(y~device+day:cov+device:(run), datS1)
lsm <- lsmeans(fit, "device")
checkEquals( round(as.numeric(lsm[,"Estimate"]), 4), c(2.8773, 1.8635, 3.2976) )
}
TF026.lsmeans.complex_model.including_covariate <- function()
{
data(VCAdata1)
datS1 <- VCAdata1[VCAdata1$sample == 1, ]
set.seed(20140608)
datS1$cov <- 20 + rnorm(252)
fit <- anovaMM(y~lot+device+day:cov+lot:device:day+lot:device:day:(run), datS1)
lsm <- lsmeans(fit, c("lot", "device"))
checkEquals( round(as.numeric(lsm[,"Estimate"]), 4), c(2.8143, 2.5496, 2.6720, 2.7762, 2.7380, 2.5217) )
}
TF027.anovaVCA.by_processing <- function()
{
data(CA19_9)
fit.lst <- anovaVCA(result~site/day, CA19_9, by="sample")
samples <- gsub("sample\\.", "",names(fit.lst))
for(i in 1:length(fit.lst))
{
tmp.fit <- anovaVCA(result~site/day, CA19_9[CA19_9$sample == samples[i],])
checkEquals(fit.lst[[i]]$aov.tab, tmp.fit$aov.tab)
}
}
TF028.anovaMM.by_processing <- function()
{
data(CA19_9)
fit.lst <- anovaMM(result~site/(day), CA19_9, by="sample")
samples <- gsub("sample\\.", "",names(fit.lst))
for(i in 1:length(fit.lst))
{
tmp.fit <- anovaMM(result~site/(day), CA19_9[CA19_9$sample == samples[i],])
print(checkEquals(fit.lst[[i]]$aov.tab, tmp.fit$aov.tab))
}
}
TF029.anovaVCA.by_processing <- function()
{
data(CA19_9)
fit.lst <- anovaVCA(result~site/day, CA19_9, by="sample")
samples <- gsub("sample\\.", "",names(fit.lst))
inf.lst <- VCAinference(fit.lst)
for(i in 1:length(fit.lst))
{
tmp.fit <- anovaVCA(result~site/day, CA19_9[CA19_9$sample == samples[i],])
tmp.inf <- VCAinference(tmp.fit)
checkEquals(inf.lst[[i]]$VCAobj$aov.tab, tmp.inf$VCAobj$aov.tab)
checkEquals(inf.lst[[i]]$ConfInt, tmp.inf$ConfInt)
}
}
TF030.anovaVCA.by_processing <- function()
{
data(CA19_9)
fit.lst <- anovaVCA(result~site/day, CA19_9, by="sample")
samples <- gsub("sample\\.", "",names(fit.lst))
total.specs <- c(1, 3, 5, 30, 80, 200)
error.specs <- c(.5, 2, 2, 5, 50, 75)
inf.lst <- VCAinference(fit.lst, total.claim=total.specs, error.claim=error.specs)
for(i in 1:length(fit.lst))
{
tmp.fit <- anovaVCA(result~site/day, CA19_9[CA19_9$sample == samples[i],])
tmp.inf <- VCAinference(tmp.fit, total.claim=total.specs[i], error.claim=error.specs[i])
checkEquals(inf.lst[[i]]$VCAobj$aov.tab, tmp.inf$VCAobj$aov.tab)
}
}
TF031.test.lsmeans <- function()
{
data(dataEP05A2_1)
fit <- anovaMM(y~day/(run), dataEP05A2_1)
lc.mat <- getL(fit, "day19-day20", "lsm")
res <- test.lsmeans(fit, lc.mat)
checkEquals(as.numeric(round(res, 6)), c(-1.220903, 20, 1.523546, -0.801356, 0.432343))
}
TF032.ANOVA_vs_REML.residuals.raw <- function()
{
data(dataEP05A2_1)
fit1.aov <- anovaVCA(y~day/run, dataEP05A2_1)
fit1.reml <- remlVCA(y~day/run, dataEP05A2_1)
checkEquals(round(resid(fit1.aov), 4), round(resid(fit1.reml), 4))
data(dataEP05A2_2)
fit2.aov <- anovaVCA(y~day/run, dataEP05A2_2)
fit2.reml <- remlVCA(y~day/run, dataEP05A2_2)
checkEquals(round(resid(fit2.aov), 6), round(resid(fit2.reml), 6))
data(dataEP05A2_3)
fit3.aov <- anovaVCA(y~day/run, dataEP05A2_3)
fit3.reml <- remlVCA(y~day/run, dataEP05A2_3)
checkEquals(round(resid(fit3.aov), 4), round(resid(fit3.reml), 4))
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho)
fit.remlMM <- remlMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, cov=FALSE)
checkEquals(round(resid(fit.anovaMM), 4), round(resid(fit.remlMM), 4))
}
TF033.ANOVA_vs_REML.residuals.studentized <- function()
{
data(dataEP05A2_1)
fit1.aov <- anovaVCA(y~day/run, dataEP05A2_1)
fit1.reml <- remlVCA(y~day/run, dataEP05A2_1)
checkEquals(round(resid(fit1.aov, mode="student"), 4), round(resid(fit1.reml, mode="student"), 4))
data(dataEP05A2_2)
fit2.aov <- anovaVCA(y~day/run, dataEP05A2_2)
fit2.reml <- remlVCA(y~day/run, dataEP05A2_2)
checkEquals(round(resid(fit2.aov, mode="student"), 5), round(resid(fit2.reml, mode="student"), 5))
data(dataEP05A2_3)
fit3.aov <- anovaVCA(y~day/run, dataEP05A2_3)
fit3.reml <- remlVCA(y~day/run, dataEP05A2_3)
checkEquals(round(resid(fit3.aov, mode="student"), 4), round(resid(fit3.reml, mode="student"), 4))
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho)
fit.remlMM <- remlMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, cov=FALSE)
checkEquals(round(resid(fit.anovaMM, mode="student"), 4), round(resid(fit.remlMM, mode="student"), 4))
}
TF034.ANOVA_vs_REML.residuals.pearson <- function()
{
data(dataEP05A2_1)
fit1.aov <- anovaVCA(y~day/run, dataEP05A2_1)
fit1.reml <- remlVCA(y~day/run, dataEP05A2_1)
checkEquals(round(resid(fit1.aov, mode="pearson"), 4), round(resid(fit1.reml, mode="pearson"), 4))
data(dataEP05A2_2)
fit2.aov <- anovaVCA(y~day/run, dataEP05A2_2)
fit2.reml <- remlVCA(y~day/run, dataEP05A2_2)
checkEquals(round(resid(fit2.aov, mode="pearson"), 6), round(resid(fit2.reml, mode="pearson"), 6))
data(dataEP05A2_3)
fit3.aov <- anovaVCA(y~day/run, dataEP05A2_3)
fit3.reml <- remlVCA(y~day/run, dataEP05A2_3)
checkEquals(round(resid(fit3.aov, mode="pearson"), 4), round(resid(fit3.reml, mode="pearson"), 4))
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho)
fit.remlMM <- remlMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, cov=FALSE)
checkEquals(round(resid(fit.anovaMM, mode="pearson"), 4), round(resid(fit.remlMM, mode="pearson"), 4))
}
TF035.ANOVA_vs_REML.residuals.standardized <- function()
{
data(dataEP05A2_1)
fit1.aov <- anovaVCA(y~day/run, dataEP05A2_1)
fit1.reml <- remlVCA(y~day/run, dataEP05A2_1)
checkEquals(round(resid(fit1.aov, mode="standard"), 4), round(resid(fit1.reml, mode="standard"), 4))
data(dataEP05A2_2)
fit2.aov <- anovaVCA(y~day/run, dataEP05A2_2)
fit2.reml <- remlVCA(y~day/run, dataEP05A2_2)
checkEquals(round(resid(fit2.aov, mode="standard"), 6), round(resid(fit2.reml, mode="standard"), 6))
data(dataEP05A2_3)
fit3.aov <- anovaVCA(y~day/run, dataEP05A2_3)
fit3.reml <- remlVCA(y~day/run, dataEP05A2_3)
checkEquals(round(resid(fit3.aov, mode="standard"), 4), round(resid(fit3.reml, mode="standard"), 4))
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho)
fit.remlMM <- remlMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, cov=FALSE)
checkEquals(round(resid(fit.anovaMM, mode="standard"), 4), round(resid(fit.remlMM, mode="standard"), 4))
}
TF036.ANOVA_vs_REML.ranef.raw <- function()
{
data(dataEP05A2_1)
fit1.aov <- anovaVCA(y~day/run, dataEP05A2_1)
fit1.reml <- remlVCA(y~day/run, dataEP05A2_1)
re.aov <- ranef(fit1.aov, mode="raw")
re.reml <- ranef(fit1.reml, mode="raw")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(dataEP05A2_2)
fit2.aov <- anovaVCA(y~day/run, dataEP05A2_2)
fit2.reml <- remlVCA(y~day/run, dataEP05A2_2)
re.aov <- ranef(fit2.aov, mode="raw")
re.reml <- ranef(fit2.reml, mode="raw")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(dataEP05A2_3)
fit3.aov <- anovaVCA(y~day/run, dataEP05A2_3)
fit3.reml <- remlVCA(y~day/run, dataEP05A2_3)
re.aov <- ranef(fit3.aov, mode="raw")
re.reml <- ranef(fit3.reml, mode="raw")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho)
fit.remlMM <- remlMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, cov=FALSE)
re.aov <- ranef(fit.anovaMM, mode="raw")
re.reml <- ranef(fit.remlMM, mode="raw")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
}
TF037.ANOVA_vs_REML.ranef.studentized <- function()
{
data(dataEP05A2_1)
fit1.aov <- anovaVCA(y~day/run, dataEP05A2_1)
fit1.reml <- remlVCA(y~day/run, dataEP05A2_1)
re.aov <- ranef(fit1.aov, mode="student")
re.reml <- ranef(fit1.reml, mode="student")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(dataEP05A2_2)
fit2.aov <- anovaVCA(y~day/run, dataEP05A2_2)
fit2.reml <- remlVCA(y~day/run, dataEP05A2_2)
re.aov <- ranef(fit2.aov, mode="student")
re.reml <- ranef(fit2.reml, mode="student")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(dataEP05A2_3)
fit3.aov <- anovaVCA(y~day/run, dataEP05A2_3)
fit3.reml <- remlVCA(y~day/run, dataEP05A2_3)
re.aov <- ranef(fit3.aov, mode="student")
re.reml <- ranef(fit3.reml, mode="student")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho)
fit.remlMM <- remlMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, cov=FALSE)
re.aov <- ranef(fit.anovaMM, mode="student")
re.reml <- ranef(fit.remlMM, mode="student")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
}
TF038.ANOVA_vs_REML.ranef.standardized <- function()
{
data(dataEP05A2_1)
fit1.aov <- anovaVCA(y~day/run, dataEP05A2_1)
fit1.reml <- remlVCA(y~day/run, dataEP05A2_1)
re.aov <- ranef(fit1.aov, mode="standard")
re.reml <- ranef(fit1.reml, mode="standard")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(dataEP05A2_2)
fit2.aov <- anovaVCA(y~day/run, dataEP05A2_2)
fit2.reml <- remlVCA(y~day/run, dataEP05A2_2)
re.aov <- ranef(fit2.aov, mode="standard")
re.reml <- ranef(fit2.reml, mode="standard")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(dataEP05A2_3)
fit3.aov <- anovaVCA(y~day/run, dataEP05A2_3)
fit3.reml <- remlVCA(y~day/run, dataEP05A2_3)
re.aov <- ranef(fit3.aov, mode="standard")
re.reml <- ranef(fit3.reml, mode="standard")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age - 11
Ortho$Subject <- factor(as.character(Ortho$Subject))
fit.anovaMM <- anovaMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho)
fit.remlMM <- remlMM(distance~Sex+Sex:age2+(Subject)+(Subject):age2, Ortho, cov=FALSE)
re.aov <- ranef(fit.anovaMM, mode="standard")
re.reml <- ranef(fit.remlMM, mode="standard")
re.reml <- re.reml[rownames(re.aov),,drop=FALSE]
checkEquals(round(re.aov[,1], 4), round(re.reml[,1], 4))
}
TF039.remlVCA.by_processing <- function()
{
data(CA19_9)
fit.lst <- remlVCA(result~site/day, CA19_9, by="sample")
samples <- gsub("sample\\.", "",names(fit.lst))
total.specs <- c(1, 3, 5, 30, 80, 200)
error.specs <- c(.5, 2, 2, 5, 50, 75)
inf.lst <- VCAinference(fit.lst, total.claim=total.specs, error.claim=error.specs)
for(i in 1:length(fit.lst))
{
tmp.fit <- remlVCA(result~site/day, CA19_9[CA19_9$sample == samples[i],])
tmp.inf <- VCAinference(tmp.fit, total.claim=total.specs[i], error.claim=error.specs[i])
checkEquals(inf.lst[[i]]$VCAobj$aov.tab, tmp.inf$VCAobj$aov.tab)
checkEquals(inf.lst[[i]]$ConfInt, tmp.inf$ConfInt)
}
}
TF040.remlMM.by_processing <- function()
{
data(CA19_9)
fit.lst <- remlMM(result~(site)/day, CA19_9, by="sample")
samples <- gsub("sample\\.", "",names(fit.lst))
total.specs <- c(1, 3, 5, 30, 80, 200)
error.specs <- c(.5, 2, 2, 5, 50, 75)
inf.lst <- VCAinference(fit.lst, total.claim=total.specs, error.claim=error.specs)
for(i in 1:length(fit.lst))
{
tmp.fit <- remlMM(result~(site)/day, CA19_9[CA19_9$sample == samples[i],])
tmp.inf <- VCAinference(tmp.fit, total.claim=total.specs[i], error.claim=error.specs[i])
print(checkEquals(inf.lst[[i]]$VCAobj$aov.tab, tmp.inf$VCAobj$aov.tab))
checkEquals(inf.lst[[i]]$ConfInt, tmp.inf$ConfInt)
}
}
TF041.REML.test.lsmeans <- function()
{
data(dataEP05A2_1)
fit <- remlMM(y~day/(run), dataEP05A2_1)
lc.mat <- getL(fit, "day19-day20", "lsm")
res <- test.lsmeans(fit, lc.mat)
checkEquals(round(as.numeric(res),7), c( -1.2209032, 20, 1.5235456, -0.8013565, 0.4323434))
}
TF042.balancedness.ordered_vs_unordered <- function()
{
data(dataEP05A2_1)
dat <- dataEP05A2_1
dat <- dat[sample(1:nrow(dat)),]
fit1 <- anovaVCA(y~day/run, dat)
fit2 <- remlVCA(y~day/run, dat)
fit3 <- anovaMM(y~day/(run), dat)
fit4 <- remlMM(y~day/(run), dat)
checkEquals(fit1$balanced, "balanced")
checkEquals(fit2$balanced, "balanced")
checkEquals(fit3$balanced, "balanced")
checkEquals(fit4$balanced, "balanced")
}
load(file=file.path(R.home(), "library/VCA/UnitTests/LSMeans_Data.RData"))
fit.vca <- remlMM(y~snp+time+snp:time+sex+(id)+(id):time, dat,VarVC=F)
TF043.LSMeans.atCovarLevel <- function()
{
lsm <- lsmeans(fit.vca, var="snp", at=list(time=1:4))
checkEquals(as.numeric(round(lsm[-c(1,2),"Estimate"],2)), c(4.89, 5.01, 5.80, 5.92, 6.71, 6.83, 7.62, 7.75))
}
TF044.LSMeans.atCovarLevel <- function()
{
lsm <- lsmeans(fit.vca, var="snp", at=list(tim=1:4))
checkEquals(nrow(lsm), 2)
}
TF045.LSMeans.atCovarLevel <- function()
{
lsm1 <- lsmeans(fit.vca, var="snp", at=list(sex=c(Male=.3, Female=.6)))
checkEquals(nrow(lsm1), 2)
lsm2 <- lsmeans(fit.vca, var="snp", at=list(sex=c(Male=.5, Female=.6)))
checkEquals(nrow(lsm2), 2)
}
TF046.model.terms <- function()
{
data(dataEP05A2_1)
checkException(anovaMM(y~(a)+.b:y, dataEP05A2_1))
checkException(remlMM(y~(a)+.b:y, dataEP05A2_1))
fit0 <- anovaVCA(y~day/run, dataEP05A2_1)
dat <- dataEP05A2_1
dat$day.var <- dat$day
checkEquals(as.numeric(anovaVCA(y~day.var/run, dat)$aov.tab[,"VC"]), as.numeric(fit0$aov.tab[,"VC"]), tolerance=1e-8)
checkEquals(as.numeric(anovaMM(y~(day.var)/(run), dat)$aov.tab[,"VC"]), as.numeric(fit0$aov.tab[,"VC"]), tolerance=1e-8)
checkEquals(as.numeric(remlMM(y~(day.var)/(run), dat)$aov.tab[,"VC"]), as.numeric(fit0$aov.tab[,"VC"]), tolerance=1e-7)
checkEquals(as.numeric(remlVCA(y~day.var/run, dat)$aov.tab[,"VC"]), as.numeric(fit0$aov.tab[,"VC"]), tolerance=1e-7)
}
TF047.as.matrix.VCA.REML <- function()
{
data(dataEP05A2_1)
fit1 <- remlVCA(y~day/run, dataEP05A2_1)
mat1 <- as.matrix(fit1)
checkEquals( fit1$aov.tab[,"DF"], as.numeric(mat1[,"DF"]))
checkEquals( fit1$aov.tab[,"VC"], as.numeric(mat1[,"VC"]))
checkEquals( fit1$aov.tab[,"%Total"], as.numeric(mat1[,"%Total"]))
checkEquals( fit1$aov.tab[,"SD"], as.numeric(mat1[,"SD"]))
checkEquals( fit1$aov.tab[,"CV[%]"], as.numeric(mat1[,"CV[%]"]))
checkEquals( fit1$aov.tab[,"Var(VC)"], as.numeric(mat1[,"Var(VC)"]))
fit2 <- remlMM(y~day/(run), dataEP05A2_1)
mat2 <- as.matrix(fit2)
checkEquals( fit2$aov.tab[,"DF"], as.numeric(mat2[,"DF"]))
checkEquals( fit2$aov.tab[,"VC"], as.numeric(mat2[,"VC"]))
checkEquals( fit2$aov.tab[,"%Total"], as.numeric(mat2[,"%Total"]))
checkEquals( fit2$aov.tab[,"SD"], as.numeric(mat2[,"SD"]))
checkEquals( fit2$aov.tab[,"CV[%]"], as.numeric(mat2[,"CV[%]"]))
checkEquals( fit2$aov.tab[,"Var(VC)"], as.numeric(mat2[,"Var(VC)"]))
}
TF048.as.matrix.VCAinference.REML <- function()
{
data(dataEP05A2_2)
fit.reml <- remlVCA(y~day/run, dataEP05A2_2)
inf.reml <- VCAinference(fit.reml)
VC.mat <- as.matrix(inf.reml, what="VC", digits=12)
SD.mat <- as.matrix(inf.reml, what="SD", digits=12)
CV.mat <- as.matrix(inf.reml, what="CV", digits=12)
checkEquals(as.numeric(fit.reml$aov.tab[,"VC"]), as.numeric(VC.mat[,"Estimate"]))
checkEquals(as.numeric(inf.reml$ConfInt$VC$OneSided[,"LCL"]), as.numeric(VC.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$VC$OneSided[,"UCL"]), as.numeric(VC.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$VC$TwoSided[,"LCL"]), as.numeric(VC.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$VC$TwoSided[,"UCL"]), as.numeric(VC.mat[,"CI UCL"]))
checkEquals(as.numeric(fit.reml$aov.tab[,"SD"]), as.numeric(SD.mat[,"Estimate"]))
checkEquals(as.numeric(inf.reml$ConfInt$SD$OneSided[,"LCL"]), as.numeric(SD.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$SD$OneSided[,"UCL"]), as.numeric(SD.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$SD$TwoSided[,"LCL"]), as.numeric(SD.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$SD$TwoSided[,"UCL"]), as.numeric(SD.mat[,"CI UCL"]))
checkEquals(as.numeric(fit.reml$aov.tab[,"CV[%]"]), as.numeric(CV.mat[,"Estimate"]))
checkEquals(as.numeric(inf.reml$ConfInt$CV$OneSided[,"LCL"]), as.numeric(CV.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$CV$OneSided[,"UCL"]), as.numeric(CV.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$CV$TwoSided[,"LCL"]), as.numeric(CV.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.reml$ConfInt$CV$TwoSided[,"UCL"]), as.numeric(CV.mat[,"CI UCL"]))
mat.list <- as.matrix(inf.reml, digits=12)
checkEquals(VC.mat, mat.list[[1]])
checkEquals(SD.mat, mat.list[[2]])
checkEquals(CV.mat, mat.list[[3]])
}
TF049.as.matrix.VCAinference.ANOVA <- function()
{
data(dataEP05A2_3)
fit.anova <- anovaVCA(y~day/run, dataEP05A2_3)
inf.anova <- VCAinference(fit.anova, VarVC=FALSE)
VC.mat <- as.matrix(inf.anova, what="VC", digits=12)
SD.mat <- as.matrix(inf.anova, what="SD", digits=12)
CV.mat <- as.matrix(inf.anova, what="CV", digits=12)
checkEquals(as.numeric(fit.anova$aov.tab[,"VC"]), as.numeric(VC.mat[,"Estimate"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$OneSided[,"LCL"]), as.numeric(VC.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$OneSided[,"UCL"]), as.numeric(VC.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$TwoSided[,"LCL"]), as.numeric(VC.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$TwoSided[,"LCL"]), as.numeric(VC.mat[,"CI LCL"]))
checkEquals(as.numeric(fit.anova$aov.tab[,"SD"]), as.numeric(SD.mat[,"Estimate"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$OneSided[,"LCL"]), as.numeric(SD.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$OneSided[,"UCL"]), as.numeric(SD.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$TwoSided[,"LCL"]), as.numeric(SD.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$TwoSided[,"LCL"]), as.numeric(SD.mat[,"CI LCL"]))
checkEquals(as.numeric(fit.anova$aov.tab[,"CV[%]"]), as.numeric(CV.mat[,"Estimate"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$OneSided[,"LCL"]), as.numeric(CV.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$OneSided[,"UCL"]), as.numeric(CV.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$TwoSided[,"LCL"]), as.numeric(CV.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$TwoSided[,"LCL"]), as.numeric(CV.mat[,"CI LCL"]))
inf.anova <- VCAinference(fit.anova, VarVC=TRUE)
VC.mat <- as.matrix(inf.anova, what="VC", digits=12)
SD.mat <- as.matrix(inf.anova, what="SD", digits=12)
CV.mat <- as.matrix(inf.anova, what="CV", digits=12)
checkEquals(as.numeric(fit.anova$aov.tab[,"VC"]), as.numeric(VC.mat[,"Estimate"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$OneSided[,"LCL"]), as.numeric(VC.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$OneSided[,"UCL"]), as.numeric(VC.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$TwoSided[,"LCL"]), as.numeric(VC.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$VC$TwoSided[,"LCL"]), as.numeric(VC.mat[,"CI LCL"]))
checkEquals(as.numeric(fit.anova$aov.tab[,"SD"]), as.numeric(SD.mat[,"Estimate"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$OneSided[,"LCL"]), as.numeric(SD.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$OneSided[,"UCL"]), as.numeric(SD.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$TwoSided[,"LCL"]), as.numeric(SD.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$SD$TwoSided[,"LCL"]), as.numeric(SD.mat[,"CI LCL"]))
checkEquals(as.numeric(fit.anova$aov.tab[,"CV[%]"]), as.numeric(CV.mat[,"Estimate"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$OneSided[,"LCL"]), as.numeric(CV.mat[,"One-Sided LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$OneSided[,"UCL"]), as.numeric(CV.mat[,"One-Sided UCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$TwoSided[,"LCL"]), as.numeric(CV.mat[,"CI LCL"]))
checkEquals(as.numeric(inf.anova$ConfInt$CV$TwoSided[,"LCL"]), as.numeric(CV.mat[,"CI LCL"]))
}
TF050.lmerSummary <- function()
{
data(VCAdata1)
fit0 <- remlVCA(y~(device+lot)/day/run, subset(VCAdata1, sample==5))
fit1 <- lme4:::lmer(y~(1|device)+(1|lot)+(1|device:lot:day)+(1|device:lot:day:run),
subset(VCAdata1, sample==5), control=lme4:::lmerControl(optimizer="bobyqa"))
sum1 <- lmerSummary(fit1, tab.only=TRUE)
sum1 <- sum1[rownames(fit0$aov.tab),]
checkEquals(as.numeric(fit0$aov.tab[,"VC"]), as.numeric(sum1[,"VC"]), tolerance=1e-5)
checkEquals(as.numeric(fit0$aov.tab[,"%Total"]), as.numeric(sum1[,"%Total"]), tolerance=1e-5)
checkEquals(as.numeric(fit0$aov.tab[,"SD"]), as.numeric(sum1[,"SD"]), tolerance=1e-6)
checkEquals(as.numeric(fit0$aov.tab[,"CV[%]"]), as.numeric(sum1[,"CV[%]"]), tolerance=1e-6)
}
TF051.Scale.reScale.anovaVCA <- function()
{
data(VCAdata1)
scaled.fits <- Scale("anovaVCA", y~(device+lot)/day/run, VCAdata1, by="sample")
reference.fits <- anovaVCA(y~(device+lot)/day/run, VCAdata1, by="sample")
scaled.infs <- VCAinference(scaled.fits, VarVC=TRUE)
reference.infs <- VCAinference(reference.fits, VarVC=TRUE)
rescaled.infs <- reScale(scaled.infs)
for(i in 1:length(scaled.infs))
{
scl <- rescaled.infs[[i]]
ref <- reference.infs[[i]]
sfac <- 1e12/scl$VCAobj$scale
tol <- 1/sfac
checkEquals(ref$VCAobj$aov.tab[,"VC"], scl$VCAobj$aov.tab[,"VC"], tolerance=tol)
checkEquals(diag(ref$vcovVC), diag(scl$vcovVC), tolerance=tol)
}
}
TF052.Scale.reScale.anovaMM <- function()
{
data(VCAdata1)
scaled.fits <- Scale("anovaMM", y~((device)+(lot))/(day)/(run), VCAdata1, by="sample")
reference.fits <- anovaMM( y~((device)+(lot))/(day)/(run), VCAdata1, by="sample")
scaled.infs <- VCAinference(scaled.fits, VarVC=TRUE)
reference.infs <- VCAinference(reference.fits, VarVC=TRUE)
rescaled.infs <- reScale(scaled.infs)
for(i in 1:length(scaled.infs))
{
scl <- rescaled.infs[[i]]
ref <- reference.infs[[i]]
tol <- 1e-10
checkEquals(ref$VCAobj$aov.tab[,"SD"], scl$VCAobj$aov.tab[,"SD"], tolerance=tol)
checkEquals(diag(ref$vcovVC), diag(scl$vcovVC), tolerance=tol)
}
}
TF053.Scale.reScale.remlVCA <- function()
{
data(VCAdata1)
scaled.fits <- Scale("remlVCA", y~(device+lot)/day/run, VCAdata1, by="sample")
reference.fits <- remlVCA(y~(device+lot)/day/run, VCAdata1, by="sample")
scaled.infs <- VCAinference(scaled.fits, VarVC=TRUE)
reference.infs <- VCAinference(reference.fits, VarVC=TRUE)
rescaled.infs <- reScale(scaled.infs)
for(i in 1:length(scaled.infs))
{
scl <- rescaled.infs[[i]]
ref <- reference.infs[[i]]
tol <- 1e-5
checkEquals(ref$VCAobj$aov.tab[,"SD"], scl$VCAobj$aov.tab[,"SD"], tolerance=tol)
checkEquals(diag(ref$vcovVC), diag(scl$vcovVC), tolerance=tol)
}
}
TF054.Scale.reScale.remlMM <- function()
{
data(VCAdata1)
scaled.fits <- Scale("remlMM", y~((device)+(lot))/(day)/(run), VCAdata1, by="sample")
reference.fits <- remlMM( y~((device)+(lot))/(day)/(run), VCAdata1, by="sample")
scaled.infs <- VCAinference(scaled.fits, VarVC=TRUE)
reference.infs <- VCAinference(reference.fits, VarVC=TRUE)
rescaled.infs <- reScale(scaled.infs)
for(i in 1:length(scaled.infs))
{
scl <- rescaled.infs[[i]]
ref <- reference.infs[[i]]
tol <- 1e-5
checkEquals(ref$VCAobj$aov.tab[,"SD"], scl$VCAobj$aov.tab[,"SD"], tolerance=tol)
checkEquals(diag(ref$vcovVC), diag(scl$vcovVC), tolerance=tol)
}
}
TF055.fitVCA.anovaVCA.remlVCA <- function()
{
data(VCAdata1)
sgnf <- 5
for(i in 1:10)
{
tmpData <- subset(VCAdata1, sample==i)
fit0.anova <- anovaVCA(y~(device+lot)/day/run, tmpData)
fit0.reml <- remlVCA( y~(device+lot)/day/run, tmpData)
fit1.anova <- fitVCA( y~(device+lot)/day/run, tmpData, method="anova", scale=(i%%2==0))
fit1.reml <- fitVCA( y~(device+lot)/day/run, tmpData, method="reml", scale=(i%%2!=0))
cat("\nsample",i,":\n")
print(checkEquals(round(fit0.anova$aov.tab[,"VC"], sgnf), round(fit1.anova$aov.tab[,"VC"], sgnf)))
print(checkEquals(round(fit0.reml$aov.tab[ ,"VC"], sgnf), round(fit1.reml$aov.tab[, "VC"], sgnf)))
}
}
TF056.orderData.remlVCA <- function()
{
data(MLrepro)
MLrepro.ord <- with(MLrepro, MLrepro[order(Lab, Lot, Day, Run),])
opt.new <- options(scipen=12)
M1.ref <- remlVCA(Result ~ (Lab + Lot)/Day/Run, MLrepro.ord, VarVC = TRUE)
M1 <- remlVCA(Result ~ (Lab + Lot)/Day/Run, MLrepro, VarVC = TRUE)
checkEquals(M1$aov.tab, M1.ref$aov.tab)
options(opt.new)
}
TF057.orderData.remlMM <- function()
{
data(MLrepro)
MLrepro.ord <- with(MLrepro, MLrepro[order(Lab, Lot, Day, Run),])
opt.new <- options(scipen=12)
M1.ref <- remlMM(Result ~ ((Lab) + (Lot))/(Day)/(Run), MLrepro.ord, VarVC = TRUE, order.data = FALSE)
M1 <- remlMM(Result ~ ((Lab) + (Lot))/(Day)/(Run), MLrepro, VarVC = TRUE, order.data = TRUE)
checkEquals(M1$aov.tab, M1.ref$aov.tab)
options(opt.new)
}
TF058.predict.anovaMM <- function()
{
data(VCAdata1)
VCAdata1_sample5 <- subset(VCAdata1, sample==5)
fitS5 <- fitLMM(y~(device+lot)/(day)/(run), VCAdata1_sample5, "anova")
pred <- predict(fitS5)
SASref <- c(17.899050479, 17.899050479, 18.019161777, 18.019161777, 17.851799074,
17.851799074, 17.844764076, 17.844764076, 16.814900319, 16.814900319,
18.099103148, 18.099103148, 18.323024006, 18.323024006, 18.402690956,
18.402690956, 18.02856588, 18.02856588, 17.414336609, 17.414336609,
18.01615201, 18.01615201, 18.150495477, 18.150495477, 17.810710437,
17.810710437, 17.541187839, 17.541187839, 17.891970672, 17.891970672,
17.477222676, 17.477222676, 16.936743521, 16.936743521, 17.009863088,
17.009863088, 16.996838687, 16.996838687, 17.742573591, 17.742573591,
17.181168314, 17.181168314, 17.913059433, 17.913059433, 17.886696332,
17.886696332, 17.920933794, 17.920933794, 17.453001574, 17.453001574,
17.908328271, 17.908328271, 17.168513101, 17.168513101, 17.208107861,
17.208107861, 18.350257975, 18.350257975, 18.454610947, 18.454610947,
18.05353796, 18.05353796, 18.148658917, 18.148658917, 17.782646776,
17.782646776, 18.191321439, 18.191321439, 17.747847363, 17.747847363,
18.160575146, 18.160575146, 17.854959068, 17.854959068, 17.94836637,
17.94836637, 17.778571123, 17.778571123, 17.981690354, 17.981690354,
17.721834759, 17.721834759, 18.210214242, 18.210214242, 17.426262307,
17.426262307, 17.516597821, 17.516597821, 17.995705373, 17.995705373,
17.839060278, 17.839060278, 17.870866129, 17.870866129, 17.455708536,
17.455708536, 17.484376436, 17.484376436, 17.633759632, 17.633759632,
18.059467054, 18.059467054, 18.109651477, 18.109651477, 17.763661497,
17.763661497, 17.678617607, 17.678617607, 17.649497267, 17.649497267,
17.671977329, 17.671977329, 16.844471453, 16.844471453, 16.304322063,
16.304322063, 17.155502328, 17.155502328, 16.959623965, 16.959623965,
17.248848449, 17.248848449, 17.203171369, 17.203171369, 16.377849768,
16.377849768, 16.840993943, 16.840993943, 17.598257565, 17.598257565,
16.993849474, 16.993849474, 15.847775522, 15.847775522, 16.139327587,
16.139327587, 17.235607942, 17.235607942, 17.194286781, 17.194286781,
18.151865586, 18.151865586, 18.006728937, 18.006728937, 17.932605325,
17.932605325, 17.993503934, 17.993503934, 17.972653873, 17.972653873,
17.938784406, 17.938784406, 17.656457414, 17.656457414, 18.171206871,
18.171206871, 18.056473288, 18.056473288, 18.12599667, 18.12599667,
18.195670704, 18.195670704, 18.531011728, 18.531011728, 18.067735776,
18.067735776, 18.445249298, 18.445249298, 17.374363044, 17.374363044,
17.467161232, 17.467161232, 17.095178243, 17.095178243, 17.74331252,
17.74331252, 17.470110835, 17.470110835, 17.734284011, 17.734284011,
17.575357936, 17.575357936, 18.042970992, 18.042970992, 17.151979443,
17.151979443, 17.175104385, 17.175104385, 17.442102655, 17.442102655,
16.823553712, 16.823553712, 17.519946213, 17.519946213, 17.338524873,
17.338524873, 17.147467912, 17.147467912, 16.713978566, 16.713978566,
16.472854882, 16.472854882, 16.65792692, 16.65792692, 15.675853935,
15.675853935, 16.380109642, 16.380109642, 16.915108884, 16.915108884,
17.599223625, 17.599223625, 16.92119403, 16.92119403, 16.700892957,
16.700892957, 15.747910906, 15.747910906, 15.559174464, 15.559174464,
17.193351366, 17.193351366, 17.602150973, 17.602150973, 17.502256685,
17.502256685, 17.701333304, 17.701333304, 17.407119593, 17.407119593,
17.591792282, 17.591792282, 17.188676031, 17.188676031, 17.659079358,
17.659079358, 17.552768564, 17.552768564, 17.347926526, 17.347926526,
16.295013278, 16.295013278, 16.52279957, 16.52279957, 16.232855875,
16.232855875, 15.900764272, 15.900764272, 16.411652347, 16.411652347,
17.135995502, 17.135995502)
checkEqualsNumeric(as.vector(pred), SASref)
}
TF059.predict.remlMM.newdata <- function()
{
data(VCAdata1)
datS5 <- subset(VCAdata1, sample==5)
set.seed(1)
datS5$cov <- round(rnorm(nrow(datS5), 65, 8),1)
fitS5 <- fitLMM(y~cov+device+lot+(day)+(run), datS5, "reml")
newdata <- datS5[c(1,62),]
newdata$cov <- newdata$cov + 3.5
pred <- predict(fitS5, newdata)
SASref <- c(18.12473221,17.28640975)
checkEqualsNumeric(as.vector(pred), SASref, tolerance=1e-6)
}
TF060.predict.remlMM.newdata <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age-11
fit.fitLMM <- fitLMM(distance~Sex*age2+(Subject)*age2, Ortho, "reml")
newdata <- Ortho[c(45,75),]
newdata$age2 <- newdata$age2 + 5
pred <- predict(fit.fitLMM, newdata)
SASref <- c(26.0117,27.2067)
checkEqualsNumeric(as.vector(pred), SASref, tolerance=1e-4)
}
TF061.predict.remlMM.restriction <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age-11
fit.fitLMM <- fitLMM(distance~Sex*age2+(Subject)*age2, Ortho, "reml")
pred <- predict(fit.fitLMM, re=NA)
SASref <- c(21.209090909, 22.168181818, 23.127272727, 24.086363636, 21.209090909,
22.168181818, 23.127272727, 24.086363636, 21.209090909, 22.168181818,
23.127272727, 24.086363636, 21.209090909, 22.168181818, 23.127272727,
24.086363636, 21.209090909, 22.168181818, 23.127272727, 24.086363636,
21.209090909, 22.168181818, 23.127272727, 24.086363636, 21.209090909,
22.168181818, 23.127272727, 24.086363636, 21.209090909, 22.168181818,
23.127272727, 24.086363636, 21.209090909, 22.168181818, 23.127272727,
24.086363636, 21.209090909, 22.168181818, 23.127272727, 24.086363636,
21.209090909, 22.168181818, 23.127272727, 24.086363636, 22.615625,
24.184375, 25.753125, 27.321875, 22.615625, 24.184375, 25.753125,
27.321875, 22.615625, 24.184375, 25.753125, 27.321875, 22.615625,
24.184375, 25.753125, 27.321875, 22.615625, 24.184375, 25.753125,
27.321875, 22.615625, 24.184375, 25.753125, 27.321875, 22.615625,
24.184375, 25.753125, 27.321875, 22.615625, 24.184375, 25.753125,
27.321875, 22.615625, 24.184375, 25.753125, 27.321875, 22.615625,
24.184375, 25.753125, 27.321875, 22.615625, 24.184375, 25.753125,
27.321875, 22.615625, 24.184375, 25.753125, 27.321875, 22.615625,
24.184375, 25.753125, 27.321875, 22.615625, 24.184375, 25.753125,
27.321875, 22.615625, 24.184375, 25.753125, 27.321875, 22.615625,
24.184375, 25.753125, 27.321875)
checkEqualsNumeric(as.vector(pred), SASref, tolerance=1e-6)
}
TF062.comparison.fitLMM.REML <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age-11
fit.fitLMM <- fitLMM(distance~Sex*age2+(Subject)*age2, Ortho, "reml")
fit.remlMM <- remlMM(distance~Sex*age2+(Subject)*age2, Ortho)
checkEqualsNumeric(fit.fitLMM$aov.tab, fit.remlMM$aov.tab, tolerance=1e-7)
}
TF063.comparison.fitLMM.ANOVA <- function()
{
data(Orthodont)
Ortho <- Orthodont
Ortho$age2 <- Ortho$age-11
fit.fitLMM <- fitLMM(distance~Sex*age2+(Subject)*age2, Ortho)
fit.anovaMM <- anovaMM(distance~Sex*age2+(Subject)*age2, Ortho)
checkEqualsNumeric(fit.fitLMM$aov.tab, fit.anovaMM$aov.tab, tolerance=1e-8)
}
TF064.Scale.reScale.response <- function()
{
data(VCAdata1)
sample1 <- VCAdata1[VCAdata1$sample==1,]
scaled.fit <- Scale("remlMM", y~((device)+(lot))/(day)/(run), sample1)
scaled.inf <- VCAinference(scaled.fit, VarVC=TRUE)
rescaled.inf <- reScale(scaled.inf)
ref.response <- orderData(sample1 , y~((device)+(lot))/(day)/(run))$y
checkEqualsNumeric(rescaled.inf$VCAobj$data$y, ref.response, tolerance=1e-10)
}
TF065.comparison.fitVCA.REML <- function()
{
data(dataEP05A2_2)
fit.fitVCA <- fitVCA(y~day/run, dataEP05A2_2, "reml")
fit.remlVCA <- remlVCA(y~day/run, dataEP05A2_2)
checkEqualsNumeric(fit.fitVCA$aov.tab, fit.remlVCA$aov.tab, tolerance=1e-8)
}
TF066.comparison.fitVCA.ANOVA <- function()
{
data(dataEP05A2_2)
fit.fitVCA <- fitVCA(y~day/run, dataEP05A2_2, "anova")
fit.anovaVCA <- anovaVCA(y~day/run, dataEP05A2_2)
checkEqualsNumeric(fit.fitVCA$aov.tab, fit.anovaVCA$aov.tab, tolerance=1e-8)
}
TF067.checkException.NoReScaling <- function()
{
data(VCAdata1)
datS511 <- subset(VCAdata1, sample==5 & lot == 1 & device == 1)
fit <- Scale("anovaVCA", y~day/run, datS511)
options(warn=2)
checkException(ranef(fit))
checkException(resid(fit))
options(warn=0)
}
TF068.check.Scale.reScale.anovaVCA <- function()
{
data(VCAdata1)
datS5 <- subset(VCAdata1, sample == 5)
datHV <- datS5
datHV$y <- datHV$y * 1e7
fit.anovaVCA <- Scale("anovaVCA", y~(device+lot)/day/run, datHV)
fit.anovaVCA <- reScale(fit.anovaVCA)
fit0 <- anovaVCA(y~((device)+(lot))/day/run, datS5)
VarCov0 <- vcovVC(fit0)
fit1 <- Scale("anovaVCA", y~((device)+(lot))/day/run, datS5)
fit1 <- reScale(fit1, VarVC=TRUE)
VarCov1 <- vcovVC(fit1)
tol <- 1e-6
digits <- log10(1/tol)
checkEquals(round(VarCov0, digits=digits), round(VarCov1, digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0),digits=digits), round(ranef(fit1),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="student"),digits=digits), round(ranef(fit1, mode="student"),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="standard"),digits=digits), round(ranef(fit1, mode="standard"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond"),digits=digits), round(resid(fit1, term="cond"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="student"),digits=digits), round(resid(fit1, term="cond", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="pearson"),digits=digits), round(resid(fit1,term="cond", mode="pearson"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal"),digits=digits), round(resid(fit1, term="marginal"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="student"),digits=digits), round(resid(fit1, term="marginal", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="pearson"),digits=digits), round(resid(fit1,term="marginal", mode="pearson"),digits=digits), tolerance=tol)
}
TF069.check.Scale.reScale.remlVCA <- function()
{
data(VCAdata1)
datS5 <- subset(VCAdata1, sample == 5)
datHV <- datS5
datHV$y <- datHV$y * 1e7
fit.remlVCA <- Scale("remlVCA", y~(device+lot)/day/run, datHV)
fit.remlVCA <- reScale(fit.remlVCA)
fit0 <- remlVCA(y~((device)+(lot))/day/run, datS5)
VarCov0 <- vcovVC(fit0)
fit1 <- Scale("remlVCA", y~((device)+(lot))/day/run, datS5)
fit1 <- reScale(fit1, VarVC=TRUE)
VarCov1 <- vcovVC(fit1)
tol <- 1e-4
digits <- log10(1/tol)
checkEquals(round(VarCov0, 6), round(VarCov1, 6), tolerance=1e-6)
checkEquals(round(ranef(fit0),digits=digits), round(ranef(fit1),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="student"),digits=digits), round(ranef(fit1, mode="student"),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="standard"),digits=digits), round(ranef(fit1, mode="standard"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond"),digits=digits), round(resid(fit1, term="cond"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="student"),digits=digits), round(resid(fit1, term="cond", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="pearson"),digits=digits), round(resid(fit1,term="cond", mode="pearson"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal"),digits=digits), round(resid(fit1, term="marginal"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="student"),digits=digits), round(resid(fit1, term="marginal", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="pearson"),digits=digits), round(resid(fit1,term="marginal", mode="pearson"),digits=digits), tolerance=tol)
}
TF070.check.Scale.reScale.anovaMM <- function()
{
data(VCAdata1)
datS5 <- subset(VCAdata1, sample == 5)
datHV <- datS5
datHV$y <- datHV$y * 1e7
fit.anovaMM <- Scale("anovaMM", y~(device+lot)/(day)/(run), datHV)
fit.anovaMM <- reScale(fit.anovaMM)
fit0 <- anovaMM(y~(device+lot)/(day)/(run), datS5)
VarCov0 <- vcovVC(fit0)
fit1 <- Scale("anovaMM", y~(device+lot)/(day)/(run), datS5)
fit1 <- reScale(fit1, VarVC=TRUE)
VarCov1 <- vcovVC(fit1)
tol <- 1e-6
digits <- log10(1/tol)
checkEquals(round(VarCov0, 6), round(VarCov1, 6), tolerance=1e-6)
checkEquals(round(ranef(fit0),digits=digits), round(ranef(fit1),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="student"),digits=digits), round(ranef(fit1, mode="student"),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="standard"),digits=digits), round(ranef(fit1, mode="standard"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond"),digits=digits), round(resid(fit1, term="cond"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="student"),digits=digits), round(resid(fit1, term="cond", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="pearson"),digits=digits), round(resid(fit1,term="cond", mode="pearson"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal"),digits=digits), round(resid(fit1, term="marginal"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="student"),digits=digits), round(resid(fit1, term="marginal", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="pearson"),digits=digits), round(resid(fit1,term="marginal", mode="pearson"),digits=digits), tolerance=tol)
}
TF071.check.Scale.reScale.remlMM <- function()
{
data(VCAdata1)
datS5 <- subset(VCAdata1, sample == 5)
datHV <- datS5
datHV$y <- datHV$y * 1e7
fit.remlMM <- Scale("remlMM", y~(device+lot)/(day)/(run), datHV)
fit.remlMM <- reScale(fit.remlMM)
fit0 <- remlMM(y~(device+lot)/(day)/(run), datS5)
VarCov0 <- vcovVC(fit0)
fit1 <- Scale("remlMM", y~(device+lot)/(day)/(run), datS5)
fit1 <- reScale(fit1, VarVC=TRUE)
VarCov1 <- vcovVC(fit1)
tol <- 1e-5
digits <- log10(1/tol)
checkEquals(round(VarCov0, 6), round(VarCov1, 6), tolerance=1e-6)
checkEquals(round(ranef(fit0),digits=digits), round(ranef(fit1),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="student"),digits=digits), round(ranef(fit1, mode="student"),digits=digits), tolerance=tol)
checkEquals(round(ranef(fit0, mode="standard"),digits=digits), round(ranef(fit1, mode="standard"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond"),digits=digits), round(resid(fit1, term="cond"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="student"),digits=digits), round(resid(fit1, term="cond", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="cond", mode="pearson"),digits=digits), round(resid(fit1,term="cond", mode="pearson"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal"),digits=digits), round(resid(fit1, term="marginal"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="student"),digits=digits), round(resid(fit1, term="marginal", mode="student"),digits=digits), tolerance=tol)
checkEquals(round(resid(fit0, term="marginal", mode="pearson"),digits=digits), round(resid(fit1,term="marginal", mode="pearson"),digits=digits), tolerance=tol)
}
TF072.getMM.multi.covariables <- function()
{
data <- as.data.frame(matrix(rnorm(40), nrow=10))
colnames(data) <- c('V.1', 'V.2', 'V.3', 'V.4')
form <- as.formula('V.1 ~ V.2 + V.3 + V.4 + V.2 * V.3 + V.2 * V.4 + V.3 * V.4 + V.2 * V.3 * V.4')
X0 <- model.matrix(form, data = data)
X1 <- getMM(form, Data = data)
checkEquals(c(X0), c(as.matrix(X1)))
data2 <- data
data2$subject <- gl(5, 2)
form2 <- as.formula('V.1 ~ V.2 + subject*V.2 * V.3')
X3 <- model.matrix(form2, data2)
X4 <- getMM(form2, data2)
checkEquals(c(X3[,c(1:6, 8:11, 7, 12:20)]), c(as.matrix(X4)[,-c(3,8,14,20)]))
}
TF073.fitVCA_ANOVA_VarVC_TRUE <- function()
{
data(dataEP05A2_3)
fit0 <- anovaVCA(y~day/run, dataEP05A2_3)
fit1 <- fitVCA( y~day/run, dataEP05A2_3)
inf0 <- VCAinference(fit0, VarVC=TRUE)
inf1 <- VCAinference(fit1, VarVC=TRUE)
checkEquals(unlist(inf0$ConfInt), unlist(inf1$ConfInt))
}
TF074.fitVCA_REML_VarVC_TRUE <- function()
{
fit0 <- remlVCA(y~day/run, dataEP05A2_3)
fit1 <- fitVCA( y~day/run, dataEP05A2_3, "REML")
inf0 <- VCAinference(fit0, VarVC=TRUE)
inf1 <- VCAinference(fit1, VarVC=TRUE)
checkEquals(unlist(inf0$ConfInt), unlist(inf1$ConfInt), tol=1e-6)
}
|
print.mlgarch <-
function(x, varma=FALSE, ...)
{
pars <- coef.mlgarch(x, varma=varma)
vcovmat <- vcov.mlgarch(x, varma=varma)
out1 <- rbind(pars, sqrt(diag(vcovmat)))
rownames(out1) <- c("Estimate:", "Std. Error:")
out2 <- as.data.frame(matrix(NA,4,1))
out2[1,1] <- as.character(round(logLik.mlgarch(x, varma=FALSE), digits=3))
out2[2,1] <- as.character(round(logLik.mlgarch(x, varma=TRUE), digits=3))
out2[3,1] <- as.character(round(x$aux$ynonzerorowsn, digits=0))
out2[4,1] <- as.character(round(x$aux$yanyrowiszeron, digits=0))
rownames(out2) <- c("Log-likelihood (log-mgarch):",
"Log-likelihood (varma):", "No. of obs. without zeros:",
"No. of obs. with zeros:")
colnames(out2) <- ""
cat("\n")
cat("Date:", x$date, "\n")
cat("Method: Multivariate ML \n")
cat("Message (nlminb):", x$message, "\n")
cat("No. of observations:", x$aux$n, "\n")
cat("Sample:", as.character(x$aux$y.index[1]),
"to", as.character(x$aux$y.index[x$aux$n]), "\n")
cat("\n")
cat("Coefficients:\n")
cat("\n")
print(out1)
print(out2)
cat("\n")
}
|
fetchVegdata <- function(SS=TRUE, stringsAsFactors = default.stringsAsFactors(), dsn = NULL) {
if (!local_NASIS_defined(dsn))
stop('Local NASIS ODBC connection has not been setup. Please see `http://ncss-tech.github.io/AQP/soilDB/setup_local_nasis.html`.')
vegplot <- get_vegplot_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegplotlocation <-get_vegplot_location_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegplotrhi <- get_vegplot_trhi_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegplotspecies <- get_vegplot_species_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegtransect <- get_vegplot_transect_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegtransplantsum <- get_vegplot_transpecies_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegsiteindexsum <- get_vegplot_tree_si_summary_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegsiteindexdet <- get_vegplot_tree_si_details_from_NASIS_db(SS = SS, stringsAsFactors = stringsAsFactors, dsn = dsn)
vegplottext <- get_vegplot_textnote_from_NASIS_db(SS = SS, fixLineEndings = TRUE,
stringsAsFactors = stringsAsFactors, dsn = dsn)
if (nrow(vegplot) == 0) message('your selected set is missing either the vegplot, pedon or site table, please load and try again :)')
return(list(
vegplot = vegplot,
vegplotlocation = vegplotlocation,
vegplotrhi = vegplotrhi,
vegplotspecies = vegplotspecies,
vegtransect = vegtransect,
vegtransplantsum = vegtransplantsum,
vegsiteindexsum = vegsiteindexsum,
vegsiteindexdet = vegsiteindexdet,
vegplottext = vegplottext
))
}
|
summary.dbv.cartogramR <- function(object, ...) {
L3 <- dbv <- NULL
if (!inherits(object, "dbv.cartogramR"))
stop(paste(deparse(substitute(object)), "must be a dbv.cartogramR object"))
object2 <- copy(object)
return(setorder(object2, -dbv)[1:10,])
}
|
library(ptm)
context("GO-related")
test_that("search.go() works properly", {
skip_on_cran()
skip_on_travis()
a <- search.go(query = 'oxidative stress')
b <- search.go(query = 'methionine')
c <- search.go(query = 'xxxxxx')
if (!is.null(a)){
expect_is(a, 'data.frame')
expect_gte(nrow(a), 300)
expect_equal(ncol(a), 5)
expect_true('GO:0070994' %in% a$GO_id)
}
if (!is.null(b)){
expect_is(b, 'data.frame')
expect_gte(nrow(b), 100)
expect_equal(ncol(b), 5)
expect_true('GO:0015821' %in% b$GO_id)
}
expect_is(c, 'NULL')
})
test_that("term.go() works properly", {
skip_on_cran()
skip_on_travis()
a <- term.go('GO:0034599')
b <- term.go('GO:0005886', children = TRUE)
c <- term.go('xxxxxxx')
if (!is.null(a)){
expect_is(a, 'data.frame')
expect_equal(nrow(a), 1)
expect_equal(ncol(a), 7)
expect_equal(a$term_name, "cellular response to oxidative stress")
}
if (!is.null(b)){
expect_is(b, 'list')
expect_is(b[[1]], 'data.frame')
expect_equal(nrow(b[[1]]), 1)
expect_equal(ncol(b[[1]]), 7)
expect_equal(b[[1]]$term_name, "plasma membrane")
expect_is(b[[2]], 'data.frame')
expect_equal(ncol(b[[2]]), 2)
}
expect_is(c, 'NULL')
})
test_that("get.go() works properly", {
skip_on_cran()
skip_on_travis()
a <- get.go(id = "P01009")
b <- get.go(id = 'P01009', filter = FALSE)
c <- get.go(id = 'P04406', format = 'string')
d <- get.go(id = 'P04406', filter = FALSE, format = 'string')
e <- get.go(id = 'P00367')
f <- get.go(id = 'Q14687')
g <- get.go(id = "P010091")
h <- get.go("AAA58698", filter = FALSE)
i <- get.go("AAA58698")
if (!is.null(a)){
expect_is(a, 'data.frame')
expect_gte(nrow(a), 20)
expect_equal(ncol(a), 5)
}
if (!is.null(b)){
expect_is(b, 'data.frame')
expect_gte(nrow(b), 70)
expect_equal(ncol(b), 8)
}
if (!is.null(c)){
expect_is(c, 'character')
expect_gte(nchar(c), 400)
}
if (!is.null(d)){
expect_is(d, "character")
expect_gte(nchar(d), 1100)
}
if (!is.null(e)){
expect_is(e, 'data.frame')
expect_gte(nrow(e), 15)
expect_equal(ncol(e), 5)
}
expect_is(f, 'NULL')
expect_is(g, 'NULL')
expect_is(h, 'NULL')
expect_is(i, 'NULL')
})
test_that("bg.go() works properly", {
skip_on_cran()
skip_on_travis()
a <- bg.go(ids = "./go/id_set.txt")
b <- bg.go(ids = c("Q13015", "Q14667", "P08575", "Q5JSZ5", "P13196", "H7C4H7"))
if (!is.null(a)){
expect_is(a, 'data.frame')
expect_equal(nrow(a), 6)
expect_equal(ncol(a), 2)
}
if (!is.null(b)){
expect_is(b, 'data.frame')
expect_equal(nrow(b), 6)
expect_equal(ncol(b), 2)
expect_equal(a, b)
}
})
test_that(" hdfisher.go() works properly", {
skip_on_cran()
skip_on_travis()
backg <- bg.go(c("Q13015", "Q14667", "P08575", "Q5JSZ5", "P13196"))
if (!is.null(backg)){
a <- hdfisher.go(target = c('Q14667', 'Q5JSZ5'),
background = backg, query = 'extracellular')
} else {
a <- NULL
}
if (!is.null(backg)){
b <- hdfisher.go(target = c('Q14667', 'Q5JSZ5'),
background = backg,
query = 'xxxxxxx')
} else {
b <- NULL
}
if (!is.null(a)){
expect_is(a, 'list')
expect_is(a[[1]], 'matrix')
expect_is(a[[2]], 'numeric')
expect_true(attributes(a)$query == 'extracellular')
}
expect_is(b, 'NULL')
})
test_that("net.go() works properly", {
skip_on_cran()
skip_on_travis()
a <- net.go(data = "./go/id_set.txt", threshold = 0.1)
b <- net.go(data = "./go/id_set.Rda", threshold = 0.1)
c <- net.go(data = "./go/id_set_dummy.txt", threshold = 0.1)
expect_is(a, 'list')
expect_is(a[[1]], 'matrix')
expect_equal(dim(a[[1]]), c(6,6))
expect_true(!isSymmetric(a[[1]]))
expect_is(a[[2]], 'matrix')
expect_equal(dim(a[[2]]), c(6,6))
expect_true(isSymmetric(a[[2]]))
expect_is(a[[3]], 'character')
expect_equal(length(a[[3]]), 6)
expect_is(a[[4]], 'matrix')
expect_equal(ncol(a[[4]]), 2)
expect_is(b, 'list')
expect_is(b[[1]], 'matrix')
expect_equal(dim(b[[1]]), c(6,6))
expect_true(!isSymmetric(b[[1]]))
expect_is(b[[2]], 'matrix')
expect_equal(dim(b[[2]]), c(6,6))
expect_true(isSymmetric(b[[2]]))
expect_is(b[[3]], 'character')
expect_equal(length(b[[3]]), 6)
expect_is(b[[4]], 'matrix')
expect_equal(ncol(b[[4]]), 2)
expect_is(c, 'list')
expect_is(c[[1]], 'matrix')
expect_equal(dim(c[[1]]), c(5,5))
expect_true(!isSymmetric(c[[1]]))
expect_is(c[[2]], 'matrix')
expect_equal(dim(c[[2]]), c(5,5))
expect_true(isSymmetric(c[[2]]))
expect_is(c[[3]], 'character')
expect_equal(length(c[[3]]), 6)
expect_is(c[[4]], 'matrix')
expect_equal(ncol(c[[4]]), 2)
})
|
map <- function(.x, .f, ...) {
lapply(.x, .f, ...)
}
map_mold <- function(...) {
out <- vapply(..., USE.NAMES = FALSE)
names(out) <- names(..1)
out
}
map_lgl <- function(.x, .f, ...) {
map_mold(.x, .f, logical(1), ...)
}
map_int <- function(.x, .f, ...) {
map_mold(.x, .f, integer(1), ...)
}
map_dbl <- function(.x, .f, ...) {
map_mold(.x, .f, double(1), ...)
}
map_chr <- function(.x, .f, ...) {
map_mold(.x, .f, character(1), ...)
}
map_cpl <- function(.x, .f, ...) {
map_mold(.x, .f, complex(1), ...)
}
pluck <- function(.x, .f) {
map(.x, `[[`, .f)
}
pluck_lgl <- function(.x, .f) {
map_lgl(.x, `[[`, .f)
}
pluck_int <- function(.x, .f) {
map_int(.x, `[[`, .f)
}
pluck_dbl <- function(.x, .f) {
map_dbl(.x, `[[`, .f)
}
pluck_chr <- function(.x, .f) {
map_chr(.x, `[[`, .f)
}
pluck_cpl <- function(.x, .f) {
map_cpl(.x, `[[`, .f)
}
map2 <- function(.x, .y, .f, ...) {
mapply(.f, .x, .y, MoreArgs = list(...), SIMPLIFY = FALSE)
}
map2_lgl <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "logical")
}
map2_int <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "integer")
}
map2_dbl <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "double")
}
map2_chr <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "character")
}
map2_cpl <- function(.x, .y, .f, ...) {
as.vector(map2(.x, .y, .f, ...), "complex")
}
args_recycle <- function(args) {
lengths <- map_int(args, length)
n <- max(lengths)
stopifnot(all(lengths == 1L | lengths == n))
to_recycle <- lengths == 1L
args[to_recycle] <- map(args[to_recycle], function(x) rep.int(x, n))
args
}
pmap <- function(.l, .f, ...) {
args <- args_recycle(.l)
do.call("mapply", c(
FUN = list(quote(.f)),
args, MoreArgs = quote(list(...)),
SIMPLIFY = FALSE, USE.NAMES = FALSE
))
}
probe <- function(.x, .p, ...) {
if (is_logical(.p)) {
stopifnot(length(.p) == length(.x))
.p
} else {
map_lgl(.x, .p, ...)
}
}
keep <- function(.x, .f, ...) {
.x[probe(.x, .f, ...)]
}
discard <- function(.x, .p, ...) {
sel <- probe(.x, .p, ...)
.x[is.na(sel) | !sel]
}
map_if <- function(.x, .p, .f, ...) {
matches <- probe(.x, .p)
.x[matches] <- map(.x[matches], .f, ...)
.x
}
compact <- function(.x) {
Filter(length, .x)
}
transpose <- function(.l) {
inner_names <- names(.l[[1]])
result <- map(seq_along(.l[[1]]), function(i) {
map(.l, .subset2, i)
})
set_names(result, inner_names)
}
every <- function(.x, .p, ...) {
for (i in seq_along(.x)) {
if (!rlang::is_true(.p(.x[[i]], ...))) return(FALSE)
}
TRUE
}
some <- function(.x, .p, ...) {
for (i in seq_along(.x)) {
if (rlang::is_true(.p(.x[[i]], ...))) return(TRUE)
}
FALSE
}
negate <- function(.p) {
function(...) !.p(...)
}
reduce <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(x, y, ...)
Reduce(f, .x, init = .init)
}
reduce_right <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(y, x, ...)
Reduce(f, .x, init = .init, right = TRUE)
}
accumulate <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(x, y, ...)
Reduce(f, .x, init = .init, accumulate = TRUE)
}
accumulate_right <- function(.x, .f, ..., .init) {
f <- function(x, y) .f(y, x, ...)
Reduce(f, .x, init = .init, right = TRUE, accumulate = TRUE)
}
invoke <- function(.f, .x, ..., .env = NULL){
.env <- .env %||% parent.frame()
args <- c(as.list(.x), list(...))
do.call(.f, args, envir = .env)
}
imap <- function(.x, .f, ...){
map2(.x, names(.x) %||% seq_along(.x), .f, ...)
}
capture_error <- function (code, otherwise = NULL, quiet = TRUE)
{
tryCatch(list(result = code, error = NULL), error = function(e) {
if (!quiet)
message("Error: ", e$message)
list(result = otherwise, error = e)
}, interrupt = function(e) {
stop("Terminated by user", call. = FALSE)
})
}
safely <- function (.f, otherwise = NULL, quiet = TRUE)
{
function(...) capture_error(.f(...), otherwise, quiet)
}
possibly <- function (.f, otherwise, quiet = TRUE)
{
force(otherwise)
function(...) capture_error(.f(...), otherwise, quiet)$result
}
compose <- function (...) {
fs <- lapply(list(...), match.fun)
n <- length(fs)
last <- fs[[n]]
rest <- fs[-n]
function(...) {
out <- last(...)
for (f in rev(rest)) {
out <- f(out)
}
out
}
}
|
Ehrenfest <-
function(n) {
States <- c(0, seq(1,2*n))
TPM <- matrix(0,nrow=length(States),ncol=length(States),dimnames=
list(seq(0,2*n),seq(0,2*n)))
tran_prob <- function(i,n) {
tranRow <- rep(0,2*n+1)
if(i==0) tranRow[2] <- 1
if(i==2*n) tranRow[(2*n+1)-1] <- 1
if(i!=0 & i!=2*n) {
j=i+1
tranRow[j-1] <- i/(2*n)
tranRow[j+1] <- 1-i/(2*n)
}
return(tranRow)
}
for(j in 0:(2*n))TPM[j+1,] <- tran_prob(j,n)
return(TPM)
}
|
kin.coef <- function(n.parents, n.sibs)
{
n.person <- n.parents+n.sibs
kin.coef <- matrix(0.25, ncol=n.person, nrow=n.person)
diag(kin.coef) <- 0.5
if (n.parents == 2) kin.coef[1,2] <- kin.coef[2,1] <- 0
kin.coef
}
vec <- function(A) A[upper.tri(A,diag=FALSE)]
score.core <- function(b1, b2, r)
{
S1 <- max(b1, 0)^2
S2 <- NULL
if ((b1 > r*b2) & (b2 >= r*b1)) S2 <- (b1^2-2*r*b1*b2+b2^2)/(1-r^2)
else if ((b1 >= 0) & (b2 < r*b1)) S2 <- b1^2
else if ((b1 <= r*b2) & (b2 > 0)) S2 <- b2^2
else S2 <- 0
k <- acos(r)/(2*pi)
p1 <- 0.5-0.5*pchisq(S1, df = 1)
p2 <- 1-((0.5-k)+0.5*pchisq(S2, df=1)+k*pchisq(S2,df=2))
c(round(S1,3), round(S2,3), round(p1,4), round(p2,4))
}
reorder <- function(familyID, pair.id)
{
pairs <- do.call("rbind", strsplit(as.character(pair.id), ","))
p1 <- as.numeric(pairs[,1])
p2 <- as.numeric(pairs[,2])
ttt1 <- paste(familyID, pmin(p1, p2), sep=".")
ttt2 <- paste(familyID, pmax(p1, p2), sep=".")
paste(ttt1, ttt2, sep=",")
}
pair.data <- function(pheno,mean,h2,var)
{
pheno <- pheno[!is.na(pheno$phenotype),]
pheno <- pheno[order(pheno$family, pheno$father, pheno$mother),]
pairID <- allv <- NULL
for (family in unique(pheno$family))
{
target <- (pheno$family == family)
ttt <- pheno[target,]
n.parents <- sum(ttt$father == 0 & ttt$mother == 0)
n.sibs <- sum(ttt$father!=0 | ttt$mother !=0)
if (n.sibs < 2) next
Sigma0 <- 2*h2*kin.coef(n.parents,n.sibs)+(1-h2)*diag(n.parents+n.sibs)
Sigma0 <- var*Sigma0
Sigma0.1 <- solve(Sigma0)
w <- as.vector(Sigma0.1 %*% (ttt$phenotype-mean))
Wanted <- (n.parents+1):(n.parents+n.sibs)
v <- vec((outer(w,w)-Sigma0.1)[Wanted,Wanted])
names <- as.character(ttt$person)
pair.id <- vec(outer(names,names, FUN=paste, sep=",")[Wanted,Wanted])
n.pairs <- length(v)
pairID <- c(pairID, reorder(family,pair.id))
allv <- c(allv, v)
}
data.frame(pairID=pairID, v=allv)
}
score <- function(chrom.pos, pair.data, ibd)
{
ibddata <- ibd[ibd$pos == chrom.pos & ibd$prior.Z0 == 0.25 &
ibd$prior.Z1 == 0.5 & ibd$prior.Z2 == 0.25,
c("pedigree", "pair", "Z1", "Z2")]
newdata <- data.frame(pairID=reorder(ibddata$pedigree, ibddata$pair),
epi=ibddata$Z1/2+ibddata$Z2, pi2=ibddata$Z2)
joint.data <- merge(pair.data, newdata, by="pairID", all=FALSE)
b1 <- sum((joint.data$epi - 0.5) * joint.data$v)
b2 <- sum((joint.data$pi2 - 0.25) * joint.data$v)
Vepi <- var(cbind(joint.data$epi, joint.data$pi2))
Vb <- Vepi*sum(joint.data$v^2)
b1 <- b1/sqrt(Vb[1,1])
b2 <- b2/sqrt(Vb[2,2])
r <- Vb[1,2]/sqrt(Vb[1,1]*Vb[2,2])
c(chrom.pos, score.core(b1, b2, r))
}
comp.score <- function(ibddata="ibd_dist.out", phenotype="pheno.dat", mean=0, var=1, h2=0.3)
{
ibd <- read.table(ibddata, skip = 1, col.names = c("pos", "pedigree",
"pair", "prior.Z0", "prior.Z1", "prior.Z2", "Z0", "Z1", "Z2"),
colClasses=c("numeric", "integer", "character", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric"),
comment.char = "")
pheno <- read.table(phenotype, col.names = c("family",
"person", "father", "mother", "gender", "phenotype"))
paired <- pair.data(pheno,mean,h2,var)
result <- NULL
for (i in unique(ibd$pos)) result <- rbind(result, score(i, paired, ibd))
dimnames(result) <-
list(NULL, c("pos", "stat.S1", "stat.S2", "p.value.S1", "p.value.S2"))
result
}
|
p <- c(1, -2, 1, -1)
prior4.4 <- uniform(p -1.5, p + 1.5)
formula4.4 <- ~exp(b0+b1*x1+b2*x2+b3*x1*x2)/(1+exp(b0+b1*x1+b2*x2+b3*x1*x2))
prob4.4 <- ~1-1/(1+exp(b0 + b1 * x1 + b2 * x2 + b3 * x1 * x2))
predvars4.4 <- c("x1", "x2")
parvars4.4 <- c("b0", "b1", "b2", "b3")
lb <- c(-1, -1)
ub <- c(1, 1)
xopt <- c(-1, -0.389, 1, 0.802, -1, 1, -1, 1)
wopt <- c(0.198, 0.618, 0.084, 0.1)
senslocallycomp(formula = formula4.4, predvars = predvars4.4, parvars = parvars4.4,
family = binomial(), prob = prob4.4, lx = lb, ux = ub,
alpha = .25, inipars = p, x = xopt, w = wopt)
\dontrun{
senslocallycomp(formula = formula4.4, predvars = predvars4.4, parvars = parvars4.4,
family = binomial(), prob = prob4.4, lx = lb, ux = ub,
alpha = .3, inipars = p, x = xopt, w = wopt)
senslocallycomp(formula = formula4.4, predvars = predvars4.4, parvars = parvars4.4,
family = binomial(), prob = prob4.4, lx = lb, ux = ub,
alpha = .5, inipars = p, x = xopt, w = wopt)
senslocallycomp(formula = formula4.4, predvars = predvars4.4, parvars = parvars4.4,
family = binomial(), prob = prob4.4, lx = lb, ux = ub,
alpha = .8, inipars = p, x = xopt, w = wopt)
senslocallycomp(formula = formula4.4, predvars = predvars4.4, parvars = parvars4.4,
family = binomial(), prob = prob4.4, lx = lb, ux = ub,
alpha = .9, inipars = p, x = xopt, w = wopt)
}
|
sim.rateshift.taxa <-function(n,numbsim,lambda,mu,frac,times,complete=TRUE,K=0,norm=TRUE){
out<-lapply(1:numbsim,sim.rateshift.taxa.help,n=n,lambda=lambda,mu=mu,frac=frac,times=times,complete=complete,K=K,norm=norm)
out1<-lapply(out,function(x){ x[[1]][[1]]})
out2<-sapply(out,function(x){ x[[2]]})
for (i in 1:length(out1)){
out1[[i]]$root.edge<-out2[i]-max(getx(out1[[i]],sersampling=1)[,1])
}
out1
}
|
test.efdr <- function(Z,wf = "la8",J=2, alpha=0.05,n.hyp=100,b=11,iteration = 200, parallel = 1L)
{
.check_args(Z = Z,wf = wf,J = J,alpha = alpha,n.hyp = n.hyp,b = b,iteration = iteration,parallel = parallel)
cat("Starting EFDR ...",sep="\n")
cat("... Finding neighbours ...",sep="\n")
nei <- nei.efdr(Z,wf = wf,J=J,b=b, parallel = parallel)
cat("... Estimating the EFDR optimal number of tests ...",sep="\n")
if(length(n.hyp)>1) {
nhat <- .gdf(Z,n.hyp=n.hyp,iteration=iteration,nei=nei,parallel = parallel)$nhat
} else {
nhat = n.hyp
}
test.efdr.base(Z, wf=wf,J=J, alpha = alpha, n.hyp = nhat, b=b, nei=nei)
}
test.efdr.base <- function(Z,wf = "la8",J=2, alpha=0.05,n.hyp=100,b=11,nei = NULL, parallel = 1L)
{
.check_args(Z = Z,wf = wf,J = J,alpha = alpha,n.hyp = n.hyp,b = b,nei = nei, parallel = parallel)
dwt.z <- dwt.2d(x=Z,wf=wf,J=J)
std.dwt.z <- .std.wav.coeff(dwt.z)
pvalue <- .p.values(std.dwt.z)
if(is.null(nei))
nei <- nei.efdr(std.dwt.z,b=b, parallel = parallel)
weight <- .weights.efdr3(std.dwt.z,nei,b=b)
weight_unlist <- unlist(weight)
index <- rev(order(weight_unlist))[1:n.hyp]
pvalue_unlist <- unlist(pvalue)
pvalue_order1 <- order(pvalue_unlist)
pvalue_order2 <- intersect(pvalue_order1,index)
pvalue_ordered2 <- pvalue_unlist[pvalue_order2]
below_th <- as.vector(which(pvalue_ordered2 <= alpha*(1:n.hyp)/n.hyp))
if(length(below_th > 0)) {
reject <- 1:max(below_th)
} else {
reject <- NULL
}
keep_coeff <- pvalue_order2[reject]
test <- list()
test$filtered <- .reconstruct.wt(dwt.z,keep = keep_coeff)
test$Z <- idwt.2d(test$filtered)
test$reject_coeff <- keep_coeff
test$pvalue_ordered <- pvalue_ordered2
test$n.hyp <- n.hyp
test
}
test.fdr <- function(Z,wf = "la8",J=2,alpha=0.05)
{
.check_args(Z = Z,wf = wf,J = J,alpha = alpha)
dwt.z <- dwt.2d(x=Z,wf=wf,J=J)
std.dwt.z <- .std.wav.coeff(dwt.z)
pvalue <- .p.values(std.dwt.z)
pvalue_unlist <- unlist(pvalue)
pvalue_order1 <- order(pvalue_unlist)
pvalue_ordered1 <- pvalue_unlist[pvalue_order1]
n <- length(pvalue_ordered1)
below_th <- which(pvalue_ordered1 <= alpha*(1:n)/n)
if(length(below_th > 0)) {
reject <- 1:max(below_th)
} else {
reject <- NULL
}
keep_coeff <- pvalue_order1[reject]
test <- list()
test$filtered <- .reconstruct.wt(dwt.z,keep = keep_coeff)
test$Z <- idwt.2d(test$filtered)
test$reject_coeff <- keep_coeff
test$pvalue_ordered <- pvalue_ordered1
test$n.hyp <- n
test
}
test.bonferroni <- function(Z,wf="la8",J=2,alpha=0.05)
{
.check_args(Z = Z,wf = wf,J = J,alpha = alpha)
dwt.z <- dwt.2d(x=Z,wf=wf,J=J)
std.dwt.z <- .std.wav.coeff(dwt.z)
pvalue <- .p.values(std.dwt.z)
pvalue_unlist <- unlist(pvalue)
pvalue_order1 <- order(pvalue_unlist)
pvalue_ordered1 <- pvalue_unlist[pvalue_order1]
n <- length(pvalue_ordered1)
reject <- as.vector(which(pvalue_ordered1 <= alpha/n))
keep_coeff <- pvalue_order1[reject]
test <- list()
test$filtered <- .reconstruct.wt(dwt.z,keep = keep_coeff)
test$Z <- idwt.2d(test$filtered)
test$reject_coeff <- keep_coeff
test$pvalue_ordered <- pvalue_ordered1
test$n.hyp <- n
test
}
test.los <- function(Z,wf="la8",J=2,alpha=0.05)
{
.check_args(Z = Z,wf = wf,J = J,alpha = alpha)
dwt.z <- dwt.2d(x=Z,wf=wf,J=J)
std.dwt.z <- .std.wav.coeff(dwt.z)
pvalue <- .p.values(std.dwt.z)
pvalue_unlist <- unlist(pvalue)
pvalue_order1 <- order(pvalue_unlist)
pvalue_ordered1 <- pvalue_unlist[pvalue_order1]
n <- length(pvalue_ordered1)
reject <- sum(pvalue_ordered1[1] < (1 - (1-alpha)^(1/n)))
keep_coeff <- pvalue_order1[reject]
test <- list()
test$filtered <- .reconstruct.wt(dwt.z,keep = keep_coeff)
test$Z <- idwt.2d(test$filtered)
test$reject_coeff <- keep_coeff
test$n.hyp <- n
test
}
test_image <- function(h=1,r=10,n1 = 64, n2=64) {
stopifnot(is.numeric(h))
stopifnot(is.numeric(r))
stopifnot(is.numeric(n1))
stopifnot(is.numeric(n2))
stopifnot(r > 0 & r < min(n1,n2))
stopifnot(n1 > 0)
stopifnot(n2 > 0)
stopifnot(.IsPowerOfTwo(n1))
stopifnot(.IsPowerOfTwo(n2))
signal=matrix(0,n1,n2)
cutoff1 <- (n1/2) - 0.5
cutoff2 <- (n2/2) - 0.5
signal.grid <- expand.grid(-cutoff1:cutoff1,-cutoff2:cutoff2)
distances <- matrix(apply(signal.grid,1,function(x) sqrt(x[1]^2 + x[2]^2)),n1,n2)
signal[distances < r] <- h
return(list(z = signal, grid = as.matrix(signal.grid)))
}
wav_th <- function(Z, wf = "la8", J = 2, th = 1) {
stopifnot(is.numeric(th))
.check_args(Z = Z,wf = wf,J = J)
zcoeff <- dwt.2d(x=Z,wf=wf,J=J) %>%
unlist()
as.numeric(which(abs(zcoeff) >= th))
}
df.to.mat <- function(df) {
stopifnot(is.data.frame(df))
stopifnot(ncol(df) == 3)
stopifnot("x" %in% names(df))
stopifnot("y" %in% names(df))
stopifnot("z" %in% names(df))
x <- y <- z <- NULL
if(!(length(unique(df$x)) * length(unique(df$y)) == nrow(df)))
stop("Data frame needs to be in long format x-y-z with x and y being the output of expand.grid(x0,y0),
where x0 and y0 are the x and y grid points")
x0 = unique(df$x)
y0 = unique(df$y)
df_check <- expand.grid(x0,y0)
names(df_check) <- c("x","y")
if(nrow(merge(df,df_check)) < nrow(df))
stop("Data frame needs to be in long format x-y-z with x and y being the output of expand.grid(x0,y0),
where x0 and y0 are the x and y grid points")
spread(df,key = x,value=z) %>%
select(-y) %>%
as.matrix() %>%
t()
}
regrid <- function(df,n1 = 128, n2 = n1, method="idw", idp = 0.5, nmax = 7,model="Exp") {
stopifnot(is.data.frame(df))
stopifnot("x" %in% names(df))
stopifnot("y" %in% names(df))
stopifnot("z" %in% names(df))
stopifnot(is.numeric(n1))
stopifnot(is.numeric(n2))
stopifnot((n1 %% 1 == 0) & n1 > 0 )
stopifnot((n2 %% 1 == 0) & n2 > 0 )
stopifnot(is.numeric(idp))
stopifnot(idp > 0)
stopifnot(is.numeric(nmax))
stopifnot((nmax %% 1 == 0) & nmax > 0 )
stopifnot(method %in% c("idw","median_polish","cond_sim"))
stopifnot(model %in% vgm()$short)
x <- y <- z <- box_x <- box_y <- z.pred <- NULL
xlim=range(df$x)
ylim=range(df$y)
x0 <- seq(xlim[1],xlim[2],,n1+1)
y0 <- seq(ylim[1],ylim[2],,n2+1)
xd <- mean(diff(x0))/2
yd <- mean(diff(y0))/2
df.regrid <- expand.grid(x0[-1] - xd,y0[-1] - yd)
names(df.regrid) <- c("x","y")
if(method == "idw") {
df.regrid <-
gstat(id = "z", formula = z ~ 1, locations = ~ x + y,
data = df, nmax = nmax, set = list(idp = idp)) %>%
predict(df.regrid) %>%
mutate(z = z.pred) %>%
select(x,y,z)
} else if(method == "cond_sim") {
df.spat <- df
coordinates(df.spat) = ~x+y
df.regrid.spat <- df.regrid
coordinates(df.regrid.spat) = ~x+y
start_range <- max(diff(range(df$y)),diff(range(df$x)))/3
image.vgm = variogram(z~1, data=df.spat)
fit = fit.variogram(image.vgm, model = vgm(var(df$z),"Exp",start_range,var(df$z)/10))
df.regrid$z = krige(z~1, df.spat, df.regrid.spat,
model = fit,nmax = nmax, nsim = 1)$sim1
} else if(method == "median_polish") {
x02 <- seq(xlim[1] - diff(xlim)/n1/2,xlim[2] + diff(xlim)/n1/2,,n1+1)
y02 <- seq(ylim[1] - diff(ylim)/n1/2,ylim[2] + diff(ylim)/n2/2,,n2+1)
df.boxed <- df %>%
mutate(box_x = cut(x,x02,labels=F),
box_y = cut(y,y02,labels=F)) %>%
group_by(box_x,box_y) %>%
summarise(z = mean(z)) %>%
data.frame()
Z <- df.regrid %>%
mutate(box_x = cut(x,x02,labels=F),
box_y = cut(y,y02,labels=F)) %>%
left_join(df.boxed,by=c("box_x","box_y")) %>%
select(x,y,z) %>%
df.to.mat()
med_Z <- medpolish(Z,na.rm=T)
if (any(is.na(med_Z$row)) | any(is.na(med_Z$col)))
stop("Grid with chosen size has rows or columns with
no observations. Use method='idw' or a lower resolution.")
med_Z$residuals[which(is.na(Z),arr.ind=T)] <- 0
df.regrid$z <- c(med_Z$overall +
outer(med_Z$row,med_Z$col, "+") +
med_Z$residuals)
}
df.regrid
}
fdrpower <- function(reject.true,reject) {
length(intersect(reject.true,reject)) / length(reject.true)
}
diagnostic.table <- function(reject.true,reject, n) {
TP = length(intersect(reject.true,reject))
FP = length(setdiff(reject.true,reject))
accept.true <- setdiff(1:n,reject.true)
accept <- setdiff(1:n,reject)
TN = length(intersect(accept.true,accept))
FN = length(setdiff(accept.true,accept))
d.table <- matrix(c(TN,FP,FN,TP),2,2)
row.names(d.table) <- c("Diagnostic Negative","Diagnostic Positive")
colnames(d.table) <- c("Real Negative","Real Positive")
d.table
}
nei.efdr <- function(Z,wf="la8",J=2,b=11,parallel=1L) {
.check_args(Z=Z,wf=wf,J=J,b=b,parallel=parallel)
parallel <- 1L
dwt.z <- dwt.2d(x=Z,wf=wf,J=J)
layers <- .flat.pack(dwt.z,b=b)
i <- s1 <-s2 <- j <- NULL
if(parallel > 1L) {
cl <- makeCluster(parallel)
registerDoParallel(cl)
nei <- foreach(i=1 : nrow(layers),.combine = rbind) %dopar% {
x <- layers[i,]
L <- subset(layers, abs(x$s1-s1) < 2.5 & abs(x$s2-s2) < 2.5 & abs(x$j - j) < 2)
L$D1 <- .jmk.dist(x$j,x$m,x$s1,x$s2,L$j,L$m,L$s1,L$s2)
max.set <- L[order(L$D1),][2:(b+1),]
as.numeric(rownames(max.set))
}
row.names(nei) <- NULL
stopCluster(cl)
} else {
nei <- t( apply(layers,1,function(x) {
L <- subset(layers, abs(x['s1']-s1) < 2.5 & abs(x['s2']-s2) < 2.5 & abs(x['j'] - j) < 2)
L$D1 <- .jmk.dist(x['j'],x['m'],x['s1'],x['s2'],L$j,L$m,L$s1,L$s2)
max.set <- L[order(L$D1),][2:(b+1),]
matrix(as.numeric(rownames(max.set)),b,1)
}))
}
nei
}
.IsPowerOfTwo <- function(x) {
(sum(as.integer(intToBits(x))) == 1)
}
.check_args <- function(Z,wf="la8",J=2,alpha = 0.05,n.hyp = 1L,b = 11L,nei = NULL,iteration = 1L,parallel=1L) {
if(!is.matrix(Z)) stop("Z needs to be a matrix")
if(!(.IsPowerOfTwo(ncol(Z))) | !(.IsPowerOfTwo(nrow(Z)))) stop("Z needs to have rows and columns a power of two")
temp <- tryCatch({
wave.filter(wf)
}, error = function(e) {
stop("Invalid filter specification. Refer to waveslim::wave.filter for filter names")
})
if(!((J %% 1 == 0) & J > 0 )) stop("J needs to be an integer greater than zero")
if(!((iteration %% 1 == 0) & iteration > 0 )) stop("iteration needs to be an integer greater than zero")
if(!(alpha > 0 & alpha < 1)) stop("alpha needs to be less than 1 and greater than 0")
if(!(all(n.hyp > 0) & all(n.hyp %% 1 == 0))) stop("n.hyp needs to be an integer vector with all elements greater than zero")
if(any(n.hyp > length(unlist(dwt.2d(Z,wf=wf))))) stop("Every element in n.hyp needs to be smaller
than the number of wavelet coefficients (i.e. smaller than the number of tests available)")
if(!((b %% 1 == 0) & b > 0 )) stop("b needs to be an integer greater than zero")
if(!((parallel %% 1 == 0) & parallel > 0 )) stop("parallel needs to be a positive integer")
if(parallel > detectCores()) stop("parallel needs to be less than the number of available cores")
}
.gdf <- function(Z, wf = "la8", J = 2, alpha = 0.05, n.hyp=c(100,150,200),iteration=200,b=11,nei=NULL,parallel=1L)
{
stopifnot(is.numeric(iteration))
stopifnot(iteration > 1);
iteration <- round(iteration)
.check_args(Z = Z, wf = wf, J = J, n.hyp = n.hyp, b =b, nei = nei, parallel = parallel)
dwt.z <- dwt.2d(x=Z,wf=wf,J=J)
if (is.null(nei)) nei <- nei.efdr(dwt.z,b=b,parallel = parallel)
dwt.z <- .std.wav.coeff(dwt.z)
loss <- n.hyp*0
nz <- length(unlist(dwt.z))
sigma <- 1
tau <- 0.5*sigma
find_loss <- function(i) {
g <- 0
for(j in 1:iteration){
delta <- tau*rnorm(nz)
dwt_unlist <- unlist(dwt.z) + delta
dwt.z.MC <- .relist.dwt(vec = dwt_unlist,x = dwt.z)
dwt.z.MC.cleaned <- test.efdr.base(idwt.2d(dwt.z.MC),wf = wf, J = J, alpha = alpha,
n.hyp = n.hyp[i],b=b,nei=nei, parallel=parallel)$filtered
g <- g+sum(unlist(dwt.z.MC.cleaned)*delta)
}
g <- g/iteration/tau^2
dwt.zhat <- test.efdr.base(idwt.2d(dwt.z),wf = wf, J = J,alpha = alpha,n.hyp = n.hyp[i],b=b,nei=nei)$filtered
sum((unlist(dwt.z)-unlist(dwt.zhat))^2)+2*g*sigma^2
}
if(parallel > 1L) {
cl <- makeCluster(parallel)
registerDoParallel(cl)
loss <- foreach(i = seq_along(n.hyp), .combine=c) %dopar% {
find_loss(i)
}
stopCluster(cl)
} else {
for(i in seq_along(n.hyp)){
loss[i] <- find_loss(i)
}
}
nhat <- n.hyp[order(loss)[1]]
list(nhat=nhat,loss=loss)
}
.lapply.dwt <- function(x,f) {
x2 <- lapply(x, f)
attr(x2, "J") <- attributes(x)$J
attr(x2, "wavelet") <- attributes(x)$wavelet
attr(x2, "boundary") <- attributes(x)$boundary
attr(x2, "class") <- c("dwt.2d")
x2
}
.relist.dwt <- function(vec,x) {
x2 <- relist(vec,as(x,"list"))
attr(x2, "names") <- names(x)
attr(x2, "J") <- attributes(x)$J
attr(x2, "wavelet") <- attributes(x)$wavelet
attr(x2, "boundary") <- attributes(x)$boundary
attr(x2, "class") <- c("dwt.2d")
x2
}
.std.wav.coeff <- function(dwt) {
.lapply.dwt(dwt,function(x) {
std <- mad(x)
x/std
})
}
.p.values <- function(dwt) {
.lapply.dwt(dwt,function(x) {
2*(1-pnorm(abs(x)))
} )
}
.jmk.dist <- function(j,m,k1,k2,j_,m_,s1,s2) {
d1 <- (j > j_) + sqrt((k1 - s1)^2 + (k2 - s2)^2) + 1 - (m == m_)
}
.jmk.sys <- function(dwt) {
M <- 3
J <- (length(dwt)-1)/M
K1 <- nrow(dwt[[1]])
K2 <- ncol(dwt[[1]])
dwt.t <- array(NA,dim=c(J,M+1,K1,K2))
for (j in 1:J)
for(m in 1:M){
dwt.partial <- dwt[[(j-1)*M + m]]
n1 <- K1*2^{-(j-1)}
n2 <- K2*2^{-(j-1)}
dwt.t[j,m,1:n1,1:n2] <- dwt.partial
}
dwt.t[J,M+1,1:n1,1:n2] <- dwt[[J*M + 1]]
dwt.t
}
.flat.pack <- function(dwt,b=11) {
M <- 3
J <- (length(dwt)-1)/M
K1 <- nrow(dwt[[1]])
K2 <- ncol(dwt[[1]])
min_n1 <- K1*2^{-(J-1)}
min_n2 <- K2*2^{-(J-1)}
layers <- list()
for (j in J:1) {
n1 <- K1*2^{-(j-1)}
n2 <- K2*2^{-(j-1)}
s1 <- seq(0,(min_n1+1),,n1+2)[-c(1,(n1+2))]
s2 <- seq(0,(min_n2+1),,n2+2)[-c(1,(n2+2))]
grid_points <- expand.grid(s1,s2)
k_points <- expand.grid(1:n1,1:n2)
layers[[j]] <- data.frame(k1 = k_points[,1], k2 = k_points[,2], s1 = grid_points[,1], s2 = grid_points[,2],m = 1,j=j)
layers_temp <- layers[[j]]
for( m in 2:M) {
layers_temp$m <- m
layers[[j]] <- rbind(layers[[j]],layers_temp)
}
if (j == J) {
layers[[j]] <- rbind(layers[[j]],data.frame(k1 = k_points[,1], k2 = k_points[,2], s1 = grid_points[,1], s2 = grid_points[,2],m = 4,j=j))
}
}
layers <- Reduce("rbind",layers)
}
.weights.efdr3 <- function(dwt,nei,b=11) {
M <- 3
J <- (length(dwt)-1)/M
K1 <- nrow(dwt[[1]])
K2 <- ncol(dwt[[1]])
dwt.t <- .jmk.sys(dwt)
weight <- dwt.t * 0
layers <- .flat.pack(dwt,b=b)
layers$z <- dwt.t[as.matrix(subset(layers,select=c("j","m","k1","k2")))]^2
weight_mat <- matrix(layers$z[c(nei)],nrow = nrow(layers))
layers$weight <- do.call(pmax, data.frame(weight_mat))
weight[cbind(layers$j,layers$m,layers$k1,layers$k2)] <- layers$weight
weight[J,M+1,,] <- 1e10
weight.list <- dwt
for (j in 1:J)
for(m in 1:M){
n1 <- K1*2^{-(j-1)}
n2 <- K2*2^{-(j-1)}
weight.list[[(j-1)*M + m]] <- weight[j,m,1:n1,1:n2]
}
weight.list[[J*M + 1]] <- weight[J,M+1,1:n1,1:n2]
weight.list
}
.reconstruct.wt <- function(dwt,keep) {
z_unlist <- unlist(dwt)
if (length(keep) == 0 ) {
z_unlist <- z_unlist * 0
} else {
z_unlist[-keep] <- 0
}
.relist.dwt(z_unlist,dwt)
}
|
context("test idig_view_records")
rec_uuid = "d4f6974f-a7d6-4bfb-b70c-4c815b516a0b"
test_that("viewing a record returns right information", {
testthat::skip_on_cran()
rec <- idig_view_records(rec_uuid)
expect_that(rec, is_a("list"))
expect_that(rec$uuid, equals(rec_uuid))
expect_that(rec$data, is_a("list"))
expect_that(length(rec$data$id) > 0, is_true())
expect_that(rec$indexTerms, is_a("list"))
expect_that(rec$indexTerms$uuid, equals(rec_uuid))
expect_that(rec$attribution, is_a("list"))
expect_that(length(rec$attribution$data_rights) > 0, is_true())
})
|
pi.dosage<-function(dos,L=NULL){
if(!is.matrix(dos)){
if(class(dos)[[1]]=="bed.matrix") dos<-gaston::as.matrix(dos)
else dos <- as.matrix(dos)
}
lims <- range(dos, na.rm = TRUE)
if ((lims[2] > 2) | (lims[1] < 0))
stop("input dosage matrix should contains only 0, 1 and 2s")
nis<-nrow(dos)
p<-colMeans(dos,na.rm=TRUE)/2
nas<-colSums(is.na(dos))
pis<-2*p*(1-p)*(2*(nis-nas))/(2*(nis-nas)-1)
if(is.null(L)) sum(pis,na.rm=TRUE) else sum(pis,na.rm=TRUE)/L
}
theta.Watt.dosage<-function(dos,L=NULL){
if(!is.matrix(dos)){
if(class(dos)[[1]]=="bed.matrix") dos<-gaston::as.matrix(dos)
else dos <- as.matrix(dos)
}
lims <- range(dos, na.rm = TRUE)
if ((lims[2] > 2) | (lims[1] < 0))
stop("input dosage matrix should contains only 0, 1 and 2s")
nis<-nrow(dos)
p<-colMeans(dos,na.rm=TRUE)/2
SegSites<-sum(p!=0.0 & p!=1.0,na.rm=TRUE)
a<-sum(1/(1:(nis*2-1)))
if(is.null(L)) SegSites/a else 1/L*SegSites/a
}
TajimaD.dosage<-function(dos){
if(!is.matrix(dos)){
if(class(dos)[[1]]=="bed.matrix") dos<-gaston::as.matrix(dos)
else dos <- as.matrix(dos)
}
lims <- range(dos, na.rm = TRUE)
if ((lims[2] > 2) | (lims[1] < 0))
stop("input dosage matrix should contains only 0, 1 and 2s")
nis<-2*nrow(dos)
p<-colMeans(dos,na.rm=TRUE)/2
Ss<-sum(p>0.0 & p<1.0,na.rm=TRUE)
a<-sum(1/(1:(nis-1)))
a2<-sum(1/((1:(nis-1))^2))
tW<-Ss/a
pis<-pi.dosage(dos)
tmp1<- ((nis+1)/(3*(nis-1))-1/a)/a
num.tmp2<-2*(nis^2+nis+3)/(9*nis*(nis-1))-(nis+2)/(nis*a)+a2/(a^2)
den.tmp2<-a^2+a2
VTD<-tmp1*Ss+num.tmp2/den.tmp2*Ss*(Ss-1)
(pis-tW)/sqrt(VTD)
}
|
context("train")
test_that("train + knn + predict() works", {
skip_on_cran()
skip_if_not_installed("caret")
library(caret)
train_data <- iris[, 1:4]
train_classes <- iris[, 5]
train_fit <- caret::train(train_data,
train_classes,
method = "knn",
preProcess = c("center", "scale"),
tuneLength = 10,
trControl = caret::trainControl(method = "cv"))
x <- axe_call(train_fit)
expect_equal(x$call, rlang::expr(dummy_call()))
expect_equal(x$dots, train_fit$dots)
x <- axe_ctrl(train_fit)
expect_equal(x$control$method, train_fit$control$method)
x <- axe_data(train_fit)
expect_equal(x$trainingData, data.frame(NA))
x <- axe_fitted(train_fit)
expect_equal(x$pred, train_fit$pred)
expect_equal(x, train_fit)
x <- axe_env(train_fit)
expect_null(attr(x$modelInfo$prob, "srcref"))
expect_null(attr(x$modelInfo$sort, "srcref"))
x <- butcher(train_fit)
expect_equal(attr(x, "butcher_disabled"),
c("summary()", "update()"))
test_data <- iris[1:3, 1:4]
expect_equal(predict(x, newdata = test_data),
structure(
c(1L, 1L, 1L),
.Label = c("setosa",
"versicolor",
"virginica"),
class = "factor"))
})
test_that("train + rda + predict() works", {
skip_on_cran()
skip_if_not_installed("caret")
library(caret)
data(cars)
set.seed(123)
train_fit <- suppressWarnings({
train(Price ~ .,
data = cars,
method = "rpart",
trControl = trainControl(method = "cv"))
})
x <- axe_call(train_fit)
expect_equal(x$call, rlang::expr(dummy_call()))
expect_equal(x$dots, train_fit$dots)
x <- axe_ctrl(train_fit)
expect_equal(x$control$method, train_fit$control$method)
x <- axe_data(train_fit)
expect_equal(x$trainingData, data.frame(NA))
x <- axe_fitted(train_fit)
expect_equal(x$pred, train_fit$pred)
expect_equal(x, train_fit)
x <- axe_env(train_fit)
expect_null(attr(x$modelInfo$prob, "srcref"))
expect_null(attr(x$modelInfo$sort, "srcref"))
x <- butcher(train_fit)
expect_equal(attr(x, "butcher_disabled"),
c("summary()", "update()"))
expect_equal(predict(x, cars[1:3, ]),
predict(train_fit, cars[1:3, ]))
})
|
list_channels <- function(token = Sys.getenv("SLACK_TOKEN"), types = "public_channel", exclude_archived = TRUE, ...) {
with_pagination(
function(cursor) {
call_slack_api(
"/api/conversations.list",
.method = GET,
token = token,
types = types,
exclude_archived = exclude_archived,
limit = 1000,
...,
.next_cursor = cursor
)
},
extract = "channels"
)
}
list_users <- function(token = Sys.getenv("SLACK_TOKEN"), ...) {
with_pagination(
function(cursor) {
call_slack_api(
"/api/users.list",
.method = GET,
token = token,
...,
.next_cursor = cursor
)
},
extract = "members"
)
}
post_message <- function(txt,
channel,
emoji = "",
username = Sys.getenv("SLACK_USERNAME"),
token = Sys.getenv("SLACK_TOKEN"),
...) {
r <-
call_slack_api(
"/api/chat.postMessage",
.method = POST,
token = token,
body = list(
text = txt,
channel = channel,
username = username,
link_names = 1,
icon_emoji = emoji,
...
)
)
invisible(content(r))
}
files_upload <- function(file,
channels,
initial_comment = NULL,
token = Sys.getenv("SLACK_TOKEN"),
...) {
r <- call_slack_api(
"/api/files.upload",
.method = POST,
token = token,
body = list(
file = upload_file(file),
initial_comment = initial_comment,
channels = paste(channels, collapse = ","),
...
)
)
invisible(content(r))
}
list_scopes <- function(token = Sys.getenv("SLACK_TOKEN")) {
r <- call_slack_api(
"/api/apps.permissions.scopes.list",
.method = GET,
token = token
)
invisible(content(r))
}
|
knownComp_to_uniform <- function(data, comp.dist, comp.param)
{
stopifnot( (length(comp.dist) == 2) & (length(comp.param) == 2) )
if (is.null(comp.dist[[2]]) | is.null(comp.param[[2]])) stop("Known component must be specified.")
comp.dist.inv <- paste0("p", comp.dist[[2]])
comp.inv <- sapply(X = comp.dist.inv, FUN = get, pos = "package:stats", mode = "function")
assign(x = names(comp.inv)[1], value = comp.inv[[1]])
arg.names <- sapply(X = comp.inv, FUN = methods::formalArgs)
n.arg.user <- length(comp.param[[2]])
if (inherits(arg.names, what = "matrix")) {
arg.names.supplied <- names(comp.param[[2]])
if (is.character(arg.names.supplied)) {
common.args <- match(x = arg.names.supplied, table = arg.names)
} else {
common.args <- apply(sapply(comp.param, names), 2, match, table = arg.names)
}
if (any(is.na(common.args))) stop("Parameters of the mixture components were not correctly specified")
} else {
common.args <- vector(mode = "list", length = length(comp.dist))
for (i in 1:length(comp.dist)) {
if (class(sapply(comp.param, names))[1] != "matrix") {
common.args[[i]] <- sapply(sapply(comp.param, names)[[i]], match, arg.names[[i]])
} else { common.args[[i]] <- match(sapply(comp.param, names)[, i], arg.names[[i]]) }
if (any(is.na(common.args[[i]]))) stop("Parameters of the mixture components were not correctly specified")
}
}
make.expr.inv <- function(z) paste(names(comp.inv)[1],"(q=", z, ",", paste(names(comp.param[[2]]), "=", comp.param[[2]], sep="", collapse=","), ")", sep="")
expr.inv <- parse(text = make.expr.inv(data))
data.transformed <- sapply(expr.inv, eval)
return(data.transformed)
}
|
pn.modselect.step <-
structure(function
(x,
y,
grp,
pn.options,
forcemod = 0,
existing = FALSE,
penaliz = "1/sqrt(n)",
taper.ends = 0.45,
Envir = .GlobalEnv,
...
) {
pcklib<- FPCEnv
if( anyNA(data.frame(x, y, grp)) == TRUE ) stop ("This function does not handle missing data values for x, y, or grp. Please subset the data, e.g. mydataframe[!is.na(mydataframe),], to remove them prior to function call")
options( warn = -1)
if(existing == FALSE) {
rm(list = ls(pattern = "richardsR", envir=FPCEnv), envir=FPCEnv)
rm(list = ls(pattern = "richardsR", envir=Envir), envir=Envir)
} else {
rm(list = ls(pattern = "richardsR", envir=FPCEnv), envir=FPCEnv)
get.mod(modelname = ls(Envir,pattern="richardsR"), from.envir = Envir, to.envir = FPCEnv, write.mod = TRUE, silent = TRUE)
print("
print(paste("NOTE: existing is not set to false, existing models in the working environment ",substitute(Envir)," will be used during fits. To remove models manually, remove all files prefixed richardsR from working environment before running",sep=""))
print("
}
pnoptm=NULL
pnoptnm <- as.character(pn.options)
checkpen <- try(unlist(strsplit(penaliz, "(n)")), silent = TRUE)
if (length(checkpen) != 2 | class(checkpen)[1] == "try-error") {
stop("penaliz parameter is ill defined: see ?pn.mod.compare")
} else {
checkpen <- try(eval(parse(text = sprintf("%s", paste(checkpen[1],
"1", checkpen[2], sep = "")))))
if (class(checkpen)[1] == "try-error")
stop("penaliz parameter is ill defined: see ?pn.mod.compare")
}
datamerg <- data.frame(x, y, grp)
userdata <- groupedData(y ~ x | grp, outer = ~grp, data = datamerg)
assign("userdata", userdata, envir = Envir)
if(as.character(list(Envir)) != "<environment>") stop ("No such environment")
if(exists("Envir", mode = "environment") == FALSE) stop ("No such environment")
FPCEnv$env <- Envir
testbounds <- 1
testpar <- 1
is.na(testbounds) <- TRUE
is.na(testpar) <- TRUE
testbounds <- try(get(pnoptnm, envir = Envir)[16:32],
silent = T)
testpar <- try(get(pnoptnm, envir = Envir)[1:15],
silent = T)
if (class(testbounds)[1] == "try-error" | class(testpar)[1] ==
"try-error" | is.na(testbounds[1]) == TRUE | is.na(testpar[1]) ==
TRUE)
try({
FPCEnv$mod.sel <- TRUE
modpar(datamerg[,1], datamerg[,2], pn.options = pnoptnm, taper.ends = taper.ends,
verbose=FALSE, Envir = Envir, ...)
options(warn=-1)
try(rm("mod.sel", envir = FPCEnv), silent =T)
options(warn=0)
}, silent = FALSE)
extraF <- try(get("extraF", pos = 1), silent = TRUE)
if (class(extraF)[1] == "try-error") {
stop("cannot find function: extraF - please reload FlexParamCurve")
}
mostreducedmod<-1
print("checking fit of positive section of the curve for variable M*************************************")
richardsR12.lis <- try(FPCEnv$richardsR12.lis,
silent = TRUE)
if (class(richardsR12.lis)[1] == "try-error" | existing ==
FALSE)
richardsR12.lis <- eval(parse(text=sprintf("%s",paste("try(nlsList(y ~ SSposnegRichards(x,
Asym = Asym, K = K, Infl = Infl, M = M, modno = 12, pn.options = ",pnoptnm, "), data = userdata),
silent = TRUE)",sep=""))))
print("checking fit of positive section of the curve for fixed M*************************************")
pnmodelparams <- get(pnoptnm, envir = Envir)[1:15]
change.pnparameters <- try(get("change.pnparameters", pos = 1),
silent = TRUE)
chk <- try(unlist(summary(richardsR12.lis))["RSE"], silent = TRUE)
richardsR20.lis <- try(FPCEnv$richardsR20.lis,
silent = TRUE)
if (class(richardsR20.lis)[1] == "try-error" | existing ==
FALSE)
richardsR20.lis <- eval(parse(text=sprintf("%s",paste("try(nlsList(y ~ SSposnegRichards(x,
Asym = Asym, K = K, Infl = Infl, modno = 20, pn.options = ",pnoptnm,"), data = userdata),
silent = TRUE)",sep=""))))
chk1 <- try(unlist(summary(richardsR20.lis))["RSE"], silent = TRUE)
if ((class(richardsR20.lis)[1]) == "try-error" | class(richardsR20.lis)[[1]] != "nlsList"
| class(chk1)[1] == "try-error") {
print("3 parameter positive richards model failed/not fitted*************************************")
if(forcemod != 3) forcemod = 4
richardsR20.lis <- 1
} else {
FPCEnv$richardsR20.lis <- richardsR20.lis
}
if ((class(richardsR12.lis)[1]) == "try-error" | class(richardsR12.lis)[[1]] != "nlsList"
| class(chk)[1] == "try-error")
{
print("4 parameter positive richards model failed/not fitted*************************************")
if(forcemod != 4) forcemod = 3
richardsR12.lis <- 1
} else {
FPCEnv$richardsR12.lis <- richardsR12.lis
}
currentmodel <- 1
testmod <- try(extraF(richardsR20.lis, richardsR12.lis, warn = F))
if (forcemod == 0) {
if (class(testmod)[1] == "try-error") {
modelsig = 0.1
} else {
modelsig = testmod[4]
if ((testmod[4]) > 0.05 & sqrt(testmod[5]/(testmod[3]-testmod[2])) > sqrt(testmod[6]/testmod[3])) {
currentmodel <- richardsR20.lis
mostreducednm <- substr("richardsR20.lis", 10,
11)
} else {
currentmodel <- richardsR12.lis
mostreducednm <- substr("richardsR12.lis", 10,
11)
}
}
}
mostreducedmod <- currentmodel
if (class(testmod)[1] != "try-error") {
mostreducednm <- substr("richardsR20.lis", 10,
11)
mostreducedmod <- richardsR20.lis
} else {
mostreducednm <- "NONE"
}
if (forcemod == 3)
{
modelsig = 0.1
}
if (forcemod == 4)
{
modelsig = 0.04
}
if (modelsig < 0.05) {
print("Variable M models most appropriate*************************************")
} else {
print("Fixed M models most appropriate*************************************")
}
options(warn = 0)
options(warn = -1)
rankmod <- function(model1 = 1, model2 = 1, model3 = 1, model4 = 1) {
nm <- rep(0, 4)
nm[1] <- (as.character(substitute(model1)))
nm[2] <- (as.character(substitute(model2)))
nm[3] <- (as.character(substitute(model3)))
nm[4] <- (as.character(substitute(model4)))
if (class(model1)[[1]] == "nlsList" & class(model1)[1] !=
"try-error") {
if (is.null(nrow(coef(model1))) == TRUE) {
model1 <- 1
}
}
if (class(model2)[[1]] == "nlsList" & class(model2)[1] !=
"try-error") {
if (is.null(nrow(coef(model2))) == TRUE) {
model2 <- 1
}
}
if (class(model3)[[1]] == "nlsList" & class(model3)[1] !=
"try-error") {
if (is.null(nrow(coef(model3))) == TRUE) {
model3 <- 1
}
}
if (class(model4)[[1]] == "nlsList" & class(model4)[1] !=
"try-error") {
if (is.null(nrow(coef(model4))) == TRUE) {
model4 <- 1
}
}
modrank <- data.frame(modno = c(1, 2, 3, 4), rank = rep(-999,
4))
nomods <- 4
RSEstr <- "RSE"
dfstr <- "df"
usefun <- unlist(strsplit(penaliz, "(n)"))
if (class(model1)[[1]] == "nlsList" & class(model1)[1] !=
"try-error") {
evfun <- parse(text = sprintf("%s", paste("summary(model1)[['",
RSEstr, "']]*(", usefun[1], "(1+sum( summary(model1)[['",
dfstr, "']],na.rm=TRUE)))", usefun[2], sep = "")))
modrank[1, 2] <- eval(evfun)
} else {
nomods = nomods - 1
}
if (class(model2)[[1]] == "nlsList" & class(model2)[1] !=
"try-error") {
evfun <- parse(text = sprintf("%s", paste("summary(model2)[['",
RSEstr, "']]*(", usefun[1], "(1+sum( summary(model2)[['",
dfstr, "']],na.rm=TRUE)))", usefun[2], sep = "")))
modrank[2, 2] <- eval(evfun)
} else {
nomods = nomods - 1
}
if (class(model3)[[1]] == "nlsList" & class(model3)[1] !=
"try-error") {
evfun <- parse(text = sprintf("%s", paste("summary(model3)[['",
RSEstr, "']]*(", usefun[1], "(1+sum( summary(model3)[['",
dfstr, "']],na.rm=TRUE)))", usefun[2], sep = "")))
} else {
nomods = nomods - 1
}
if (class(model4)[[1]] == "nlsList" & class(model4)[1] !=
"try-error") {
evfun <- parse(text = sprintf("%s", paste("summary(model4)[['",
RSEstr, "']]*(", usefun[1], "(1+sum( summary(model4)[['",
dfstr, "']],na.rm=TRUE)))", usefun[2], sep = "")))
modrank[4, 2] <- eval(evfun)
} else {
nomods = nomods - 1
}
if (nomods == 0) {
for (j in 1:4) {
if (modrank[j, 2] == -999 & nm[j] != 1)
print(paste("Model", nm[j], "failed to converge for all individuals",
sep = " "))
}
return(print("*************************************no models to evaluate*************************************"))
} else {
modnmsav = ""
for (j in 1:4) {
if (modrank[j, 2] == -999 & nm[j] != 1)
print(paste("Model", nm[j], "failed to converge for all individuals",
sep = " "))
if (j == 1)
if (modrank[j, 2] != -999)
modnmsav <- nm[j]
if (j > 1)
if (modrank[j, 2] != -999)
modnmsav <- paste(modnmsav, nm[j], sep = " vs. ")
}
print(paste("**************Ranking this step's models (all have same
modnmsav, sep = ""))
modrank <- modrank[modrank[, 2] > -999, ]
modrank <- modrank[order(modrank[, 2], modrank[,
1]), ]
if (modrank[1, 1] == 1) {
model <- model1
submod <- as.character(substitute(model1))
}
if (modrank[1, 1] == 2) {
model <- model2
submod <- as.character(substitute(model2))
}
if (modrank[1, 1] == 3) {
model <- model3
submod <- as.character(substitute(model3))
}
if (modrank[1, 1] == 4) {
model <- model4
submod <- as.character(substitute(model4))
}
submod <- substr(submod, 10, 11)
FPCEnv$tempparam.select <- submod
return(model)
}
}
rncheck <- function(modname, existing = FALSE) {
modname <- as.character(substitute(modname))
FPCEnv$tempmodnm <- modname
modobj <- try(get(as.character(substitute(modname)),envir = pcklib)
, silent = TRUE)
if (class(modobj)[1] == "try-error" | existing == FALSE |
class(modobj)[1] == "NULL") {
outp <- TRUE
} else {
outp <- FALSE
}
return(outp)
}
rncheckfirst <- function(modname, existing = FALSE) {
modname <- as.character(substitute(modname))
modobj <- try(get(as.character(substitute(modname)),envir = pcklib)
, silent = TRUE)
if (class(modobj)[1] == "try-error" | existing == FALSE) {
} else {
return(modobj)
}
}
rnassign <- function() {
modname <- parse(text = sprintf("%s", FPCEnv$tempmodnm))
if (class(eval(modname)[1]) != "try-error") {
chk <- try(unlist(summary(eval(modname)))["RSE"],
silent = TRUE)
if (class(chk)[1] == "try-error") {
return(1)
} else {
modnm <- sprintf("%s", FPCEnv$tempmodnm)
FPCEnv$tempparam.select <- substr(modnm, 10,
11)
assign(modnm,eval(modname), pcklib)
return(eval(modname))
}
} else {
return(1)
}
}
tstmod <- function(modelsub, modelcurrent) {
extraF <- try(get("extraF", pos = 1), silent = TRUE)
if (is.na(extraF(modelsub, modelcurrent, warn = F)[4]) == FALSE) {
if ((extraF(modelsub, modelcurrent, warn = F)[4]) > 0.05 &
sign(extraF(modelsub, modelcurrent, warn = F)[1]) != -1 &
FPCEnv$legitmodel[1] == "legitmodelreset") {
currentmodel <- modelsub
return(currentmodel)
} else {
if(sign(extraF(modelsub, modelcurrent, warn = F)[1]) != -1 &
FPCEnv$legitmodel[1] == "legitmodelreset") {
currentmodel <- modelcurrent
return(currentmodel)
}else{
if (FPCEnv$legitmodel[1] == "legitmodelreset" ) {
currentmodel <- modelsub
return(currentmodel)
} else {
currentmodel <- FPCEnv$legitmodel
return(currentmodel)
}
}
}
} else {
if (FPCEnv$legitmodel[1] == "legitmodelreset") {
currentmodel <- modelcurrent
return(currentmodel)
} else {
currentmodel <- FPCEnv$legitmodel
return(currentmodel)
}
}
}
cnt <- 1
step1submod <- FALSE
step5submod <- FALSE
FPCEnv$tempparam.select <- "NONE"
while (cnt < 6) {
if (modelsig < 0.05) {
if (cnt == 1) {
print("Step 1 of a maximum of 6*********************************************************************")
print("--ASSESSING MODEL: richardsR1.lis --")
richardsR1.lis <- rncheckfirst(richardsR1.lis,
existing = existing)
if (rncheck(richardsR1.lis, existing = existing) ==
TRUE)
richardsR1.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = Rk, Ri = Ri, RM = RM, modno = 1, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
currentmodel <- rnassign()
if (class(currentmodel)[1] != "numeric")
step1submod <- TRUE
}
if (cnt == 2) {
print("Step 2 of a maximum of 6*********************************************************************")
print("--ASSESSING MODEL: richardsR2.lis --")
richardsR2.lis <- rncheckfirst(richardsR2.lis,
existing = existing)
if (rncheck(richardsR2.lis, existing = existing) ==
TRUE)
richardsR2.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = Rk, Ri = Ri, RM = 1, modno = 2, pn.options = ",pnoptnm,"), data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR7.lis --")
richardsR7.lis <- rncheckfirst(richardsR7.lis,
existing = existing)
if (rncheck(richardsR7.lis, existing = existing) ==
TRUE)
richardsR7.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1, Rk = Rk,
Ri = Ri, RM = RM, modno = 7, pn.options = ",pnoptnm,"), data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR6.lis --")
richardsR6.lis <- rncheckfirst(richardsR6.lis,
existing = existing)
if (rncheck(richardsR6.lis, existing = existing) ==
TRUE)
richardsR6.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = Ri, RM = RM, modno = 6, pn.options = ",pnoptnm,"), data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR8.lis --")
richardsR8.lis <- rncheckfirst(richardsR8.lis,
existing = existing)
if (rncheck(richardsR8.lis, existing = existing) ==
TRUE)
richardsR8.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = Rk, Ri = 1, RM = RM, modno = 8, pn.options = ",pnoptnm,"), data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR2.lis, richardsR7.lis,
richardsR6.lis, richardsR8.lis)
step2stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (cnt == 3) {
print("Step 3 of a maximum of 6*********************************************************************")
currentmodID3 <- FPCEnv$tempparam.select
if (currentmodID3 == "NONE")
currentmodID3 = "2."
if (currentmodID3 == "2.") {
print("--ASSESSING MODEL: richardsR14.lis --")
richardsR14.lis <- rncheckfirst(richardsR14.lis,
existing = existing)
if (rncheck(richardsR14.lis, existing = existing) ==
TRUE)
richardsR14.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = Ri, RM = 1, modno = 14, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR13.lis --")
richardsR13.lis <- rncheckfirst(richardsR13.lis,
existing = existing)
if (rncheck(richardsR13.lis, existing = existing) ==
TRUE)
richardsR13.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = Ri, RM = 1, modno = 13, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR15.lis --")
richardsR15.lis <- rncheckfirst(richardsR15.lis,
existing = existing)
if (rncheck(richardsR15.lis, existing = existing) ==
TRUE)
richardsR15.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = Rk, Ri = 1, RM = 1, modno = 15, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR14.lis, richardsR13.lis,
richardsR15.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE")
currentmodID3 = "7."
if (currentmodID3 == "7.") {
print("--ASSESSING MODEL: richardsR14.lis --")
richardsR14.lis <- rncheckfirst(richardsR14.lis,
existing = existing)
if (rncheck(richardsR14.lis, existing = existing) ==
TRUE)
richardsR14.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = Ri, RM = 1, modno = 14, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR3.lis --")
richardsR3.lis <- rncheckfirst(richardsR3.lis,
existing = existing)
if (rncheck(richardsR3.lis, existing = existing) ==
TRUE)
richardsR3.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = RM, modno = 3, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR9.lis --")
richardsR9.lis <- rncheckfirst(richardsR9.lis,
existing = existing)
if (rncheck(richardsR9.lis, existing = existing) ==
TRUE)
richardsR9.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = RM, modno = 9, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR14.lis, richardsR3.lis,
richardsR9.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE")
currentmodID3 = "6."
if (currentmodID3 == "6.") {
print("--ASSESSING MODEL: richardsR13.lis --")
richardsR13.lis <- rncheckfirst(richardsR13.lis,
existing = existing)
if (rncheck(richardsR13.lis, existing = existing) ==
TRUE)
richardsR13.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = Ri, RM = 1, modno = 13, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR3.lis --")
richardsR3.lis <- rncheckfirst(richardsR3.lis,
existing = existing)
if (rncheck(richardsR3.lis, existing = existing) ==
TRUE)
richardsR3.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = RM, modno = 3, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR4.lis --")
richardsR4.lis <- rncheckfirst(richardsR4.lis,
existing = existing)
if (rncheck(richardsR4.lis, existing = existing) ==
TRUE)
richardsR4.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = RM, modno = 4, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR13.lis, richardsR3.lis,
richardsR4.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE")
currentmodID3 = "8."
if (currentmodID3 == "8.") {
print("--ASSESSING MODEL: richardsR15.lis --")
richardsR15.lis <- rncheckfirst(richardsR15.lis,
existing = existing)
if (rncheck(richardsR15.lis, existing = existing) ==
TRUE)
richardsR15.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = Rk, Ri = 1, RM = 1, modno = 15, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR9.lis --")
richardsR9.lis <- rncheckfirst(richardsR9.lis,
existing = existing)
if (rncheck(richardsR9.lis, existing = existing) ==
TRUE)
richardsR9.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = RM, modno = 9, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR4.lis --")
richardsR4.lis <- rncheckfirst(richardsR4.lis,
existing = existing)
if (rncheck(richardsR4.lis, existing = existing) ==
TRUE)
richardsR4.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = RM, modno = 4, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR15.lis, richardsR9.lis,
richardsR4.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
}
if (cnt == 4) {
print("Step 4 of a maximum of 6*********************************************************************")
currentmodID2 <- FPCEnv$tempparam.select
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "2."
currentmodID2 = "14"
}
if (currentmodID3 == "2." & currentmodID2 ==
"14") {
print("--ASSESSING MODEL: richardsR10.lis --")
richardsR10.lis <- rncheckfirst(richardsR10.lis,
existing = existing)
if (rncheck(richardsR10.lis, existing = existing) ==
TRUE)
richardsR10.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 10, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR16.lis --")
richardsR16.lis <- rncheckfirst(richardsR16.lis,
existing = existing)
if (rncheck(richardsR16.lis, existing = existing) ==
TRUE)
richardsR16.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 16, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR10.lis, richardsR16.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "2."
currentmodID2 = "13"
}
if (currentmodID3 == "2." & currentmodID2 ==
"13") {
print("--ASSESSING MODEL: richardsR10.lis --")
richardsR10.lis <- rncheckfirst(richardsR10.lis,
existing = existing)
if (rncheck(richardsR10.lis, existing = existing) ==
TRUE)
richardsR10.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 10, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR11.lis --")
richardsR11.lis <- rncheckfirst(richardsR11.lis,
existing = existing)
if (rncheck(richardsR11.lis, existing = existing) ==
TRUE)
richardsR11.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 11, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR10.lis, richardsR11.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "2."
currentmodID2 = "15"
}
if (currentmodID3 == "2." & currentmodID2 ==
"15") {
print("--ASSESSING MODEL: richardsR16.lis --")
richardsR16.lis <- rncheckfirst(richardsR16.lis,
existing = existing)
if (rncheck(richardsR16.lis, existing = existing) ==
TRUE)
richardsR16.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 16, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR11.lis --")
richardsR11.lis <- rncheckfirst(richardsR11.lis,
existing = existing)
if (rncheck(richardsR11.lis, existing = existing) ==
TRUE)
richardsR11.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 11, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR16.lis, richardsR11.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "7."
currentmodID2 = "14"
}
if (currentmodID3 == "7." & currentmodID2 ==
"14") {
print("--ASSESSING MODEL: richardsR10.lis --")
richardsR10.lis <- rncheckfirst(richardsR10.lis,
existing = existing)
if (rncheck(richardsR10.lis, existing = existing) ==
TRUE)
richardsR10.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 10, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR16.lis --")
richardsR16.lis <- rncheckfirst(richardsR16.lis,
existing = existing)
if (rncheck(richardsR16.lis, existing = existing) ==
TRUE)
richardsR16.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 16, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR10.lis, richardsR16.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "7."
currentmodID2 = "3."
}
if (currentmodID3 == "7." & currentmodID2 ==
"3.") {
print("--ASSESSING MODEL: richardsR10.lis --")
richardsR10.lis <- rncheckfirst(richardsR10.lis,
existing = existing)
if (rncheck(richardsR10.lis, existing = existing) ==
TRUE)
richardsR10.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 10, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR5.lis --")
richardsR5.lis <- rncheckfirst(richardsR5.lis,
existing = existing)
if (rncheck(richardsR5.lis, existing = existing) ==
TRUE)
richardsR5.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 5, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR10.lis, richardsR5.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "7."
currentmodID2 = "9."
}
if (currentmodID3 == "7." & currentmodID2 ==
"9.") {
print("--ASSESSING MODEL: richardsR16.lis --")
richardsR16.lis <- rncheckfirst(richardsR16.lis,
existing = existing)
if (rncheck(richardsR16.lis, existing = existing) ==
TRUE)
richardsR16.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 16, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR5.lis --")
richardsR5.lis <- rncheckfirst(richardsR5.lis,
existing = existing)
if (rncheck(richardsR5.lis, existing = existing) ==
TRUE)
richardsR5.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 5, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR16.lis, richardsR5.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "6."
currentmodID2 = "13"
}
if (currentmodID3 == "6." & currentmodID2 ==
"13") {
print("--ASSESSING MODEL: richardsR10.lis --")
richardsR10.lis <- rncheckfirst(richardsR10.lis,
existing = existing)
if (rncheck(richardsR10.lis, existing = existing) ==
TRUE)
richardsR10.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 10, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR11.lis --")
richardsR11.lis <- rncheckfirst(richardsR11.lis,
existing = existing)
if (rncheck(richardsR11.lis, existing = existing) ==
TRUE)
richardsR11.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 11, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR10.lis, richardsR11.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "6."
currentmodID2 = "3."
}
if (currentmodID3 == "6." & currentmodID2 ==
"3.") {
print("--ASSESSING MODEL: richardsR10.lis --")
richardsR10.lis <- rncheckfirst(richardsR10.lis,
existing = existing)
if (rncheck(richardsR10.lis, existing = existing) ==
TRUE)
richardsR10.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 10, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR5.lis --")
richardsR5.lis <- rncheckfirst(richardsR5.lis,
existing = existing)
if (rncheck(richardsR5.lis, existing = existing) ==
TRUE)
richardsR5.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 5, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR10.lis, richardsR5.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "6."
currentmodID2 = "4."
}
if (currentmodID3 == "6." & currentmodID2 ==
"4.") {
print("--ASSESSING MODEL: richardsR11.lis --")
richardsR11.lis <- rncheckfirst(richardsR11.lis,
existing = existing)
if (rncheck(richardsR11.lis, existing = existing) ==
TRUE)
richardsR11.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 11, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR5.lis --")
richardsR5.lis <- rncheckfirst(richardsR5.lis,
existing = existing)
if (rncheck(richardsR5.lis, existing = existing) ==
TRUE)
richardsR5.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 5, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR11.lis, richardsR5.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "8."
currentmodID2 = "15"
}
if (currentmodID3 == "8." & currentmodID2 ==
"15") {
print("--ASSESSING MODEL: richardsR16.lis --")
richardsR16.lis <- rncheckfirst(richardsR16.lis,
existing = existing)
if (rncheck(richardsR16.lis, existing = existing) ==
TRUE)
richardsR16.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 16, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR11.lis --")
richardsR11.lis <- rncheckfirst(richardsR11.lis,
existing = existing)
if (rncheck(richardsR11.lis, existing = existing) ==
TRUE)
richardsR11.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 11, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR16.lis, richardsR11.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "8."
currentmodID2 = "9."
}
if (currentmodID3 == "8." & currentmodID2 ==
"9.") {
print("--ASSESSING MODEL: richardsR16.lis --")
richardsR16.lis <- rncheckfirst(richardsR16.lis,
existing = existing)
if (rncheck(richardsR16.lis, existing = existing) ==
TRUE)
richardsR16.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 16, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR5.lis --")
richardsR5.lis <- rncheckfirst(richardsR5.lis,
existing = existing)
if (rncheck(richardsR5.lis, existing = existing) ==
TRUE)
richardsR5.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 5, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR16.lis, richardsR5.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "8."
currentmodID2 = "4."
}
if (currentmodID3 == "8." & currentmodID2 ==
"4.") {
print("--ASSESSING MODEL: richardsR11.lis --")
richardsR11.lis <- rncheckfirst(richardsR11.lis,
existing = existing)
if (rncheck(richardsR11.lis, existing = existing) ==
TRUE)
richardsR11.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 11, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR5.lis --")
richardsR5.lis <- rncheckfirst(richardsR5.lis,
existing = existing)
if (rncheck(richardsR5.lis, existing = existing) ==
TRUE)
richardsR5.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 5, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR11.lis, richardsR5.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
}
if (cnt == 5) {
print("Step 5 of 6*********************************************************************")
currentmodID1 <- FPCEnv$tempparam.select
print("--ASSESSING MODEL: richardsR12.lis --")
richardsR12.lis <- rncheckfirst(richardsR12.lis,
existing = existing)
if (rncheck(richardsR12.lis, existing = existing) ==
TRUE)
richardsR12.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = M, RAsym = 1, Rk = 1,
Ri = 1, RM = 1, modno = 12, pn.options = ",pnoptnm,"), data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
step5stat <- extraF(richardsR12.lis, currentmodel, warn = F)
step5submod <- TRUE
currentmodel <- tstmod(richardsR12.lis, currentmodel)
}
cnt <- cnt + 1
print("4 param")
print(cnt)
} else {
if (cnt == 1) {
print("Step 1 of a maximum of 6*********************************************************************")
print("--ASSESSING MODEL: richardsR21.lis --")
richardsR21.lis <- rncheckfirst(richardsR21.lis,
existing = existing)
if (rncheck(richardsR21.lis, existing = existing) ==
TRUE)
richardsR21.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = Rk, Ri = Ri, RM = RM, modno = 21, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
currentmodel <- rnassign()
if (class(currentmodel)[1] != "numeric")
step1submod <- TRUE
}
if (cnt == 2) {
print("Step 2 of a maximum of 6*********************************************************************")
print("--ASSESSING MODEL: richardsR22.lis --")
richardsR22.lis <- rncheckfirst(richardsR22.lis,
existing = existing)
if (rncheck(richardsR22.lis, existing = existing) ==
TRUE)
richardsR22.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = Rk, Ri = Ri, RM = 1, modno = 22, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR27.lis --")
richardsR27.lis <- rncheckfirst(richardsR27.lis,
existing = existing)
if (rncheck(richardsR27.lis, existing = existing) ==
TRUE)
richardsR27.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1, Rk = Rk,
Ri = Ri, RM = RM, modno = 27, pn.options = ",pnoptnm,"), data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR26.lis --")
richardsR26.lis <- rncheckfirst(richardsR26.lis,
existing = existing)
if (rncheck(richardsR26.lis, existing = existing) ==
TRUE)
richardsR26.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = Ri, RM = RM, modno = 26, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR28.lis --")
richardsR28.lis <- rncheckfirst(richardsR28.lis,
existing = existing)
if (rncheck(richardsR28.lis, existing = existing) ==
TRUE)
richardsR28.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = Rk, Ri = 1, RM = RM, modno = 28, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR22.lis, richardsR27.lis,
richardsR26.lis, richardsR28.lis)
step2stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (cnt == 3) {
print("Step 3 of a maximum of 6*********************************************************************")
currentmodID3 <- FPCEnv$tempparam.select
if (currentmodID3 == "NONE")
currentmodID3 = "22"
if (currentmodID3 == "22") {
print("--ASSESSING MODEL: richardsR34.lis --")
richardsR34.lis <- rncheckfirst(richardsR34.lis,
existing = existing)
if (rncheck(richardsR34.lis, existing = existing) ==
TRUE)
richardsR34.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = Ri, RM = 1, modno = 34, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR33.lis --")
richardsR33.lis <- rncheckfirst(richardsR33.lis,
existing = existing)
if (rncheck(richardsR33.lis, existing = existing) ==
TRUE)
richardsR33.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = Ri, RM = 1, modno = 33, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR35.lis --")
richardsR35.lis <- rncheckfirst(richardsR35.lis,
existing = existing)
if (rncheck(richardsR35.lis, existing = existing) ==
TRUE)
richardsR35.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = Rk, Ri = 1, RM = 1, modno = 35, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR34.lis, richardsR33.lis,
richardsR35.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE")
currentmodID3 = "27"
if (currentmodID3 == "27") {
print("--ASSESSING MODEL: richardsR34.lis --")
richardsR34.lis <- rncheckfirst(richardsR34.lis,
existing = existing)
if (rncheck(richardsR34.lis, existing = existing) ==
TRUE)
richardsR34.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = Ri, RM = 1, modno = 34, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR23.lis --")
richardsR23.lis <- rncheckfirst(richardsR23.lis,
existing = existing)
if (rncheck(richardsR23.lis, existing = existing) ==
TRUE)
richardsR23.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = RM, modno = 23, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR29.lis --")
richardsR29.lis <- rncheckfirst(richardsR29.lis,
existing = existing)
if (rncheck(richardsR29.lis, existing = existing) ==
TRUE)
richardsR29.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = RM, modno = 29, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR34.lis, richardsR23.lis,
richardsR29.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE")
currentmodID3 = "26"
if (currentmodID3 == "26") {
print("--ASSESSING MODEL: richardsR33.lis --")
richardsR33.lis <- rncheckfirst(richardsR33.lis,
existing = existing)
if (rncheck(richardsR33.lis, existing = existing) ==
TRUE)
richardsR33.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = Ri, RM = 1, modno = 33, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR23.lis --")
richardsR23.lis <- rncheckfirst(richardsR23.lis,
existing = existing)
if (rncheck(richardsR23.lis, existing = existing) ==
TRUE)
richardsR23.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = RM, modno = 23, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR24.lis --")
richardsR24.lis <- rncheckfirst(richardsR24.lis,
existing = existing)
if (rncheck(richardsR24.lis, existing = existing) ==
TRUE)
richardsR24.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = RM, modno = 24, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR33.lis, richardsR23.lis,
richardsR24.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE")
currentmodID3 = "28"
if (currentmodID3 == "28") {
print("--ASSESSING MODEL: richardsR35.lis --")
richardsR35.lis <- rncheckfirst(richardsR35.lis,
existing = existing)
if (rncheck(richardsR35.lis, existing = existing) ==
TRUE)
richardsR35.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = Rk, Ri = 1, RM = 1, modno = 35, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR29.lis --")
richardsR29.lis <- rncheckfirst(richardsR29.lis,
existing = existing)
if (rncheck(richardsR29.lis, existing = existing) ==
TRUE)
richardsR29.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = RM, modno = 29, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR24.lis --")
richardsR24.lis <- rncheckfirst(richardsR24.lis,
existing = existing)
if (rncheck(richardsR24.lis, existing = existing) ==
TRUE)
richardsR24.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = RM, modno = 24, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR35.lis, richardsR29.lis,
richardsR24.lis)
step3stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
}
if (cnt == 4) {
print("Step 4 of a maximum of 6*********************************************************************")
currentmodID2 <- FPCEnv$tempparam.select
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "22"
currentmodID2 = "34"
}
if (currentmodID3 == "22" & currentmodID2 ==
"34") {
print("--ASSESSING MODEL: richardsR30.lis --")
richardsR30.lis <- rncheckfirst(richardsR30.lis,
existing = existing)
if (rncheck(richardsR30.lis, existing = existing) ==
TRUE)
richardsR30.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 30, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR36.lis --")
richardsR36.lis <- rncheckfirst(richardsR36.lis,
existing = existing)
if (rncheck(richardsR36.lis, existing = existing) ==
TRUE)
richardsR36.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 36, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR30.lis, richardsR36.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "22"
currentmodID2 = "33"
}
if (currentmodID3 == "22" & currentmodID2 ==
"33") {
print("--ASSESSING MODEL: richardsR33.lis --")
richardsR30.lis <- rncheckfirst(richardsR30.lis,
existing = existing)
if (rncheck(richardsR30.lis, existing = existing) ==
TRUE)
richardsR30.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 30, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR31.lis --")
richardsR31.lis <- rncheckfirst(richardsR31.lis,
existing = existing)
if (rncheck(richardsR31.lis, existing = existing) ==
TRUE)
richardsR31.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 31, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR30.lis, richardsR31.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "22"
currentmodID2 = "35"
}
if (currentmodID3 == "22" & currentmodID2 ==
"35") {
print("--ASSESSING MODEL: richardsR36.lis --")
richardsR36.lis <- rncheckfirst(richardsR36.lis,
existing = existing)
if (rncheck(richardsR36.lis, existing = existing) ==
TRUE)
richardsR36.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 36, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR31.lis --")
richardsR31.lis <- rncheckfirst(richardsR31.lis,
existing = existing)
if (rncheck(richardsR31.lis, existing = existing) ==
TRUE)
richardsR31.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 31, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR36.lis, richardsR31.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "27"
currentmodID2 = "34"
}
if (currentmodID3 == "27" & currentmodID2 ==
"34") {
print("--ASSESSING MODEL: richardsR30.lis --")
richardsR30.lis <- rncheckfirst(richardsR30.lis,
existing = existing)
if (rncheck(richardsR30.lis, existing = existing) ==
TRUE)
richardsR30.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 30, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR36.lis --")
richardsR36.lis <- rncheckfirst(richardsR36.lis,
existing = existing)
if (rncheck(richardsR36.lis, existing = existing) ==
TRUE)
richardsR36.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 36, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR30.lis, richardsR36.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "27"
currentmodID2 = "23"
}
if (currentmodID3 == "27" & currentmodID2 ==
"23") {
print("--ASSESSING MODEL: richardsR30.lis --")
richardsR30.lis <- rncheckfirst(richardsR30.lis,
existing = existing)
if (rncheck(richardsR30.lis, existing = existing) ==
TRUE)
richardsR30.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 30, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR25.lis --")
richardsR25.lis <- rncheckfirst(richardsR25.lis,
existing = existing)
if (rncheck(richardsR25.lis, existing = existing) ==
TRUE)
richardsR25.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 25, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR30.lis, richardsR25.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "27"
currentmodID2 = "29"
}
if (currentmodID3 == "27" & currentmodID2 ==
"29") {
print("--ASSESSING MODEL: richardsR36.lis --")
richardsR36.lis <- rncheckfirst(richardsR36.lis,
existing = existing)
if (rncheck(richardsR36.lis, existing = existing) ==
TRUE)
richardsR36.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 36, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR25.lis --")
richardsR25.lis <- rncheckfirst(richardsR25.lis,
existing = existing)
if (rncheck(richardsR25.lis, existing = existing) ==
TRUE)
richardsR25.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 25, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR36.lis, richardsR25.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "26"
currentmodID2 = "33"
}
if (currentmodID3 == "26" & currentmodID2 ==
"33") {
print("--ASSESSING MODEL: richardsR33.lis --")
richardsR30.lis <- rncheckfirst(richardsR30.lis,
existing = existing)
if (rncheck(richardsR30.lis, existing = existing) ==
TRUE)
richardsR30.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 30, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR31.lis --")
richardsR31.lis <- rncheckfirst(richardsR31.lis,
existing = existing)
if (rncheck(richardsR31.lis, existing = existing) ==
TRUE)
richardsR31.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 31, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR30.lis, richardsR31.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "26"
currentmodID2 = "23"
}
if (currentmodID3 == "26" & currentmodID2 ==
"23") {
print("--ASSESSING MODEL: richardsR30.lis --")
richardsR30.lis <- rncheckfirst(richardsR30.lis,
existing = existing)
if (rncheck(richardsR30.lis, existing = existing) ==
TRUE)
richardsR30.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = Ri, RM = 1, modno = 30, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR25.lis --")
richardsR25.lis <- rncheckfirst(richardsR25.lis,
existing = existing)
if (rncheck(richardsR25.lis, existing = existing) ==
TRUE)
richardsR25.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 25, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR30.lis, richardsR25.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "26"
currentmodID2 = "24"
}
if (currentmodID3 == "26" & currentmodID2 ==
"24") {
print("--ASSESSING MODEL: richardsR31.lis --")
richardsR31.lis <- rncheckfirst(richardsR31.lis,
existing = existing)
if (rncheck(richardsR31.lis, existing = existing) ==
TRUE)
richardsR31.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 31, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR25.lis --")
richardsR25.lis <- rncheckfirst(richardsR25.lis,
existing = existing)
if (rncheck(richardsR25.lis, existing = existing) ==
TRUE)
richardsR25.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 25, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR31.lis, richardsR25.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "28"
currentmodID2 = "35"
}
if (currentmodID3 == "28" & currentmodID2 ==
"35") {
print("--ASSESSING MODEL: richardsR36.lis --")
richardsR36.lis <- rncheckfirst(richardsR36.lis,
existing = existing)
if (rncheck(richardsR36.lis, existing = existing) ==
TRUE)
richardsR36.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 36, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR31.lis --")
richardsR31.lis <- rncheckfirst(richardsR31.lis,
existing = existing)
if (rncheck(richardsR31.lis, existing = existing) ==
TRUE)
richardsR31.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 31, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR36.lis, richardsR31.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "28"
currentmodID2 = "29"
}
if (currentmodID3 == "28" & currentmodID2 ==
"29") {
print("--ASSESSING MODEL: richardsR36.lis --")
richardsR36.lis <- rncheckfirst(richardsR36.lis,
existing = existing)
if (rncheck(richardsR36.lis, existing = existing) ==
TRUE)
richardsR36.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = Rk, Ri = 1, RM = 1, modno = 36, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR25.lis --")
richardsR25.lis <- rncheckfirst(richardsR25.lis,
existing = existing)
if (rncheck(richardsR25.lis, existing = existing) ==
TRUE)
richardsR25.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 25, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR36.lis, richardsR25.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
if (currentmodID3 == "NONE" & currentmodID2 ==
"NONE") {
currentmodID3 = "28"
currentmodID2 = "24"
}
if (currentmodID3 == "28" & currentmodID2 ==
"24") {
print("--ASSESSING MODEL: richardsR31.lis --")
richardsR31.lis <- rncheckfirst(richardsR31.lis,
existing = existing)
if (rncheck(richardsR31.lis, existing = existing) ==
TRUE)
richardsR31.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = RAsym,
Rk = 1, Ri = 1, RM = 1, modno = 31, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
print("--ASSESSING MODEL: richardsR25.lis --")
richardsR25.lis <- rncheckfirst(richardsR25.lis,
existing = existing)
if (rncheck(richardsR25.lis, existing = existing) ==
TRUE)
richardsR25.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1,
Rk = 1, Ri = 1, RM = RM, modno = 25, pn.options = ",pnoptnm,"),
data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
submodel <- rankmod(richardsR31.lis, richardsR25.lis)
step4stat <- extraF(submodel, currentmodel, warn = F)
currentmodel <- tstmod(submodel, currentmodel)
}
}
if (cnt == 5) {
print("Step 5 of 6*********************************************************************")
currentmodID1 <- FPCEnv$tempparam.select
print("--ASSESSING MODEL: richardsR32.lis --")
richardsR32.lis <- rncheckfirst(richardsR32.lis,
existing = existing)
if (rncheck(richardsR32.lis, existing = existing) ==
TRUE)
richardsR32.lis <- eval(parse(text=sprintf("%s",paste("try({
nlsList(y ~ SSposnegRichards(x, Asym = Asym,
K = K, Infl = Infl, M = 1, RAsym = 1, Rk = 1,
Ri = 1, RM = 1, modno = 32, pn.options = ",pnoptnm,"), data = userdata, ...)
}, silent = TRUE)",sep=""))))
dump <- rnassign()
step5stat <- extraF(richardsR32.lis, currentmodel)
step5submod <- TRUE
currentmodel <- tstmod(richardsR32.lis, currentmodel)
}
cnt <- cnt + 1
}
}
if (modelsig < 0.05) {
mod1 <- "1."
mod4 <- "12"
} else {
mod1 <- "21"
mod4 <- "32"
}
if (step5submod == FALSE) {
step5submod <- NA
step5stat <- NA
mod4 <- NA
mod5 <- NA
step6stat <- NA
} else {
print("Step 6 of 6*********************************************************************")
if (class (mostreducedmod)[1] != "numeric"){
mod5 <- mostreducednm
step6stat <- extraF(mostreducedmod, currentmodel, warn = F)
currentmodel <- tstmod(mostreducedmod, currentmodel)
} else {
step6stat <- NA
mod5 <- NA
}
}
options(warn = -1)
currentmodID3 <- as.numeric(currentmodID3)
currentmodID2 <- as.numeric(currentmodID2)
currentmodID1 <- as.numeric(currentmodID1)
options(warn = 0)
if(mod1 == "1.") mod1 <- "1"
modnames <- c(paste("richardsR", mod1, ".lis", sep = ""),
paste("richardsR", currentmodID3, ".lis", sep = ""),
paste("richardsR", currentmodID2, ".lis", sep = ""),
paste("richardsR", currentmodID1, ".lis", sep = ""),
paste("richardsR", mod4, ".lis", sep = ""), paste("richardsR",
mod5, ".lis", sep = ""))
modnames[modnames == "richardsRNA.lis"] <- ""
modnames[modnames == "richardsRNONE.lis"] <- ""
stepwisetable <- data.frame(` Best Submodel at Step` = modnames)
testof <- rep("", 6)
xf <- rep(NA, 6)
dfn <- rep(NA, 6)
dfd <- rep(NA, 6)
pval <- rep(NA, 6)
RSSgen <- rep(NA, 6)
RSSsub <- rep(NA, 6)
countfit <- 0
for (i in 1:6) {
if (i > 1) {
if (modnames[i] == "" & modnames[i - 1] == "") {
testof[i] <- "No models converged at this step"
} else {
testof[i] <- paste("| ", modnames[i], " vs ",
modnames[i - 1], " |", sep = "")
}
options(warn = -1)
assessfits <- try( eval(parse(text = sprintf("%s", paste("step",
i, "stat", sep = "")))),silent = TRUE)
if (class(assessfits)[1] != "try-error") countfit <- countfit + 1
if (i == 6 & countfit == 0) stop("No models were successfully fitted. Aborting..... Please check your data or change argument options.")
currstat <- eval(parse(text = sprintf("%s", (paste("step",
i, "stat", sep = "")))))
options(warn = 0)
xf[i] <- round(as.numeric(currstat[1]), 4)
dfn[i] <- as.numeric(currstat[2])
dfd[i] <- as.numeric(currstat[3])
pval[i] <- round(as.numeric(currstat[4]), 8)
RSSgen[i] <- as.numeric(currstat[5])
RSSsub[i] <- as.numeric(currstat[6])
} else {
testof[i] <- "| Reduced model More complex model |"
}
}
modnames1 <- modnames
for (i in 2:6) {
if (is.na(pval[i]) == FALSE) {
if (pval[i] > 0.05 & sign(xf[i]) != -1) {
modnames1[i] <- modnames1[i]
} else {
if (sign(xf[i]) != -1) {
modnames1[i] <- modnames1[i - 1]
} else {
modnames1[i] <- modnames1[i]
}
}
} else {
modnames1[i] <- modnames1[i]
}
}
for (i in 2:6) {
if (modnames[i] == "" & modnames[i - 1] == "") {
testof[i] <- "No models converged at this step"
} else {
if (modnames[i - 1] != modnames1[i - 1])
testof[i] <- paste("| ", modnames[i], " vs ",
modnames1[i - 1], " |", sep = "")
}
}
testof <- unlist(testof)
xf <- data.frame(sprintf("%.4f", as.numeric(unlist(xf))))
dfn <- data.frame(as.numeric(unlist(dfn)))
dfd <- data.frame(as.numeric(unlist(dfd)))
pval <- data.frame(sprintf("%.8f", as.numeric(unlist(pval))))
RSSgen <- data.frame(signif( as.numeric(unlist(RSSgen)),4))
RSSsub <- data.frame(signif( as.numeric(unlist(RSSsub)),4))
stepwisetable <- data.frame(` Best Submodel at Step` = modnames1,
Test = testof, `F-stat` = xf, df_n = dfn, df_d = dfd,
P = pval, RSS_sub = RSSsub, RSS_gen = RSSgen)
names(stepwisetable) <- c(" Best Submodel at Step",
"Test ", "F-stat", "df_n", "df_d", "P",
"RSS_sub", "RSS_gen")
row.names(stepwisetable) <- c("Step 1", "Step 2", "Step 3",
"Step 4", "Step 5", "Step 6")
stepwisetable[is.na(stepwisetable)] <- ""
print("
assign("pn.bestmodel.lis", currentmodel, envir = Envir)
print("writing output to environment:")
print(Envir)
get.mod(to.envir = Envir, write.mod = TRUE)
assign("userdata",userdata, envir = Envir)
options(warn=-1)
try(rm("model1",envir = FPCEnv),silent=T)
try(rm("tempparam.select",envir = FPCEnv),silent=T)
try(rm("tempmodnm",envir = FPCEnv),silent=T)
try(rm("legitmodel",envir = FPCEnv),silent=T)
options( warn = 0)
return(stepwisetable)
}
, ex = function(){
subdata<-subset(posneg.data, as.numeric(row.names (posneg.data) ) < 40)
modseltable <- pn.modselect.step(subdata$age, subdata$mass,
subdata$id, existing = FALSE)
subdata<-subset(posneg.data, as.numeric(row.names (posneg.data) ) < 40)
richardsR22.lis <- nlsList(mass ~ SSposnegRichards(age, Asym = Asym, K = K,
Infl = Infl, M = 1, RAsym = RAsym, Rk = Rk, Ri = Ri, RM = 1 , modno = 22)
,data = subdata)
modseltable <- pn.modselect.step(subdata$age, subdata$mass,
subdata$id, forcemod = 3, existing = TRUE)
modseltable <- pn.modselect.step(subdata$age, subdata$mass,
subdata$id, penaliz='1*(n)', existing = TRUE)
}
)
|
library(rigr)
data(fev)
descrip(fev)
descrip(fev$fev, fev$height)
descrip(fev$fev, fev$height, strata = fev$smoke)
greater_10 <- ifelse(fev$age > 10, 1, 0)
descrip(fev$fev, fev$height, subset = greater_10)
descrip(fev$fev, strata = fev$smoke, above = 2)
|
"investor"
"marketprices"
"portfolio_results"
"portfolio_results_ts"
"DEanalysis"
|
profoundProFound=function(image=NULL, segim=NULL, objects=NULL, mask=NULL, skycut=1, pixcut=3,
tolerance=4, ext=2, reltol=0, cliptol=Inf, sigma=1, smooth=TRUE,
SBlim=NULL, SBdilate=NULL, SBN100=100, size=5, shape='disc', iters=6,
threshold=1.05, magzero=0, gain=NULL, pixscale=1, sky=NULL, skyRMS=NULL,
redosegim=FALSE, redosky=TRUE, redoskysize=21, box=c(100,100), grid=box,
skygrid_type = 'new', type='bicubic', skytype='median', skyRMStype='quanlo', roughpedestal=FALSE,
sigmasel=1, skypixmin=prod(box)/2, boxadd=box/2, boxiters=0, conviters=100, iterskyloc=TRUE,
deblend=FALSE, df=3, radtrunc=2, iterative=FALSE, doclip=TRUE,
shiftloc = FALSE, paddim = TRUE, header=NULL, verbose=FALSE,
plot=FALSE, stats=TRUE, rotstats=FALSE, boundstats=FALSE,
nearstats=boundstats, groupstats=boundstats, group=NULL,
groupby='segim_orig', offset=1, haralickstats=FALSE, sortcol="segID",
decreasing=FALSE, lowmemory=FALSE, keepim=TRUE, watershed='ProFound',
pixelcov=FALSE, deblendtype='fit', psf=NULL, fluxweight='sum',
convtype = 'brute', convmode = 'extended', fluxtype='Raw',
app_diam=1, Ndeblendlim=Inf, ...){
if(verbose){message('Running ProFound:')}
timestart=proc.time()[3]
call=match.call()
if(length(box)==1){
box=rep(box,2)
if(missing(grid)){grid=box}
if(missing(boxadd)){boxadd=box/2}
if(missing(skypixmin)){skypixmin=prod(box)/2}
}
if(length(grid)==1){
grid=rep(grid,2)
}
if(length(boxadd)==1){
boxadd=rep(boxadd,2)
}
fluxtype=tolower(fluxtype)
if(skytype == 'mode' | skytype == 'converge'){
skygrid_type = 'old'
}
if(fluxtype=='raw' | fluxtype=='adu' | fluxtype=='adus'){
if(verbose){message('Using raw flux units')}
fluxscale=1
}else if (fluxtype=='jansky'){
if(verbose){message('Using Jansky flux units (WARNING: magzero must take system to AB)')}
fluxscale=10^(-0.4*(magzero-8.9))
}else if (fluxtype=='microjansky'){
if(verbose){message('Using Micro-Jansky flux units (WARNING: magzero must take system to AB)')}
fluxscale=10^(-0.4*(magzero-23.9))
}else{
stop('fluxtype must be Jansky / Microjansky / Raw!')
}
if(!is.null(image)){
if(any(names(image)=='imDat') & is.null(header)){
if(verbose){message('Supplied image contains image and header components')}
header=image$hdr
image=image$imDat
}else if(any(names(image)=='imDat') & !is.null(header)){
if(verbose){message('Supplied image contains image and header but using specified header')}
image=image$imDat
}
if(any(names(image)=='dat') & is.null(header)){
if(verbose){message('Supplied image contains image and header components')}
header=image$hdr[[1]]
header=data.frame(key=header[,1],value=header[,2], stringsAsFactors = FALSE)
image=image$dat[[1]]
}else if(any(names(image)=='dat') & !is.null(header)){
if(verbose){message('Supplied image contains image and header but using specified header')}
image=image$dat[[1]]
}
if(any(names(image)=='image') & is.null(header)){
if(verbose){message('Supplied image contains image and header components')}
header=image$header
image=image$image
}else if(any(names(image)=='image') & !is.null(header)){
if(verbose){message('Supplied image contains image and header but using specified header')}
image=image$image
}
}else{
stop('Missing image - this is a required input!')
}
if(box[1] > ceiling(dim(image)[1]/3)){
box[1] = ceiling(dim(image)[1]/3)
message('dim(image)[1]/box[1] must be >=3, box[1] modified to ',box[1])
}
if(box[2] > ceiling(dim(image)[1]/3)){
box[2] = ceiling(dim(image)[2]/3)
message('dim(image)[2]/box[2] must be >=3, box[2] modified to ',box[2])
}
if(grid[1] > ceiling(dim(image)[1]/3)){
grid[1] = ceiling(dim(image)[1]/3)
message('dim(image)[1]/grid[1] must be >=3, grid[1] modified to ',grid[1])
}
if(grid[2] > ceiling(dim(image)[1]/3)){
grid[2] = ceiling(dim(image)[2]/3)
message('dim(image)[2]/grid[2] must be >=3, grid[2] modified to ',grid[2])
}
if(verbose){message(paste('Supplied image is',dim(image)[1],'x',dim(image)[2],'pixels'))}
badpix=NULL
if(!is.null(mask)){
mask=mask*1L
if(length(mask)==1 & !is.na(mask[1])){
maskflag=mask
mask=matrix(0L,dim(image)[1],dim(image)[2])
mask[image==maskflag]=1L
}
if(anyNA(image)){
badpix=which(is.na(image))
mask[badpix]=1L
image[badpix]=0
}
if(is.numeric(mask)){
mask=as.integer(mask)
attributes(mask)$dim = dim(image)
}
}else{
if(anyNA(image)){
mask=matrix(0L,dim(image)[1],dim(image)[2])
badpix=which(is.na(image))
mask[badpix]=1L
image[badpix]=0
}
}
if(missing(pixscale) & !is.null(header)){
pixscale=getpixscale(header)
if(verbose){message(paste('Extracted pixel scale from header provided:',signif(pixscale,4),'asec/pixel'))}
}else{
if(verbose){message(paste('Using suggested pixel scale:',signif(pixscale,4),'asec/pixel'))}
}
imarea=prod(dim(image))*pixscale^2/(3600^2)
if(verbose){message(paste('Supplied image is',signif(dim(image)[1]*pixscale/60,4),'x',signif(dim(image)[2]*pixscale/60,4),'amin, ', signif(imarea,4),'deg-sq'))}
if(is.null(objects)){
if(!is.null(segim)){
objects=matrix(0L,dim(segim)[1],dim(segim)[2])
objects[]=as.logical(segim)
}
}else{
objects=objects*1L
}
hassky=!is.null(sky)
hasskyRMS=!is.null(skyRMS)
if((hassky==FALSE | hasskyRMS==FALSE) & is.null(segim)){
if(verbose){message(paste('Making initial sky map -',round(proc.time()[3]-timestart,3),'sec'))}
roughsky=profoundMakeSkyGrid(image=image, objects=objects, mask=mask, box=box,
grid=grid, skygrid_type=skygrid_type, type=type,
skytype=skytype, skyRMStype=skyRMStype, sigmasel=sigmasel,
skypixmin=skypixmin, boxadd=boxadd, boxiters=0, conviters=conviters,
doclip=doclip, shiftloc=shiftloc, paddim=paddim)
if(roughpedestal){
roughsky$sky=median(roughsky$sky,na.rm=TRUE)
roughsky$skyRMS=median(roughsky$skyRMS,na.rm=TRUE)
}
if(hassky==FALSE){
sky=roughsky$sky
if(verbose){message(' - Sky statistics :')}
if(verbose){print(summary(as.numeric(sky)))}
}
if(hasskyRMS==FALSE){
skyRMS=roughsky$skyRMS
if(verbose){message(' - Sky-RMS statistics :')}
if(verbose){print(summary(as.numeric(skyRMS)))}
}
rm(roughsky)
}else{
if(verbose){message("Skipping making initial sky map - User provided sky and sky RMS, or user provided segim")}
}
if(is.null(segim)){
if(verbose){message(paste('Making initial segmentation image -',round(proc.time()[3]-timestart,3),'sec'))}
segim=profoundMakeSegim(image=image, objects=objects, mask=mask, tolerance=tolerance,
ext=ext, reltol=reltol, cliptol=cliptol, sigma=sigma,
smooth=smooth, pixcut=pixcut, skycut=skycut, SBlim=SBlim,
sky=sky, skyRMS=skyRMS, magzero=magzero, pixscale=pixscale,
verbose=verbose, watershed=watershed, plot=FALSE, stats=FALSE)
objects=segim$objects
segim=segim$segim
}else{
redosegim=FALSE
if(verbose){message("Skipping making an initial segmentation image - User provided segim")}
}
if(any(segim>0)){
if((hassky==FALSE | hasskyRMS==FALSE)){
if(redosky){
if(verbose){message(paste('Doing initial aggressive dilation -',round(proc.time()[3]-timestart,3),'sec'))}
objects_redo=profoundMakeSegimDilate(segim=objects, size=redoskysize, shape=shape, sky=sky, verbose=verbose, plot=FALSE, stats=FALSE, rotstats=FALSE)$objects
}else{
objects_redo=objects
}
if(verbose){message(paste('Making better sky map -',round(proc.time()[3]-timestart,3),'sec'))}
if(hassky==FALSE){rm(sky)}
if(hasskyRMS==FALSE){rm(skyRMS)}
bettersky=profoundMakeSkyGrid(image=image, objects=objects_redo, mask=mask,
box=box, skygrid_type=skygrid_type, grid=grid,
type=type, skytype=skytype, skyRMStype=skyRMStype,
sigmasel=sigmasel, skypixmin=skypixmin, boxadd=boxadd,
boxiters=boxiters, conviters=conviters, doclip=doclip, shiftloc=shiftloc,
paddim=paddim)
if(hassky==FALSE){
sky=bettersky$sky
if(verbose){message(' - Sky statistics :')}
if(verbose){print(summary(as.numeric(sky)))}
}
if(hasskyRMS==FALSE){
skyRMS=bettersky$skyRMS
if(verbose){message(' - Sky-RMS statistics :')}
if(verbose){print(summary(as.numeric(skyRMS)))}
}
rm(bettersky)
if(redosegim){
if(verbose){message(paste('Making better segmentation image -',round(proc.time()[3]-timestart,3),'sec'))}
imagescale=(image-sky)/skyRMS
imagescale[!is.finite(imagescale)]=0
if(!is.null(SBlim) & !missing(magzero)){
imagescale[imagescale<skycut | sky<profoundSB2Flux(SBlim, magzero, pixscale)]=0
}else{
imagescale[imagescale<skycut]=0
}
if(!is.null(mask)){
imagescale[mask!=0]=0
}
segim[imagescale==0]=0
objects[segim==0]=0
}
}else{
if(verbose){message("Skipping making better sky map - User provided sky and sky RMS")}
}
if(iters>0 | iterskyloc){
if(verbose){message(paste('Calculating initial segstats -',round(proc.time()[3]-timestart,3),'sec'))}
if(length(sky)>1){
skystats=.profoundFluxCalcMin(image=sky, segim=segim, mask=mask)
skystats=skystats$flux/skystats$N100
skymed=median(skystats, na.rm=TRUE)
}else{
skystats=sky
skymed=sky
}
segstats=.profoundFluxCalcMin(image=image, segim=segim, mask=mask)
segstats$flux = segstats$flux - (skystats * segstats$N100)
origfrac = segstats$flux
segim_orig=segim
expand_segID=segstats[,'segID']
SBlast=rep(Inf,length(expand_segID))
selseg=rep(0,length(expand_segID))
if(is.null(SBdilate)){
SBdilate=Inf
}else{
skyRMSstats = .profoundFluxCalcMin(image=skyRMS, segim=segim, mask=mask)
skyRMSstats = skyRMSstats$flux/skyRMSstats$N100
SBdilate = skyRMSstats * 10^(-0.4*SBdilate)
SBdilate[!is.finite(SBdilate)]=Inf
}
if(verbose){message('Doing dilations:')}
if(iters>0){
for(i in 1:(iters)){
if(verbose){message(paste('Iteration',i,'of',iters,'-',round(proc.time()[3]-timestart,3),'sec'))}
segim_new=profoundMakeSegimDilate(segim=segim, expand=expand_segID, size=size, shape=shape, verbose=verbose, plot=FALSE, stats=FALSE, rotstats=FALSE)$segim
segstats_new=.profoundFluxCalcMin(image=image, segim=segim_new, mask=mask)
segstats_new$flux = segstats_new$flux - (skystats * segstats_new$N100)
N100diff = (segstats_new$N100-segstats$N100)
SBnew=(segstats_new$flux - segstats$flux) / N100diff
fluxgrowthratio = segstats_new$flux / segstats$flux
skyfrac = abs(((skystats-skymed) * N100diff) / (segstats_new$flux - segstats$flux))
expand_segID=segstats[which((fluxgrowthratio > threshold | (SBnew > SBdilate & N100diff>SBN100)) & segstats_new$flux>0 & SBnew < SBlast & skyfrac < 0.5 & selseg==(i-1)),'segID']
expand_segID=expand_segID[is.finite(expand_segID)]
if(length(expand_segID)==0){break}
updateID=which(segstats$segID %in% expand_segID)
selseg[updateID] = i
segstats[updateID,] = segstats_new[updateID,]
SBlast = SBnew
if('fastmatch' %in% .packages()){
selpix = which(fastmatch::fmatch(segim_new, expand_segID, nomatch = 0L) > 0)
}else{
selpix = which(segim_new %in% expand_segID)
}
segim[selpix]=segim_new[selpix]
}
}
if(iterskyloc){
segim_skyloc = profoundMakeSegimDilate(segim=segim, size=size, shape=shape, verbose=verbose, plot=FALSE, stats=FALSE, rotstats=FALSE)$segim
segstats_new = .profoundFluxCalcMin(image=image, segim=segim_skyloc, mask=mask)
segstats$flux = segstats$flux + (skystats * segstats$N100)
skyseg_mean=(segstats_new$flux-segstats$flux)/(segstats_new$N100-segstats$N100)
skyseg_mean[!is.finite(skyseg_mean)]=0
}
objects=matrix(0L,dim(segim)[1],dim(segim)[2])
objects[]=as.logical(segim)
origfrac = origfrac / (segstats$flux - (skystats * segstats$N100))
}else{
if(verbose){message('Iters set to 0 - keeping segim un-dilated')}
segim_orig=segim
selseg=0
origfrac=1
skyseg_mean=NA
}
if(redosky){
if(redoskysize %% 2 == 0){redoskysize=redoskysize+1}
if(verbose){message(paste('Doing final aggressive dilation -',round(proc.time()[3]-timestart,3),'sec'))}
objects_redo=profoundMakeSegimDilate(segim=objects, mask=mask, size=redoskysize, shape=shape, sky=sky, verbose=verbose, plot=FALSE, stats=FALSE, rotstats=FALSE)$objects
if(verbose){message(paste('Making final sky map -',round(proc.time()[3]-timestart,3),'sec'))}
rm(skyRMS)
sky = profoundMakeSkyGrid(image=image, objects=objects_redo, mask=mask, sky=sky, box=box,
skygrid_type=skygrid_type, grid=grid, type=type, skytype=skytype,
skyRMStype=skyRMStype, sigmasel=sigmasel, skypixmin=skypixmin,
boxadd=boxadd, boxiters=boxiters, conviters=conviters, doclip=doclip, shiftloc=shiftloc,
paddim=paddim)$sky
if(verbose){message(paste('Making final sky RMS map -',round(proc.time()[3]-timestart,3),'sec'))}
skyRMS = profoundMakeSkyGrid(image=image, objects=objects_redo, mask=mask, sky=sky, box=box,
skygrid_type=skygrid_type, grid=grid, type=type, skytype=skytype,
skyRMStype=skyRMStype, sigmasel=sigmasel, skypixmin=skypixmin,
boxadd=boxadd, boxiters=boxiters, conviters=conviters, doclip=doclip, shiftloc=shiftloc,
paddim=paddim)$skyRMS
if(verbose){message(' - Sky statistics :')}
if(verbose){print(summary(as.numeric(sky)))}
if(verbose){message(' - Sky-RMS statistics :')}
if(verbose){print(summary(as.numeric(skyRMS)))}
}else{
if(verbose){message("Skipping making final sky map - redosky set to FALSE")}
objects_redo=NULL
}
Norig=tabulate(segim_orig)
if(is.function(pixelcov)){
cor_err_func=pixelcov
}else{
if(pixelcov){
if(verbose){message(paste('Calculating pixel covariance -',round(proc.time()[3]-timestart,3),'sec'))}
cor_err_func=profoundPixelCorrelation(image=image, objects=objects, mask=mask, sky=sky, skyRMS=skyRMS,
fft=FALSE, lag=apply(expand.grid(c(1,2,4),c(1,10,100,1000,1e4)),MARGIN=1,FUN=prod))$cor_err_func
}else{
cor_err_func=NULL
}
}
if(lowmemory){
image=image-sky
sky=0
skyRMS=0
segim_orig=NULL
objects=NULL
objects_redo=NULL
}
if(stats & !is.null(image)){
if(verbose){message(paste('Calculating final segstats for',length(which(tabulate(segim)>0)),'objects -',round(proc.time()[3]-timestart,3),'sec'))}
if(verbose){message(paste(' - magzero =', signif(magzero,4)))}
if(verbose){
if(is.null(gain)){
message(paste(' - gain = NULL (ignored)'))
}else{
message(paste(' - gain =', signif(gain,4)))
}
}
if(verbose){message(paste(' - pixscale =', signif(pixscale,4)))}
if(verbose){message(paste(' - rotstats =', rotstats))}
if(verbose){message(paste(' - boundstats =', boundstats))}
segstats=profoundSegimStats(image=image, segim=segim, mask=mask, sky=sky, skyRMS=skyRMS,
magzero=magzero, gain=gain, pixscale=pixscale, header=header,
sortcol=sortcol, decreasing=decreasing, rotstats=rotstats,
boundstats=boundstats, offset=offset, cor_err_func=cor_err_func,
app_diam=app_diam)
segstats=cbind(segstats, iter=selseg, origfrac=origfrac, Norig=Norig[segstats$segID], skyseg_mean=skyseg_mean)
segstats=cbind(segstats, flag_keep=segstats$origfrac>= median(segstats$origfrac[segstats$iter==iters]) | segstats$iter<iters)
}else{
if(verbose){message("Skipping segmentation statistics - segstats set to FALSE")}
segstats=NULL
}
if(nearstats){
near=profoundSegimNear(segim=segim, offset=offset)
}else{
near=NULL
}
if(deblend){
groupstats=TRUE
}
if(groupstats){
if(verbose){message(paste(' - groupstats = TRUE - ',round(proc.time()[3]-timestart,3),'sec'))}
if(groupby=='segim'){
if(is.null(group)){
group=profoundSegimGroup(segim)
}
}else if(groupby=='segim_orig'){
if(is.null(group)){
group=profoundSegimGroup(segim_orig)
if(any(group$groupsegID$Ngroup>1)){
group$groupim=profoundSegimKeep(segim=segim, segID_merge=group$groupsegID[group$groupsegID$Ngroup>1,'segID'])
group$groupsegID$Npix=tabulate(group$groupim)[group$groupsegID$groupID]
}
}
}else{
stop('Non legal groupby option, must be segim or segim_orig!')
}
if(stats & !is.null(image) & !is.null(group)){
groupstats=profoundSegimStats(image=image, segim=group$groupim, mask=mask,
sky=sky, skyRMS=skyRMS, magzero=magzero, gain=gain,
pixscale=pixscale, header=header, sortcol=sortcol,
decreasing=decreasing, rotstats=rotstats, boundstats=boundstats,
offset=offset, cor_err_func=cor_err_func, app_diam=app_diam)
colnames(groupstats)[1]='groupID'
}else{
groupstats=NULL
}
}else{
if(verbose){message(' - groupstats = FALSE')}
group=NULL
groupstats=NULL
}
if(deblend & stats & !is.null(image) & any(group$groupsegID$Ngroup>1)){
if(verbose){message(paste(' - deblend = TRUE - ',round(proc.time()[3]-timestart,3),'sec'))}
tempblend=profoundFluxDeblend(image=image-sky, segim=segim, segstats=segstats,
groupim=group$groupim, groupsegID=group$groupsegID,
magzero=magzero, df=df, radtrunc=radtrunc, iterative=iterative,
doallstats=TRUE, deblendtype=deblendtype, psf=psf,
fluxweight=fluxweight, convtype=convtype, convmode=convmode,
Ndeblendlim = Ndeblendlim)
if(!is.null(tempblend)){
segstats=cbind(segstats,tempblend[,-2])
}
}else{
if(verbose){message(' - deblend = FALSE')}
}
if(haralickstats){
if(requireNamespace("EBImage", quietly = TRUE)){
scale=10^(0.4*(30-magzero))
temphara=(image-sky)*scale
if(!is.null(mask)){
temphara[mask!=0]=0
}
temphara[!is.finite(temphara)]=0
haralick=as.data.frame(EBImage::computeFeatures.haralick(segim,temphara))
haralick=haralick[segstats$segID,]
}else{
if(verbose){
message('The EBImage package is needed to compute Haralick statistics.')
haralick=NULL
}
}
}else{
haralick=NULL
}
if(plot){
if(verbose){message(paste('Plotting segments -',round(proc.time()[3]-timestart,3),'sec'))}
if(any(is.finite(sky))){
profoundSegimPlot(image=image-sky, segim=segim, mask=mask, header=header, ...)
}else{
profoundSegimPlot(image=image, segim=segim, mask=mask, header=header, ...)
}
}else{
if(verbose){message("Skipping segmentation plot - plot = FALSE")}
}
if(is.null(SBlim)){
SBlim=NULL
}else if(is.numeric(SBlim)){
SBlimtemp=profoundFlux2SB(flux=skyRMS*skycut, magzero=magzero, pixscale=pixscale)
SBlimtemp=matrix(SBlimtemp,dim(skyRMS)[1],dim(skyRMS)[2])
SBlimtemp[which(SBlimtemp>SBlim)]=SBlim
SBlim=SBlimtemp
}else if(SBlim[1]=='get' & skycut> -Inf){
SBlim=profoundFlux2SB(flux=skyRMS*skycut, magzero=magzero, pixscale=pixscale)
}
if(is.null(header)){header=NULL}
if(keepim==FALSE){image=NULL; mask=NULL}
if(is.null(mask)){mask=NULL}
if(!is.null(badpix)){image[badpix]=NA}
row.names(segstats)=NULL
segstats[,grep('flux',colnames(segstats))] = fluxscale*segstats[,grep('flux',colnames(segstats))]
if(stats){
cutsky = (image - sky) / skyRMS
cutsky[!is.finite(cutsky)] = NA
if(!is.null(mask)){
cutsky[mask==1] = NA
}
if(!is.null(objects_redo)){
cutsky[objects_redo == 1] = NA
}else{
cutsky[objects == 1] = NA
}
cutsky = cutsky[which(cutsky<=0)]
if(length(cutsky) > 0){
skyLL = dchisq(sum(cutsky^2, na.rm =TRUE), df=length(cutsky)-1, log=TRUE)
}else{
skyLL = NA
}
}else{
skyLL = NULL
}
if(verbose){message(paste('ProFound is finished! -',round(proc.time()[3]-timestart,3),'sec'))}
output=list(segim=segim, segim_orig=segim_orig, objects=objects, objects_redo=objects_redo,
sky=sky, skyRMS=skyRMS, image=image, mask=mask, segstats=segstats,
Nseg=dim(segstats)[1], near=near, group=group, groupstats=groupstats,
haralick=haralick, header=header, SBlim=SBlim, magzero=magzero, dim=dim(segim),
pixscale=pixscale, imarea=imarea, skyLL=skyLL, gain=gain, call=call, date=date(),
time=proc.time()[3]-timestart, ProFound.version=packageVersion('ProFound'),
R.version=R.version)
}else{
if(is.null(header)){header=NULL}
if(keepim==FALSE){image=NULL; mask=NULL}
if(is.null(mask)){mask=NULL}
if(!is.null(badpix)){image[badpix]=NA}
if(verbose){message('No objects in segmentation map - skipping dilations and CoG')}
if(verbose){message(paste('ProFound is finished! -',round(proc.time()[3]-timestart,3),'sec'))}
output=list(segim=NULL, segim_orig=NULL, objects=NULL, objects_redo=NULL, sky=sky,
skyRMS=skyRMS, image=image, mask=mask, segstats=NULL, Nseg=0, near=NULL,
group=NULL, groupstats=NULL, haralick=NULL, header=header, SBlim=NULL,
magzero=magzero, dim=dim(segim), pixscale=pixscale, imarea=imarea, skyLL=skyLL,
gain=gain, call=call, date=date(), time=proc.time()[3]-timestart,
ProFound.version=packageVersion('ProFound'), R.version=R.version)
}
class(output)='profound'
return(invisible(output))
}
plot.profound=function(x, logR50=TRUE, dmag=0.5, hist='sky', ...){
if(class(x)!='profound'){
stop('Object class is not of type profound!')
}
if(is.null(x$image)){
stop('Missing image!')
}
if(is.null(x$segim)){
stop('Missing segmentation map!')
}
if(is.null(x$sky)){
x$sky=matrix(0, x$dim[1], x$dim[2])
}
if(length(x$sky)==1){
x$sky=matrix(x$sky, x$dim[1], x$dim[2])
}
if(is.null(x$skyRMS)){
x$skyRMS=matrix(1, x$dim[1], x$dim[2])
}
if(length(x$skyRMS)==1){
x$skyRMS=matrix(x$skyRMS, x$dim[1], x$dim[2])
}
segdiff=x$segim-x$segim_orig
segdiff[segdiff<0]=0
if(all(x$skyRMS>0, na.rm=TRUE)){
image = (x$image-x$sky)/x$skyRMS
}else{
image = (x$image-x$sky)
}
if(!is.null(x$mask)){
image[x$mask==1]=NA
}
cmap = rev(colorRampPalette(brewer.pal(9,'RdYlBu'))(100))
maximg = quantile(abs(image[is.finite(image)]), 0.995, na.rm=TRUE)
stretchscale = 1/median(abs(image), na.rm=TRUE)
layout(matrix(1:9, 3, byrow=TRUE))
if(!is.null(x$header)){
par(mar=c(3.5,3.5,0.5,0.5))
magimageWCS(image, x$header, stretchscale=stretchscale, locut=-maximg, hicut=maximg, range=c(-1,1), type='num', zlim=c(-1,1), col=cmap)
if(!is.null(x$mask)){magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))}
par(mar=c(3.5,3.5,0.5,0.5))
magimageWCS(x$segim, x$header, col=c(NA, rainbow(max(x$segim,na.rm=TRUE), end=2/3)), magmap=FALSE)
if(!is.null(x$mask)){magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))}
abline(v=c(0,dim(x$image)[1]))
abline(h=c(0,dim(x$image)[2]))
par(mar=c(3.5,3.5,0.5,0.5))
magimageWCS(image, x$header)
magimage(segdiff, col=c(NA, rainbow(max(x$segim,na.rm=TRUE), end=2/3)), magmap=FALSE, add=TRUE)
if(!is.null(x$mask)){magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))}
par(mar=c(3.5,3.5,0.5,0.5))
if(is.null(x$imarea)){
imarea=prod(x$dim)*x$pixscale^2/(3600^2)
}else{
imarea=x$imarea
}
temphist=maghist(x$segstats$mag, log='y', scale=(2*dmag)/x$imarea, breaks=seq(floor(min(x$segstats$mag, na.rm = TRUE)), ceiling(max(x$segstats$mag, na.rm = TRUE)),by=0.5),
xlab='mag', ylab=paste('
ymax=log10(max(temphist$counts,na.rm = T))
xmax=temphist$mids[which.max(temphist$counts)]
abline(ymax - xmax*0.4, 0.4, col='red')
abline(v=xmax+0.25, col='red')
axis(side=1, at=xmax+0.25, labels=xmax+0.25, tick=FALSE, line=-1, col.axis='red')
legend('topleft', legend=paste('N:',length(x$segstats$mag)))
par(mar=c(3.5,3.5,0.5,0.5))
magimageWCS(x$sky-median(x$sky,na.rm=TRUE), x$header, qdiff=TRUE)
if(!is.null(x$mask)){
magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))
stat_mean = signif(mean(x$sky[x$mask==0], na.rm=TRUE),4)
stat_sd = signif(sd(x$sky[x$mask==0], na.rm=TRUE),4)
stat_cor = signif(cor(as.numeric(x$sky[x$mask==0]), as.numeric(x$skyRMS[x$mask==0])^2, use="pairwise.complete.obs"),4)
}else{
stat_mean = signif(mean(x$sky, na.rm=TRUE),4)
stat_sd = signif(sd(x$sky, na.rm=TRUE),4)
stat_cor = signif(cor(as.numeric(x$sky), as.numeric(x$skyRMS)^2, use="pairwise.complete.obs"),4)
}
legend('topleft',legend=c('Sky',paste0('Mean: ',stat_mean),paste0('SD: ',stat_sd)),bg='white')
par(mar=c(3.5,3.5,0.5,0.5))
magimageWCS(x$skyRMS, x$header)
if(!is.null(x$mask)){
magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))
stat_mean = signif(mean(x$skyRMS[x$mask==0], na.rm=TRUE),4)
stat_sd = signif(sd(x$skyRMS[x$mask==0], na.rm=TRUE),4)
}else{
stat_mean = signif(mean(x$skyRMS, na.rm=TRUE),4)
stat_sd = signif(sd(x$skyRMS, na.rm=TRUE),4)
}
legend('topleft',legend=c('Sky RMS',paste0('Mean: ',stat_mean),paste0('SD: ',stat_sd)),bg='white')
if(hist=='iters'){
maghist(x$segstats$iter, breaks=seq(-0.5,max(x$segstats$iter, na.rm=TRUE)+0.5,by=1), majorn=max(x$segstats$iter, na.rm=TRUE)+1, xlab='Number of Dilations', ylab='
}else if(hist=='sky'){
try({
if(!is.null(x$objects_redo)){
tempsky=image[x$objects_redo==0]
}else{
tempsky=image[x$objects==0]
}
tempsky=tempsky[tempsky > -8 & tempsky < 8 & !is.na(tempsky)]
stat_LL = signif(x$skyLL,4)
magplot(density(tempsky[is.finite(tempsky)], bw=0.1), grid=TRUE, xlim=c(-6,6), xlab='(image - sky) / skyRMS', ylab='PDF', log='y', ylim=c(1e-8,0.5))
curve(dnorm(x, mean=0, sd=1), add=TRUE, col='red', lty=2)
legend('topleft',legend=c('Sky Pixels',paste0('Cor: ',stat_cor), paste0('sky LL: ',stat_LL)), bg='white')
})
}else{stop('Not a recognised hist type! Must be iters / sky.')}
par(mar=c(3.5,3.5,0.5,0.5))
if(logR50){
magplot(x$segstats$mag, x$segstats$R50, pch='.', col=hsv(alpha=0.5), ylim=c(min(x$segstats$R50, 0.1, na.rm = TRUE), max(x$segstats$R50, 1, na.rm = TRUE)), cex=3, xlab='mag', ylab='R50 / asec', grid=TRUE, log='y')
}else{
magplot(x$segstats$mag, x$segstats$R50, pch='.', col=hsv(alpha=0.5), ylim=c(0, max(x$segstats$R50, 1, na.rm = TRUE)), cex=3, xlab='mag', ylab='R50 / asec', grid=TRUE)
}
par(mar=c(3.5,3.5,0.5,0.5))
fluxrat=x$segstats$flux/x$segstats$flux_err
magplot(x$segstats$SB_N90, fluxrat, pch='.', col=hsv(alpha=0.5), ylim=c(0.5,max(fluxrat, 1, na.rm=TRUE)), cex=3, xlab='SB90 / mag/asec-sq', ylab='Flux/Flux-Error', grid=TRUE, log='y')
}else{
par(mar=c(3.5,3.5,0.5,0.5))
magimage(image, stretchscale=stretchscale, locut=-maximg, hicut=maximg, range=c(-1,1), type='num', zlim=c(-1,1), col=cmap)
if(!is.null(x$mask)){magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))}
par(mar=c(3.5,3.5,0.5,0.5))
magimage(x$segim, col=c(NA, rainbow(max(x$segim,na.rm=TRUE), end=2/3)), magmap=FALSE)
if(!is.null(x$mask)){magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))}
abline(v=c(0,dim(image)[1]))
abline(h=c(0,dim(image)[2]))
par(mar=c(3.5,3.5,0.5,0.5))
magimage(image)
magimage(segdiff, col=c(NA, rainbow(max(x$segim,na.rm=TRUE), end=2/3)), magmap=FALSE, add=TRUE)
if(!is.null(x$mask)){magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))}
par(mar=c(3.5,3.5,0.5,0.5))
temphist=maghist(x$segstats$mag, log='y', scale=(2*dmag), breaks=seq(floor(min(x$segstats$mag, na.rm = TRUE)), ceiling(max(x$segstats$mag, na.rm = TRUE)),by=0.5), xlab='mag', ylab=paste('
ymax=log10(max(temphist$counts,na.rm = T))
xmax=temphist$mids[which.max(temphist$counts)]
abline(ymax - xmax*0.4, 0.4, col='red')
abline(v=xmax+0.25, col='red')
axis(side=1, at=xmax+0.25, labels=xmax+0.25, tick=FALSE, line=-1, col.axis='red')
legend('topleft', legend=paste('N:',length(x$segstats$mag)))
par(mar=c(3.5,3.5,0.5,0.5))
magimage(x$sky-median(x$sky,na.rm=TRUE), qdiff=TRUE)
if(!is.null(x$mask)){
magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))
stat_mean = signif(mean(x$sky[x$mask==0], na.rm=TRUE),4)
stat_sd = signif(sd(x$sky[x$mask==0], na.rm=TRUE),4)
stat_cor = signif(cor(as.numeric(x$sky[x$mask==0]), as.numeric(x$skyRMS[x$mask==0])^2, use="pairwise.complete.obs"),4)
}else{
stat_mean = signif(mean(x$sky, na.rm=TRUE),4)
stat_sd = signif(sd(x$sky, na.rm=TRUE),4)
stat_cor = signif(cor(as.numeric(x$sky), as.numeric(x$skyRMS)^2, use="pairwise.complete.obs"),4)
}
legend('topleft',legend=c('Sky',paste0('Mean: ',stat_mean),paste0('SD: ',stat_sd)),bg='white')
par(mar=c(3.5,3.5,0.5,0.5))
magimage(x$skyRMS)
if(!is.null(x$mask)){
magimage(x$mask!=0, col=c(NA,hsv(alpha=0.2)), add=TRUE, magmap=FALSE, zlim=c(0,1))
stat_mean = signif(mean(x$skyRMS[x$mask==0], na.rm=TRUE),4)
stat_sd = signif(sd(x$skyRMS[x$mask==0], na.rm=TRUE),4)
}else{
stat_mean = signif(mean(x$skyRMS, na.rm=TRUE),4)
stat_sd = signif(sd(x$skyRMS, na.rm=TRUE),4)
}
legend('topleft',legend=c('Sky RMS',paste0('Mean: ',stat_mean),paste0('SD: ',stat_sd)),bg='white')
if(hist=='iters'){
maghist(x$segstats$iter, breaks=seq(-0.5,max(x$segstats$iter, na.rm=TRUE)+0.5,by=1), majorn=max(x$segstats$iter, na.rm=TRUE)+1, xlab='Number of Dilations', ylab='
}else if(hist=='sky'){
try({
if(!is.null(x$objects_redo)){
tempsky=image[x$objects_redo==0]
}else{
tempsky=image[x$objects==0]
}
tempsky=tempsky[tempsky > -8 & tempsky < 8 & !is.na(tempsky)]
stat_LL = signif(x$skyLL,4)
magplot(density(tempsky[is.finite(tempsky)], bw=0.1), grid=TRUE, xlim=c(-6,6), xlab='(image - sky) / skyRMS', ylab='PDF', log='y', ylim=c(1e-8,0.5))
curve(dnorm(x, mean=0, sd=1), add=TRUE, col='red', lty=2)
legend('topleft',legend=c('Sky Pixels',paste0('Cor: ',stat_cor), paste0('sky LL: ',stat_LL)), bg='white')
})
}else{stop('Not a recognised hist type! Must be iters / sky.')}
par(mar=c(3.5,3.5,0.5,0.5))
if(logR50){
magplot(x$segstats$mag, x$segstats$R50, pch='.', col=hsv(alpha=0.5), ylim=c(min(x$segstats$R50, 0.1, na.rm = TRUE), max(x$segstats$R50, 1, na.rm = TRUE)), cex=3, xlab='mag', ylab='R50 / asec', grid=TRUE, log='y')
}else{
magplot(x$segstats$mag, x$segstats$R50, pch='.', col=hsv(alpha=0.5), ylim=c(0, max(x$segstats$R50, 1, na.rm = TRUE)), cex=3, xlab='mag', ylab='R50 / asec', grid=TRUE)
}
par(mar=c(3.5,3.5,0.5,0.5))
fluxrat=x$segstats$flux/x$segstats$flux_err
magplot(x$segstats$SB_N90, fluxrat, pch='.', col=hsv(alpha=0.5), ylim=c(0.5,max(fluxrat, 1, na.rm=TRUE)), cex=3, xlab='SB90 / mag/pix-sq', ylab='Flux/Flux-Error', grid=TRUE, log='y')
}
}
.selectCoG=function(diffmat, threshold=1.05){
IDmat=matrix(rep(1:dim(diffmat)[2],each=dim(diffmat)[1]),nrow=dim(diffmat)[1])
logmat=diffmat>1 & diffmat<threshold
IDfin=IDmat
IDfin[logmat==FALSE]=NA
NegFlux=which(diffmat<threshold^0.2,arr.ind=TRUE)
if(length(NegFlux)>0){
NegFlux[,2]=NegFlux[,2]-1
IDfin[NegFlux]=IDmat[NegFlux]
IDfin[NegFlux[NegFlux[,2]==0,1],1]=0
}
tempout=suppressWarnings(apply(IDfin,1,min,na.rm=TRUE))
tempout[is.infinite(tempout)]=dim(diffmat)[2]
tempout+1
}
|
context("sim_generate")
test_that("sim_generate for smstp_fe", code={
test_out <- sim_base(base_id(nDomains = 2, nUnits = c(3, 5))) %>%
sim_gen_x() %>% as.data.frame
expect_is(test_out, "data.frame")
expect_equal(length(test_out), 3)
expect_equal(nrow(test_out), 8)
expect_equal(max(test_out$idU), 5)
expect_equal(max(test_out$idD), 2)
})
test_that("sim_gen", code={
setup1 <- sim_base() %>%
sim_gen(gen_norm(0, 4, name = "x")) %>%
sim_gen(gen_norm(0, 4, "e")) %>%
sim_gen_cont(gen_norm(0, 150, "e"), nCont = 0.05, type = "unit", areaVar = "idD", fixed = TRUE)
setup2 <- sim_base() %>% sim_gen_x() %>% sim_gen_e() %>% sim_gen_ec()
set.seed(1)
result1 <- sim(setup1, R = 1)
set.seed(1)
result2 <- sim(setup2, R = 1)
expect_equal(result2, result1)
})
test_that("gen_generic", {
set.seed(1)
dat1 <- gen_generic(runif, groupVars = "idD", name = "x")(base_id(5, 2))
set.seed(1)
dat2 <- gen_generic(runif, name = "x")(base_id(5, 2))
expect_is(dat1, "data.frame")
expect_equal(nrow(dat1), 10)
expect_equal(dat1[1, "x"], dat1[2, "x"])
expect_equal(unique(dat2["x"]), dat2["x"])
expect_error(gen_generic(runif, level = "")(5, 2, "x"))
set.seed(1)
dat1 <- sim(sim_base() %>%
sim_gen(gen_generic(rnorm, mean = 0, sd = 4, name="e")) %>%
sim_gen(gen_generic(rnorm, mean = 0, sd = 1, groupVars = "idD", name="v")))
set.seed(1)
dat2 <- sim(sim_base() %>% sim_gen_e() %>% sim_gen_v())
expect_equal(dat1, dat2)
})
test_that("sim_gen_generic", {
set.seed(1)
dat1 <- base_id(5, 5) %>%
sim_gen_generic(rnorm, mean = 0, sd = 4, name="e") %>%
sim_gen_generic(rnorm, mean = 0, sd = 1, groupVars = "idD", name="v") %>%
as.data.frame
set.seed(1)
dat2 <- base_id(5, 5) %>% sim_gen_e() %>% sim_gen_v() %>% as.data.frame
expect_equal(dat1, dat2)
})
test_that("gen_v_ar1", {
set.seed(1)
dat <- base_id_temporal(3, 1, 3) %>%
sim_gen(gen_v_ar1(
1.2, sd = 5, rho = 0.6,
groupVar = "idD", timeVar = "idT", name = "v")) %>%
as.data.frame
testthat::expect_equal(length(unique(dat$v)), 9)
dat <- base_id_temporal(3, 1:3, 3) %>%
sim_gen(gen_v_ar1(
1.2, sd = 5, rho = 0.6,
groupVar = "idD", timeVar = "idT", name = "v")) %>%
as.data.frame
testthat::expect_equal(length(unique(dat$v)), 9)
dat <- base_id_temporal(3, 1:3, 3) %>%
sim_gen(gen_v_ar1(
1.2, sd = 5, rho = 0.6,
groupVar = c("idD", "idU"), timeVar = "idT", name = "v")) %>%
as.data.frame
testthat::expect_equal(length(unique(dat$v)), 18)
})
|
GLMSmoothIndex<-
function(R = NULL, ...)
{
columns = 1
columnnames = NULL
if(!is.null(R)){
x = checkData(R)
columns = ncol(x)
n = nrow(x)
count = q
columns = ncol(x)
columnnames = colnames(x)
for(column in 1:columns) {
y = checkData(x[,column], method="vector", na.rm = TRUE)
sum = sum(abs(acf(y,plot=FALSE,lag.max=6)[[1]][2:7]));
acflag6 = acf(y,plot=FALSE,lag.max=6)[[1]][2:7]/sum;
values = sum(acflag6*acflag6)
if(column == 1) {
result.df = data.frame(Value = values)
colnames(result.df) = columnnames[column]
}
else {
nextcol = data.frame(Value = values)
colnames(nextcol) = columnnames[column]
result.df = cbind(result.df, nextcol)
}
}
rownames(result.df)= paste("GLM Smooth Index")
return(result.df)
}
edhec=NULL
}
|
aa.VLF.count.pos <-
function(freq, p, seqlength){
count <- mat.or.vec(nr = 1, nc = seqlength)
for(i in 1:seqlength){
count[i] <- length(which(freq[,i] < p))
}
return(count)
}
|
biblioNetwork <-
function(M,
analysis = "coupling",
network = "authors",
n = NULL,
sep = ";",
short = FALSE,
shortlabel = TRUE) {
crossprod <- Matrix::crossprod
NetMatrix <- NA
if (analysis == "coupling") {
switch(
network,
authors = {
WA <- cocMatrix(M, Field = "AU", type = "sparse", n,sep,short=short)
WCR <- cocMatrix(M, Field = "CR", type = "sparse", n,sep,short=short)
CRA <- crossprod(WCR, WA)
NetMatrix <- crossprod(CRA, CRA)
},
references = {
WCR <- Matrix::t(cocMatrix(M, Field = "CR", type = "sparse", n,sep,short=short))
NetMatrix <- crossprod(WCR, WCR)
},
sources = {
WSO <- cocMatrix(M, Field = "SO", type = "sparse", n, sep,short=short)
WCR <- cocMatrix(M, Field = "CR", type = "sparse", n, sep,short=short)
CRSO <- crossprod(WCR, WSO)
NetMatrix <- crossprod(CRSO, CRSO)
},
countries = {
WCO <- cocMatrix(M, Field = "AU_CO", type = "sparse", n, sep,short=short)
WCR <- cocMatrix(M, Field = "CR", type = "sparse", n, sep,short=short)
CRCO <- crossprod(WCR, WCO)
NetMatrix <- crossprod(CRCO, CRCO)
}
)
}
if (analysis == "co-occurrences") {
switch(
network,
authors = {
WA <- cocMatrix(M, Field = "AU", type = "sparse", n, sep,short=short)
},
keywords = {
WA <- cocMatrix(M, Field = "ID", type = "sparse", n, sep,short=short)
},
author_keywords = {
WA <- cocMatrix(M, Field = "DE", type = "sparse", n, sep,short=short)
},
titles = {
WA <- cocMatrix(M, Field = "TI_TM", type = "sparse", n, sep,short=short)
},
abstracts = {
WA <- cocMatrix(M, Field = "AB_TM", type = "sparse", n, sep,short=short)
},
sources = {
WA <- cocMatrix(M, Field = "SO", type = "sparse", n, sep,short=short)
}
)
NetMatrix <- crossprod(WA, WA)
}
if (analysis == "co-citation") {
switch(
network,
authors = {
WA <- cocMatrix(M, Field = "CR_AU", type = "sparse", n, sep,short=short)
},
references = {
WA <- cocMatrix(M, Field = "CR", type = "sparse", n, sep,short=short)
},
sources = {
WA <- cocMatrix(M, Field = "CR_SO", type = "sparse", n, sep,short=short)
}
)
NetMatrix <- crossprod(WA, WA)
}
if (analysis == "collaboration") {
switch(
network,
authors = {
WA <- cocMatrix(M, Field = "AU", type = "sparse", n, sep,short=short)
},
universities = {
WA <- cocMatrix(M, Field = "AU_UN", type = "sparse", n, sep,short=short)
},
countries = {
WA <- cocMatrix(M, Field = "AU_CO", type = "sparse", n, sep,short=short)
}
)
NetMatrix <- crossprod(WA, WA)
}
NetMatrix <- NetMatrix[nchar(colnames(NetMatrix)) != 0, nchar(colnames(NetMatrix)) != 0]
if (network == "references") {
ind <- which(regexpr("[A-Za-z]", substr(colnames(NetMatrix), 1, 1)) == 1)
NetMatrix <- NetMatrix[ind, ind]
if (isTRUE(shortlabel)) {
LABEL <- labelShort(NetMatrix, db = tolower(M$DB[1]))
LABEL <- removeDuplicatedlabels(LABEL)
colnames(NetMatrix) <- rownames(NetMatrix) <- LABEL
}
}
return(NetMatrix)
}
labelShort <- function(NET,db="isi"){
LABEL <- colnames(NET)
YEAR <- suppressWarnings(as.numeric(sub('.*(\\d{4}).*', '\\1', LABEL)))
YEAR[is.na(YEAR)] <- ""
switch(db,
isi={
AU <- strsplit(LABEL," ")
AU <- unlist(lapply(AU, function(l){paste(l[1]," ",l[2],sep="")}))
LABEL <- paste0(AU, " ", YEAR, sep="")
},
scopus={
AU <- strsplit(LABEL,"\\. ")
AU <- unlist(lapply(AU, function(l){l[1]}))
LABEL <- paste0(AU, ". ", YEAR, sep="")
})
return(LABEL)
}
removeDuplicatedlabels <- function(LABEL){
tab <- sort(table(LABEL),decreasing=T)
dup <- names(tab[tab>1])
for (i in 1:length(dup)){
ind <- which(LABEL %in% dup[i])
if (length(ind)>0){
LABEL[ind] <- paste0(LABEL[ind],"-",as.character(1:length(ind)),sep="")
}
}
return(LABEL)
}
|
"auto.noise"
"feedlot"
"fiber"
"MOats"
"neuralgia"
"nutrition"
"oranges"
"pigs"
"ubds"
|
lvTransformer.default<-function(delta, mat, lib.size=NULL)
{
if(is.null(lib.size))
{
lib.size=colSums(mat)
}
tt <- t(mat + 0.5)/(lib.size + 1) * 1e+06
mat2 <- t(log2(tt+1/delta))
vec = c(mat2)
res=(mean(vec, na.rm=TRUE)-median(vec, na.rm=TRUE))^2
return(res)
}
lvTransformer=function(mat, lib.size=NULL, low=0.001, upp=1000)
{
res.delta=optimize(lvTransformer.default, mat=mat, lib.size=lib.size,
lower=low, upper=upp)
delta = res.delta$minimum
if(is.null(lib.size))
{
lib.size=colSums(mat)
}
tt <- t(mat + 0.5)/(lib.size + 1) * 1e+06
mat2 <- t(log2(tt+1/delta))
res=list(res.delta=res.delta, delta=delta, mat2=mat2)
invisible(res)
}
|
ran.genf <- function(data, n, ran.args)
{
if (length(ran.args$ar)==0)
{
x <- as.numeric(arima.sim(model=list(ma=ran.args$ma), n=n,
innov=rnorm(n, ran.args$intercept, sqrt(ran.args$var))))
}
if (length(ran.args$ma)==0)
{
x <- as.numeric(arima.sim(model=list(ar=ran.args$ar), n=n,
innov=rnorm(n, ran.args$intercept, sqrt(ran.args$var))))
}
if (length(ran.args$ar)!=0 & length(ran.args$ma)!=0)
{
x <- as.numeric(arima.sim(model=list(ar=ran.args$ar, ma=ran.args$ma), n=n,
innov=rnorm(n, ran.args$intercept, sqrt(ran.args$var))))
}
y <- ifelse(ran.args$z==0, x, x*ran.args$q)
return(y)
}
|
estimate_pls_mga <- function(pls_model, condition, nboot = 2000, ...) {
pls_data <- pls_model$rawdata
path_estimate <- function(path, path_coef) {
path_coef[path["source"], path["target"]]
}
boot_paths <- function(path_coef, beta_df) {
betas <- apply(beta_df, MARGIN=1, FUN=path_estimate, path_coef = path_coef)
}
group1_data <- pls_data[condition, ]
group2_data <- pls_data[!condition, ]
message("Estimating and bootstrapping groups...")
group1_model <- rerun(pls_model, data = group1_data)
group2_model <- rerun(pls_model, data = group2_data)
group1_boot <- bootstrap_model(seminr_model = group1_model, nboot = nboot, ...)
group2_boot <- bootstrap_model(seminr_model = group2_model, nboot = nboot, ...)
message("Computing similarity of groups")
beta <- as.data.frame(pls_model$smMatrix[,c("source", "target")])
path_names <- do.call(paste0, cbind(beta["source"], " -> ", beta["target"]))
rownames(beta) <- path_names
beta$estimate <- apply(beta, MARGIN = 1, FUN=path_estimate, path_coef = pls_model$path_coef)
beta$group1_beta <- apply(beta, MARGIN = 1, FUN=path_estimate, path_coef = group1_model$path_coef)
beta$group2_beta <- apply(beta, MARGIN = 1, FUN=path_estimate, path_coef = group2_model$path_coef)
beta_diff <- group1_model$path_coef - group2_model$path_coef
beta$diff <- apply(beta, MARGIN = 1, FUN=path_estimate, path_coef = beta_diff)
boot1_betas <- t(apply(group1_boot$boot_paths, MARGIN=3, FUN=boot_paths, beta_df=beta))
colnames(boot1_betas) <- path_names
boot2_betas <- t(apply(group2_boot$boot_paths, MARGIN=3, FUN=boot_paths, beta_df=beta))
colnames(boot2_betas) <- path_names
J <- min(dim(boot1_betas)[1], dim(boot2_betas)[1])
if (J < nboot) {
message(paste("NOTE: Using", J, "bootstrapped results of each group after removing inadmissible runs"))
}
boot1_betas <- boot1_betas[1:J,]
boot2_betas <- boot2_betas[1:J,]
beta$group1_beta_mean <- apply(boot1_betas, MARGIN=2, FUN=mean)
beta$group2_beta_mean <- apply(boot2_betas, MARGIN=2, FUN=mean)
Theta <- function(s) {
ifelse(s > 0, 1, 0)
}
beta_comparison <- function(i, beta, beta1_boots, beta2_boots) {
for_all <- expand.grid(beta1_boots[,i], beta2_boots[,i])
2*beta$group1_beta_mean[i] - for_all[,1] - 2*beta$group2_beta_mean[i] + for_all[,2]
}
pls_mga_p <- function(i, beta, beta1_boots, beta2_boots) {
1 - (sum(Theta(beta_comparison(i, beta, beta1_boots, beta2_boots))) / J^2)
}
beta$pls_mga_p <- sapply(1:nrow(beta), FUN=pls_mga_p, beta=beta, beta1_boots=boot1_betas, beta2_boots=boot2_betas)
class(beta) <- c("seminr_pls_mga", class(beta))
beta
}
|
cacoord <- function(obj,
type = c("standard", "principal", "symmetric", "rowprincipal", "colprincipal",
"symbiplot", "rowgab", "colgab", "rowgreen", "colgreen"),
dim = NA,
rows = NA,
cols = NA,
...){
if (!inherits(obj, c("ca", "mjca"))){
stop("'obj' must be a 'ca' or 'mjca' object")
}
map <- match.arg(type)
if (is.na(rows) & is.na(cols)){
rows <- TRUE
cols <- TRUE
} else{
if (is.na(rows) | !rows){
rows <- FALSE
cols <- TRUE
obj$rowcoord <- matrix(rep(0, ncol(obj$colcoord)), nrow = 1)
obj$rowmass <- 1
}
if (is.na(cols) | !cols){
cols <- FALSE
rows <- TRUE
obj$colcoord <- matrix(rep(0, ncol(obj$rowcoord)), nrow = 1)
obj$colmass <- 1
}
}
if (is.null(rownames(obj$rowcoord))){
x.rnames <- 1:nrow(obj$rowcoord)
rownames(obj$rowcoord) <- x.rnames
} else {
x.rnames <- rownames(obj$rowcoord)
}
if (is.null(colnames(obj$rowcoord))){
x.cnames <- paste("Dim", 1:ncol(obj$rowcoord), sep = "")
colnames(obj$rowcoord) <- x.cnames
} else {
x.cnames <- colnames(obj$rowcoord)
}
if (is.null(rownames(obj$colcoord))){
y.rnames <- 1:nrow(obj$colcoord)
rownames(obj$colcoord) <- y.rnames
} else {
y.rnames <- rownames(obj$colcoord)
}
if (is.null(colnames(obj$colcoord))){
y.cnames <- paste("Dim", 1:ncol(obj$colcoord), sep = "")
colnames(obj$colcoord) <- y.cnames
} else {
y.cnames <- colnames(obj$colcoord)
}
if (is.na(dim)[1]){
sv <- obj$sv
rsc <- obj$rowcoord
csc <- obj$colcoord
} else {
sv <- obj$sv[dim]
rsc <- matrix(obj$rowcoord[,dim], ncol = length(dim))
csc <- matrix(obj$colcoord[,dim], ncol = length(dim))
rownames(rsc) <- x.rnames
colnames(rsc) <- x.cnames[dim]
rownames(csc) <- y.rnames
colnames(csc) <- y.cnames[dim]
}
if (map == "standard"){
x <- rsc
y <- csc
} else {
I <- nrow(rsc)
J <- nrow(csc)
K <- ncol(rsc)
rpc <- rsc %*% diag(sv)
cpc <- csc %*% diag(sv)
if (map == "principal"){
x <- rpc
y <- cpc
} else {
symrpc <- rsc %*% diag(sqrt(sv))
symcpc <- csc %*% diag(sqrt(sv))
rgab <- rsc * matrix(obj$rowmass, ncol = ncol(rsc), nrow = nrow(rsc))
cgab <- csc * matrix(obj$colmass, ncol = ncol(csc), nrow = nrow(csc))
rgreen <- rsc * matrix(sqrt(obj$rowmass), ncol = ncol(rsc), nrow = nrow(rsc))
cgreen <- csc * matrix(sqrt(obj$colmass), ncol = ncol(csc), nrow = nrow(csc))
mt <- c("symmetric", "rowprincipal", "colprincipal", "symbiplot",
"rowgab", "colgab", "rowgreen", "colgreen")
mti <- 1:length(mt)
mtlut <- list(symmetric = list(x = rpc, y = cpc),
rowprincipal = list(x = rpc, y = csc),
colprincipal = list(x = rsc, y = cpc),
symbiplot = list(x = symrpc, y = symcpc),
rowgab = list(x = rpc, y = cgab),
colgab = list(x = rgab, y = cpc),
rowgreen = list(x = rpc, y = cgreen),
rowgreen = list(x = rgreen, y = cpc)
)
x <- mtlut[[mti[mt == map]]][[1]]
y <- mtlut[[mti[mt == map]]][[2]]
}
}
rownames(x) <- rownames(rsc)
colnames(x) <- colnames(rsc)
rownames(y) <- rownames(csc)
colnames(y) <- colnames(csc)
if (rows & cols){
out <- list(rows = x, columns = y)
} else {
if (rows){
out <- x
} else {
out <- y
}
}
return(out)
}
|
NULL
mortalityTable.observed = setClass(
"mortalityTable.observed",
slots = list(
deathProbs = "data.frame",
years = "numeric",
ages = "numeric"
),
prototype = list(
deathProbs = data.frame(),
years = c(),
ages = c()
),
contains = "mortalityTable"
)
setMethod("ages", "mortalityTable.observed",
function(object, ...) {
object@ages;
})
setMethod("getOmega", "mortalityTable.observed",
function(object) {
max(object@ages, na.rm = TRUE);
})
setMethod("mT.round", "mortalityTable.observed",
function(object, digits = 8) {
o = callNextMethod()
o@data = round(o@data, digits = digits)
o
})
findIntRuns <- function(run) {
rundiff <- c(1, diff(run))
difflist <- split(run, cumsum(rundiff != 1))
runs = unlist(lapply(difflist, function(x) {
if (length(x) %in% 1:2) as.character(x) else paste0(x[1], "-", x[length(x)])
}), use.names = FALSE)
paste0(runs, collapse = ",")
}
setMethod("periodDeathProbabilities", "mortalityTable.observed",
function(object, ..., ages = NULL, Period = 1975) {
if (is.null(ages)) {
ages = ages(object)
}
col = which.min(abs(object@years - Period))
if (object@years[col] != Period) {
warning("periodDeathProbabilities: Desired Period ", Period,
" of observed mortalityTable not available, using closest period ",
object@years[[col]], ".\nAvailable periods: ", findIntRuns(object@years))
}
fillAges(
object@modification(object@deathProbs[,col] * (1 + object@loading)),
givenAges = ages(object),
neededAges = ages)
})
setMethod("deathProbabilities","mortalityTable.observed",
function(object, ..., ages = NULL, YOB = 1975) {
if (is.null(ages)) {
ages = ages(object);
}
years = YOB + ages;
yearcols = sapply(years, function(y) which.min(abs(object@years - y)))
agerows = match(ages, object@ages)
if (sum(abs(object@years[yearcols] - years)) > 0) {
warning("deathProbabilities: Not all observation years ", findIntRuns(years),
" of observed mortalityTable are available, using closest observations.\nAvailable periods: ", findIntRuns(object@years))
}
qx = object@deathProbs[cbind(agerows, yearcols)] * (1 + object@loading);
fillAges(object@modification(qx), givenAges = ages(object), neededAges = ages)
})
setMethod("mT.cleanup", "mortalityTable.observed",
function(object) {
o = callNextMethod()
o@ages = unname(o@ages)
o@deathProbs = unname(o@deathProbs)
o@years = unname(o@years)
o
})
|
test_not_seen_level <- function() {
d <- wrapr::build_frame(
"ID" , "OP", "DATE" , "rank" |
1 , "A" , "2001-01-02 00:00:00", 1 |
1 , "B" , "2015-04-25 00:00:00", 2 |
2 , "A" , "2000-04-01 00:00:00", 1 |
3 , "C" , "2014-04-07 00:00:00", 1 |
4 , "A" , "2005-06-16 00:00:00", 1 |
4 , "D" , "2009-01-20 00:00:00", 2 |
4 , "C" , "2012-12-01 00:00:00", 3 |
5 , "B" , "2003-11-09 00:00:00", 1 |
5 , "A" , "2010-10-10 00:00:00", 2 |
6 , "B" , "2004-01-09 00:00:00", 1 )
diagram <- wrapr::build_frame(
"rank", "DATE", "OP" |
"1", "DATE1", "OP1" |
"2", "DATE2", "OP2" |
"3", "DATE3", "OP3" |
"4", "DATE4", "OP4" |
"5", "DATE5", "OP5" )
res <- blocks_to_rowrecs(d, keyColumns = "ID", controlTable = diagram)
expect <- wrapr::build_frame(
"ID" , "DATE1" , "OP1", "DATE2" , "OP2" , "DATE3" , "OP3" , "DATE4" , "OP4" , "DATE5" , "OP5" |
1 , "2001-01-02 00:00:00", "A" , "2015-04-25 00:00:00", "B" , NA_character_ , NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ |
2 , "2000-04-01 00:00:00", "A" , NA_character_ , NA_character_, NA_character_ , NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ |
3 , "2014-04-07 00:00:00", "C" , NA_character_ , NA_character_, NA_character_ , NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ |
4 , "2005-06-16 00:00:00", "A" , "2009-01-20 00:00:00", "D" , "2012-12-01 00:00:00", "C" , NA_character_, NA_character_, NA_character_, NA_character_ |
5 , "2003-11-09 00:00:00", "B" , "2010-10-10 00:00:00", "A" , NA_character_ , NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ |
6 , "2004-01-09 00:00:00", "B" , NA_character_ , NA_character_, NA_character_ , NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ )
expect_true(wrapr::check_equiv_frames(res, expect))
invisible(NULL)
}
test_not_seen_level()
|
format.default <- function(x, ...){
res <- get("format.default", pos = "package:base")(x, ...)
attributes(res) <- attributes(x)
res
}
|
context ("avisContributorsSummary")
test_that("avisContributorsSummary returns expected header",{
response <- avisContributorsSummary()
expectedNames <- c("UserId", "Observations","Species","Provinces","UTMs","Periods")
expect_equal (colnames (response), expectedNames)
})
|
NULL
setClass(
Class = "Weight",
representation=representation(
descr = "character"
)
)
setMethod(f = "getDescr",
signature = "Weight",
definition = function(object) {
return(object@descr)
}
)
|
sparsePC <- function(...)
{
sparsePC.spikeslab(...)
}
sparsePC.spikeslab <-
function(x=NULL,
y=NULL,
n.rep=10,
n.iter1=150,
n.iter2=100,
n.prcmp=5,
max.genes=100,
ntree=1000,
nodesize=1,
verbose=TRUE,
... )
{
permute.rows <-function(x)
{
dd <- dim(x)
n <- dd[1]
p <- dd[2]
mm <- runif(length(x)) + rep(seq(n) * 10, rep(p, n))
matrix(t(x)[order(mm)], n, p, byrow = TRUE)
}
balanced.folds <- function(y, nfolds = min(min(table(y)), 10)) {
totals <- table(y)
fmax <- max(totals)
nfolds <- min(nfolds, fmax)
nfolds= max(nfolds, 2)
folds <- as.list(seq(nfolds))
yids <- split(seq(y), y)
bigmat <- matrix(NA, ceiling(fmax/nfolds) * nfolds, length(totals))
for(i in seq(totals)) {
if(length(yids[[i]])>1){bigmat[seq(totals[i]), i] <- sample(yids[[i]])}
if(length(yids[[i]])==1){bigmat[seq(totals[i]), i] <- yids[[i]]}
}
smallmat <- matrix(bigmat, nrow = nfolds)
smallmat <- permute.rows(t(smallmat))
res <-vector("list", nfolds)
for(j in 1:nfolds) {
jj <- !is.na(smallmat[, j])
res[[j]] <- smallmat[jj, j]
}
return(res)
}
class.error <- function(y, ytest, pred) {
cl <- sort(unique(y))
err <- rep(NA, length(cl))
for (k in 1:length(cl)) {
cl.pt <- (ytest == cl[k])
if (sum(cl.pt) > 0) {
err[k] <- mean(ytest[cl.pt] != pred[cl.pt])
}
}
err
}
get.pc <- function(X, Y, n.prcmp) {
cat("Getting prcmp...\n")
nclass <- length(unique(Y))
n <- nrow(X)
p <- ncol(X)
o.r <- order(Y)
Y <- Y[o.r]
Y.freq <- tapply(Y, Y, length)
X <- X[o.r, ]
X.mean <- as.double(c(apply(X, 2, mean.center, center = TRUE)))
X.sd <- as.double(c(apply(X, 2, sd.center, center = TRUE)))
ss.X <- scale(X, center = X.mean, scale = X.sd)
pc.out <- svd(ss.X)
U <- pc.out$u
D <- pc.out$d
UD <- t(t(U)*D)
imp <- apply(UD, 2, function(p){
rf.out <- randomForest(cbind(Y), p, importance = TRUE)
rf.out$importance[1,1]})
o.r <- order(imp, decreasing = TRUE)
tot.var <- cumsum(D[o.r]/sum(D))
cat("total variation for top prcmp(s):", round(100 * tot.var[n.prcmp]), "%", "\n")
Y.prcmp <- as.matrix(UD[, o.r[1:n.prcmp]])
Y.prcmp <- as.matrix(apply(Y.prcmp, 2, function(p) {
p.mean <- rep(tapply(p, Y, mean), Y.freq)
p.sd <- rep(tapply(p, Y, sd), Y.freq)
rnorm(n, p.mean, 0.1 * p.sd) }
))
colnames(ss.X) <- paste("X.", 1:p, sep="")
return(list(Y.prcmp=Y.prcmp, ss.X=ss.X, tot.var=tot.var))
}
gene.signature <- NULL
X <- as.matrix(x)
Y <- y
if (!is.factor(y)) Y <- factor(y)
n.genes <- ncol(X)
n.data <- nrow(X)
if (any(is.na(Y))) stop("Missing values not allowed in y")
if (n.data != length(Y)) stop("number of rows of x should match length of y")
n.class <- length(unique(Y))
dim.results <- pred.results <- rep(0, n.rep)
pred.class.results <- matrix(NA, n.rep, n.class)
X <- scale(X, center = TRUE, scale = TRUE)
for (k in 1:n.rep) {
if (verbose) cat("\n ---> Monte Carlo Replication:", k, "\n")
if (n.rep > 1) {
cv.sample <- balanced.folds(Y, 3)
train.pt <- c(cv.sample[[1]], cv.sample[[2]])
test.pt <- cv.sample[[3]]
}
else {
train.pt <- test.pt <- 1:n.data
}
n.prcmp <- min(n.prcmp, length(train.pt))
pc.out <- get.pc(X[train.pt, ], Y[train.pt], n.prcmp = n.prcmp)
sig.genes <- NULL
signal <- rep(0, n.genes)
for (p in 1:n.prcmp) {
if (verbose) cat("fitting principal component:", p, "(", round(100*pc.out$tot.var[p]), "%)", "\n")
ss.out <- spikeslab(x = pc.out$ss.X, y = pc.out$Y.prcmp[, p],
n.iter1 = n.iter1,
n.iter2 = n.iter2,
max.var = max.genes,
bigp.smalln.factor = max(1, round(max.genes/nrow(pc.out$ss.X))),
bigp.smalln = (nrow(pc.out$ss.X) < ncol(pc.out$ss.X)))
sig.genes.p <- as.double(which(abs(ss.out$gnet) > .Machine$double.eps))
if (length(sig.genes.p) > 0) {
sig.genes <- c(sig.genes, sig.genes.p)
signal[sig.genes.p] <- signal[sig.genes.p] + abs(ss.out$gnet)[sig.genes.p]
}
}
if (length(sig.genes) == 0) {
sig.genes <- as.double(which(signal == max(signal, na.rm = TRUE)))[1]
}
sig.genes <- sort(unique(sig.genes))
P <- min(max.genes, length(sig.genes))
o.r <- order(signal[sig.genes], decreasing = TRUE)
sig.genes.k <- sig.genes[o.r][1:P]
rf.data.x <- as.matrix(X[, sig.genes.k])
colnames(rf.data.x) <- paste("x.", 1:length(sig.genes.k))
Y.train <- Y[train.pt]
Y.test <- Y[test.pt]
rf.out <- randomForest(x=as.matrix(rf.data.x[train.pt, ]), y=Y.train,
importance = TRUE, ntree=ntree, nodesize=nodesize)
gene.signature <- c(gene.signature, sig.genes.k)
dim.results[k] <- length(sig.genes.k)
if (n.rep > 1) {
rf.pred <- predict(rf.out, newdata = as.matrix(rf.data.x[test.pt, ]))
pred.results[k] <- mean(as.character(Y.test) != rf.pred)
pred.class.results[k, ] <- class.error(as.character(Y), as.character(Y.test), rf.pred)
}
if (verbose & (n.rep > 1)) {
cat("\n", "PE:", round(pred.results[k], 3), "dimension:", dim.results[k], "\n")
}
}
gene.signature.all <- gene.signature
gene.signature.freq <- tapply(gene.signature, gene.signature, length)
gene.signature <- as.double(names(gene.signature.freq)[rev(order(gene.signature.freq))][1:mean(dim.results)])
if (verbose) cat("growing the forest classifier...\n")
rf.data.x <- as.matrix(X[, gene.signature])
colnames(rf.data.x) <- paste("x.", gene.signature)
rf.out <- randomForest(x=rf.data.x, y=Y, importance = TRUE, ntree=ntree, nodesize=nodesize)
cat("\n\n")
cat("-----------------------------------------------------------\n")
cat("no. prcmps :", n.prcmp, "\n")
cat("no. genes :", n.genes, "\n")
cat("max genes :", max.genes, "\n")
cat("no. samples :", nrow(X), "\n")
cat("no. classes :", n.class, "\n")
cat("class freq :", as.double(tapply(Y, Y, length)), "\n")
cat("class names :", levels(Y), "\n")
cat("replicates :", n.rep, "\n")
cat("model size :", round(mean(dim.results), 4), "+/-", round(sd(dim.results), 4), "\n")
if (n.rep > 1) {
cat("misclass :", round(mean(100*pred.results), 4), "+/-", round(sd(100*pred.results), 4), "\n")
for (j in 1:n.class) {
cat(paste(" class
}
}
cat("\n")
cat("Gene Signature:\n")
print(gene.signature)
cat("-----------------------------------------------------------\n")
invisible(list(gene.signature=gene.signature,
gene.signature.all=gene.signature.all,
rf.object=rf.out))
}
|
context("ebirdregion")
test_that("ebirdregion works correctly", {
skip_on_cran()
out <- ebirdregion(loc = 'US', species = 'btbwar', max = 50)
expect_is(out, "data.frame")
expect_equal(ncol(out), 13)
expect_is(out$comName, "character")
expect_is(out$howMany, "integer")
expect_equal(dim(ebirdregion('US-OH', max=10, provisional=TRUE, hotspot=TRUE)), c(10,13))
res <- ebirdregion(loc = 'US-CA', max = 10)
expect_equal(ncol(res), 13)
expect_equal(ncol(ebirdregion(loc = 'US', species = 'coohaw')), 13)
expect_gte(ncol(ebirdregion(loc = 'L109339', species = 'amecro', simple = FALSE)), 26)
})
|
fun.Rjudge <-
function(A,B,symbol)
{
size <- length(A);
Output <- matrix(1, nrow=size, ncol=1)
for(i in 1:size){
if(symbol=='<'){
if(A[i] >= B){Output[i] <- 0}
} else
if(symbol=='>='){
if(A[i] < B){Output[i] <- 0}
}
}
return(Output)
}
|
`confint.cusp` <-
function (object, parm, level = 0.95, ...)
{
cf <- coef(object)
pnames <- names(cf)
v <- vcov(object)
pnames <- colnames(v)
if (missing(parm))
parm <- seq_along(pnames)
else if (is.character(parm))
parm <- match(parm, pnames, nomatch = 0)
a <- (1 - level)/2
a <- c(a, 1 - a)
pct <- format.perc(a, 3)
fac <- qnorm(a)
ci <- array(NA, dim = c(length(parm), 2), dimnames = list(pnames[parm],
pct))
ses <- sqrt(diag(v))[parm]
ci[] <- cf[pnames[parm]] + ses %o% fac
ci
}
|
assertthat::on_failure(is_valid_bdc) <- function (call, env) {
paste0(deparse(call$bdc), " contains invalid business day convention.")
}
assertthat::on_failure(is_valid_day_basis) <- function(call, env) {
paste0(deparse(call$bdc), " contains invalid business day convention.")
}
assertthat::on_failure(is_list_of) <- function(call, env) {
paste0("All elements of ", deparse(call$object), " are not objects of class ",
deparse(call$class), ".")
}
|
.readComment <- function(fileName, commentChar = "*", metaChar = "
comment <- NULL
if (!is.null(commentChar)) {
if (commentChar != "") {
zz <- file(fileName)
open(zz)
readRepeat <- TRUE
while (readRepeat) {
tmp <- readLines(zz, 1)
if (length(grep(paste("^", escapeRegex(commentChar), sep = ""), tmp)) &
!grepl(metaChar, substr(tmp, 3, 3), fixed = TRUE)) {
comment <- c(comment, tmp)
} else {
readRepeat <- FALSE
}
}
close(zz)
}
}
return(substring(comment, 2))
}
|
library(uwot)
context("API output")
set.seed(1337)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "normlaplacian", verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res)
set.seed(1337)
res2 <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "normlaplacian", verbose = FALSE, n_threads = 0
)
expect_equal(res2, res)
res <- umap(dist(iris10),
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "laplacian", verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res)
res <- tumap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, metric = "cosine",
init = "spectral", verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, metric = "cosine",
init = "spectral", verbose = FALSE, n_threads = 1
)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, metric = "manhattan",
init = "rand", verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, metric = "manhattan",
init = "spca", verbose = FALSE, n_threads = 1
)
expect_ok_matrix(res)
iris10_pca <- prcomp(iris10,
retx = TRUE, center = TRUE,
scale. = FALSE
)$x[, 1:2]
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = iris10_pca, verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res)
expect_equal(iris10_pca, prcomp(iris10,
retx = TRUE, center = TRUE,
scale. = FALSE
)$x[, 1:2])
set.seed(1337)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "spca", verbose = FALSE, n_threads = 0,
ret_nn = TRUE
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
expect_is(res$nn, "list")
expect_is(res$nn$euclidean, "list")
expect_ok_matrix(res$nn$euclidean$idx, nc = 4)
expect_ok_matrix(res$nn$euclidean$dist, nc = 4)
set.seed(1337)
res_nn <- umap(iris10,
nn_method = res$nn[[1]], n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "spca", verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res_nn)
expect_equal(res_nn, res$embedding)
set.seed(1337)
res_nnxn <- umap(
X = NULL,
nn_method = nn, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "rand", verbose = FALSE, n_threads = 0
)
set.seed(1337)
res_nnl <- umap(iris10,
nn_method = res$nn, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "rand", verbose = FALSE, n_threads = 0,
ret_nn = TRUE
)
expect_ok_matrix(res_nnl$embedding)
expect_equal(res_nnl$nn[[1]], res$nn[[1]])
expect_equal(names(res_nnl$nn), "precomputed")
expect_equal(res_nnxn, res_nnl$embedding)
res_nn2 <- umap(iris10,
nn_method = list(nn, nn), n_epochs = 2, learning_rate = 0.5,
min_dist = 0.001,
init = "spca", verbose = FALSE, n_threads = 0, ret_nn = TRUE
)
expect_ok_matrix(res_nn2$embedding)
expect_equal(names(res_nn2$nn), c("precomputed", "precomputed"))
res <- lvish(iris10,
perplexity = 4, n_epochs = 2, learning_rate = 0.5, nn_method = "annoy",
init = "lvrand", verbose = FALSE, n_threads = 1, ret_extra = c("sigma")
)
expect_ok_matrix(res$embedding)
expect_equal(res$sigma, c(0.3039, 0.2063, 0.09489, 0.08811, 0.3091, 0.6789, 0.1743, 0.1686, 0.3445, 0.1671), tol = 1e-4)
res <- lvish(iris10,
kernel = "knn", perplexity = 4, n_epochs = 2, learning_rate = 0.5,
init = "lvrand", verbose = FALSE, n_threads = 1
)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "rand", verbose = FALSE, n_threads = 1,
ret_model = TRUE
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
res_test <- umap_transform(iris10, res, n_threads = 1, verbose = FALSE)
expect_ok_matrix(res_test)
res_test0 <- umap_transform(iris10, res, n_epochs = 0, n_threads = 1, verbose = FALSE)
expect_ok_matrix(res_test)
expect_equal(dim(res_test0), c(10, 2))
res <- tumap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "rand", verbose = FALSE, n_threads = 1,
ret_model = TRUE, ret_nn = TRUE
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
expect_is(res$nn, "list")
expect_is_nn(res$nn[[1]], k = 4)
expect_equal(names(res$nn), "euclidean")
res_test <- umap_transform(iris10, res, n_threads = 0, verbose = FALSE)
expect_ok_matrix(res_test)
res <- umap(iris10,
n_components = 1, n_neighbors = 4, n_epochs = 2,
n_threads = 1, verbose = FALSE
)
expect_ok_matrix(res, nc = 1)
res <- umap(iris10,
n_components = 1, n_neighbors = 4, n_epochs = 2,
n_threads = 1, verbose = FALSE, init = "irlba_spectral"
)
expect_ok_matrix(res, nc = 1)
set.seed(1337)
res_y <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
min_dist = 0.001, init = "spca", verbose = FALSE, n_threads = 0,
y = 1 / (1:10)^2, target_n_neighbors = 2
)
expect_ok_matrix(res_y)
y_nn <- list(
idx = matrix(c(
1, 2,
2, 3,
3, 4,
4, 5,
5, 6,
6, 7,
7, 8,
8, 9,
9, 10,
10, 9
), ncol = 2, byrow = TRUE),
dist = matrix(c(
0, 0.750000000,
0, 0.138888896,
0, 0.048611112,
0, 0.022500001,
0, 0.012222221,
0, 0.007369615,
0, 0.004783163,
0, 0.003279321,
0, 0.002345679,
0, 0.002345679
), ncol = 2, byrow = TRUE)
)
set.seed(1337)
res_ynn <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
min_dist = 0.001, init = "spca", verbose = FALSE, n_threads = 0,
y = y_nn
)
expect_ok_matrix(res_ynn)
expect_equal(res_ynn, res_y)
bin10 <- structure(c(
0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L
), .Dim = c(10L, 4L))
res <- umap(bin10,
n_neighbors = 4, metric = "hamming", verbose = FALSE,
n_threads = 1
)
expect_ok_matrix(res)
set.seed(1337)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0,
metric = list(euclidean = c(1, 2), euclidean = c(3, 4)),
ret_model = TRUE
)
res_trans <- umap_transform(iris10,
model = res, verbose = FALSE, n_threads = 0,
n_epochs = 2
)
expect_ok_matrix(res_trans)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0, pca = 2,
ret_model = TRUE
)
expect_ok_matrix(res$embedding)
expect_is(res$pca_models, "list")
expect_equal(length(res$pca_models), 1)
expect_ok_matrix(res$pca_models[["1"]]$rotation, nr = 4, nc = 2)
expect_equal(res$pca_models[["1"]]$center, c(4.86, 3.31, 1.45, 0.22),
check.attributes = FALSE
)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0, pca = 2,
pca_center = FALSE, ret_model = TRUE
)
expect_ok_matrix(res$embedding)
expect_is(res$pca_models, "list")
expect_equal(length(res$pca_models), 1)
expect_ok_matrix(res$pca_models[["1"]]$rotation, nr = 4, nc = 2)
expect_null(res$pca_models[["1"]]$center)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
metric = list("euclidean" = 1:2, "euclidean" = 3:4),
init = "spca", verbose = FALSE, n_threads = 0, pca = 2
)
expect_ok_matrix(res)
set.seed(1337)
ib10 <- cbind(iris10, bin10, bin10)
res <- umap(ib10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0,
metric = list(
euclidean = c(1, 2), hamming = 5:12,
euclidean = c(3, 4)
),
pca = 2,
ret_model = TRUE
)
expect_ok_matrix(res$embedding)
expect_is(res$pca_models, "list")
expect_equal(length(res$pca_models), 2)
expect_equal(names(res$pca_models), c("1", "3"))
expect_ok_matrix(res$pca_models[["1"]]$rotation, nr = 2, nc = 2)
expect_equal(res$pca_models[["1"]]$center, c(4.86, 3.31),
check.attributes = FALSE
)
expect_ok_matrix(res$pca_models[["3"]]$rotation, nr = 2, nc = 2)
expect_equal(res$pca_models[["3"]]$center, c(1.45, 0.22),
check.attributes = FALSE
)
res_trans <- umap_transform(ib10,
model = res, verbose = FALSE, n_threads = 0,
n_epochs = 2
)
expect_ok_matrix(res_trans)
set.seed(1337)
res <- umap(ib10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0,
metric = list(
euclidean = c(1, 2),
hamming = 5:8,
euclidean = list(c(3, 4), pca = NULL)
),
pca = 2,
ret_model = TRUE
)
expect_ok_matrix(res$embedding)
expect_is(res$pca_models, "list")
expect_equal(length(res$pca_models), 1)
expect_equal(names(res$pca_models), "1")
expect_ok_matrix(res$pca_models[["1"]]$rotation, nr = 2, nc = 2)
expect_equal(res$pca_models[["1"]]$center, c(4.86, 3.31),
check.attributes = FALSE
)
res_trans <- umap_transform(ib10,
model = res, verbose = FALSE, n_threads = 0,
n_epochs = 2
)
expect_ok_matrix(res_trans)
set.seed(1337)
res <- umap(bin10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0,
metric = "manhattan", pca = 2, pca_center = FALSE,
ret_model = TRUE
)
expect_ok_matrix(res$embedding)
expect_is(res$pca_models, "list")
expect_equal(length(res$pca_models), 1)
expect_equal(names(res$pca_models), "1")
expect_ok_matrix(res$pca_models[["1"]]$rotation, nr = 4, nc = 2)
expect_null(res$pca_models[["1"]]$center)
res_trans <- umap_transform(bin10,
model = res, verbose = FALSE, n_threads = 0,
n_epochs = 2
)
expect_ok_matrix(res_trans)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "pca", verbose = FALSE, n_threads = 0, init_sdev = 2
)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "laplacian", verbose = FALSE, n_threads = 0,
init_sdev = 0.1
)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spectral", verbose = FALSE, n_threads = 0,
init_sdev = 5
)
expect_ok_matrix(res)
res_sd <- apply(res, 2, sd)
res2 <- umap(iris10,
n_neighbors = 4, n_epochs = 0, learning_rate = 0.5,
init = res, verbose = FALSE, n_threads = 0,
init_sdev = 5
)
expect_ok_matrix(res2)
expect_equal(apply(res2, 2, sd), rep(5, ncol(res2)))
expect_equal(apply(res, 2, sd), res_sd)
set.seed(1337)
res <- umap(iris10[1:4, ],
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "rand", verbose = FALSE, ret_model = TRUE
)
expect_is(res, "list")
expect_ok_matrix(res$embedding, nr = 4)
res_test <- umap_transform(iris10[5:10, ], res, verbose = FALSE, n_epochs = 10)
expect_ok_matrix(res_test, nr = 6)
expect_equal(res$metric$euclidean$ndim, 4)
res <- umap(iris10,
pcg_rand = FALSE,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spectral", verbose = FALSE, n_threads = 0,
init_sdev = 5
)
expect_ok_matrix(res)
res <- umap(iris10, n_neighbors = 4, n_threads = 0.5)
expect_ok_matrix(res)
res <- umap(iris10, n_neighbors = 4, n_threads = 1.5)
expect_ok_matrix(res)
res <- umap(iris10, n_neighbors = 4, n_sgd_threads = 0.5)
expect_ok_matrix(res)
res <- umap(iris10, n_neighbors = 4, n_sgd_threads = 1.5)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "spca", verbose = FALSE, n_threads = 0,
ret_extra = c("fgraph")
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
expect_is(res$fgraph, "Matrix")
res <- tumap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0,
ret_extra = c("fgraph")
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
expect_is(res$fgraph, "Matrix")
res <- lvish(iris10,
perplexity = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, n_threads = 0,
ret_extra = c("P")
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
expect_is(res$P, "Matrix")
set.seed(42)
res_cor <- tumap(iris10, n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
metric = "correlation", init = "spectral", verbose = FALSE,
n_threads = 0, ret_model = TRUE)
expect_ok_matrix(res_cor$embedding)
set.seed(42)
res_cos <- tumap(iris10, n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
metric = "cosine", init = "spectral", verbose = FALSE,
n_threads = 0, ret_model = TRUE)
expect_gt(sum((res_cor$embedding - res_cos$embedding) ^ 2), 1e-3)
set.seed(42)
res_trans_cor <- umap_transform(x2m(iris[11:20, ]), res_cor, n_threads = 0, verbose = FALSE)
expect_ok_matrix(res_trans_cor)
res_cor$nn_index$metric <- "cosine"
set.seed(42)
res_trans_cor2 <- umap_transform(x2m(iris[11:20, ]), res_cor, n_threads = 0, verbose = FALSE)
expect_ok_matrix(res_trans_cor2)
expect_gt(sum((res_trans_cor - res_trans_cor2) ^ 2), 1e-3)
set.seed(42)
xnames <-
data.frame(matrix(rnorm(10 * 4), nrow = 10), row.names = letters[1:10])
xumap <-
umap(
xnames,
n_neighbors = 4,
verbose = FALSE,
n_threads = 0,
ret_model = TRUE,
ret_nn = TRUE
)
expect_equal(row.names(xumap$embedding), row.names(xnames))
expect_equal(row.names(xumap$nn$euclidean$idx), row.names(xnames))
expect_equal(row.names(xumap$nn$euclidean$dist), row.names(xnames))
first_coords <- c()
test_callback <- function(epochs, n_epochs, coords) {
first_coords <<- c(first_coords, coords[1, 1])
}
set.seed(42)
ibatch <- tumap(iris10, n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, batch = TRUE,
n_threads = 0, n_sgd_threads = 0, ret_model = TRUE,
epoch_callback = test_callback)
expect_equal(length(first_coords), 2)
set.seed(42)
ibatch2 <- tumap(iris10, n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, batch = TRUE,
n_threads = 0, n_sgd_threads = 2, ret_model = TRUE)
expect_equal(ibatch$embedding, ibatch2$embedding)
itest <- x2m(iris[11:20, ])
first_coords <- c()
fixed_first_coords <- c()
test_transform_callback <- function(epochs, n_epochs, coords, fixed_coords) {
first_coords <<- c(first_coords, coords[1, 1])
fixed_first_coords <<- c(fixed_first_coords, fixed_coords[1, 1])
}
set.seed(42)
ibatchtest <- umap_transform(itest, ibatch, epoch_callback = test_transform_callback, n_epochs = 5)
expect_equal(length(first_coords), 5)
expect_equal(length(fixed_first_coords), 5)
expect_equal(length(unique(first_coords)), 4)
expect_equal(length(unique(fixed_first_coords)), 1)
set.seed(42)
ibatchtest2 <- umap_transform(itest, ibatch, n_sgd_threads = 2, n_epochs = 5)
expect_equal(ibatchtest, ibatchtest2)
oargs_umap <- tumap(iris10, n_neighbors = 4, n_epochs = 0, learning_rate = 0.5,
init = "spca", verbose = FALSE, batch = TRUE,
n_threads = 0, n_sgd_threads = 0, ret_model = TRUE,
opt_args = list(alpha = 0.4, beta1 = 0.1, beta2 = 0.2, eps = 1e-3))
expect_equal(length(oargs_umap$opt_args), 5)
expect_equal(oargs_umap$opt_args$method, "adam")
expect_equal(oargs_umap$opt_args$alpha, 0.4)
expect_equal(oargs_umap$opt_args$beta1, 0.1)
expect_equal(oargs_umap$opt_args$beta2, 0.2)
expect_equal(oargs_umap$opt_args$eps, 1e-3)
oargs_umap <- tumap(iris10, n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
init = "spca", verbose = FALSE, batch = TRUE,
n_threads = 0, n_sgd_threads = 0, ret_model = TRUE,
opt_args = list(method = "sgd", alpha = 0.4))
expect_equal(length(oargs_umap$opt_args), 2)
expect_equal(oargs_umap$opt_args$method, "sgd")
expect_equal(oargs_umap$opt_args$alpha, 0.4)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "spca", verbose = FALSE, n_threads = 0,
ret_extra = c("sigma")
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
expected_sigma <- c(
0.1799, 0.2049, 0.04938, 0.0906, 0.2494, 0.003906, 0.1537, 0.1355, 0.2454, 0.2063
)
sigma <- res$sigma
expect_equal(sigma, expected_sigma, tolerance = 1e-4)
expected_rho <- c(
0.1414, 0.1732, 0.2449, 0.2449, 0.1414, 0.6164, 0.2646, 0.1732, 0.3, 0.1732
)
rho <- res$rho
expect_equal(rho, expected_rho, 1e-4)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "normlaplacian", verbose = FALSE, n_threads = 0, dens_scale = 1
)
expect_ok_matrix(res)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "normlaplacian", verbose = FALSE, n_threads = 0, dens_scale = 1,
ret_extra = c("sigma", "localr")
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
sigma <- res$sigma
expect_equal(sigma, expected_sigma, tolerance = 1e-4)
rho <- res$rho
expect_equal(rho, expected_rho, tolerance = 1e-4)
expected_localr <- c(
0.3214, 0.3781, 0.2943, 0.3356, 0.3908, 0.6203, 0.4182, 0.3087, 0.5454, 0.3795
)
localr <- res$localr
expect_equal(localr, expected_localr, tolerance = 1e-4)
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "normlaplacian", verbose = FALSE, n_threads = 0, dens_scale = 1,
ret_model = TRUE
)
expect_is(res, "list")
expect_ok_matrix(res$embedding)
expected_ai <- c(
8.072, 2.957, 13.89, 6.181, 2.41, 0.1389, 1.585, 10.34, 0.3076, 2.888
)
ai <- res$ai
expect_equal(ai, expected_ai, tolerance = 1e-4)
expect_equal(res$dens_scale, 1.0)
expect_equal(res$method, "leopold")
res <- umap(iris10,
n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
init = "normlaplacian", verbose = FALSE, n_threads = 0, dens_scale = 0.5,
ret_model = TRUE
)
expected_ai05 <- c(
3.348, 2.027, 4.392, 2.93, 1.83, 0.4392, 1.484, 3.79, 0.6536, 2.003
)
expect_equal(res$ai, expected_ai05, tolerance = 1e-3)
expect_equal(res$dens_scale, 0.5)
ret_trans <- umap_transform(iris10, res)
expect_ok_matrix(res$embedding)
|
library(qrmtools)
n <- 10^c(1,3,5)
pmExp <- 2
pmG <- c(2, 2)
pmN <- c(1, 2)
pmLN <- c(1, 2)
x <- seq(-3, 10, length.out = 257)
pFun <- list(function(q, ...) pexp(q, rate = pmExp, ...),
function(q, ...) pgamma(q, shape = pmG[1], rate = pmG[2], ...),
function(q, ...) pnorm(q, mean = pmN[1], sd = pmN[2], ...),
function(q, ...) plnorm(q, meanlog = pmLN[1], sdlog = pmLN[2], ...))
ndf <- length(pFun)
dfs <- as.expression(c(paste0("Exp(",pmExp,")"),
substitute(Gamma(a,b), list(a = pmG[1], b = pmG[2])),
paste0("N(",pmN[1],", ",pmN[2],")"),
paste0("LN(",pmLN[1],", ",pmLN[2],")")))
dFun <- list(function(x, ...) dexp(x, rate = pmExp, ...),
function(x, ...) dgamma(x, shape = pmG[1], rate = pmG[2], ...),
function(x, ...) dnorm(x, mean = pmN[1], sd = pmN[2], ...),
function(x, ...) dlnorm(x, meanlog = pmLN[1], sdlog = pmLN[2], ...))
qFun <- list(function(p, ...) qexp(p, rate = pmExp, ...),
function(p, ...) qgamma(p, shape = pmG[1], rate = pmG[2], ...),
function(p, ...) qnorm(p, mean = pmN[1], sd = pmN[2], ...),
function(p, ...) qlnorm(p, meanlog = pmLN[1], sdlog = pmLN[2], ...))
d_n <- function(n, k)
switch(k,
{ log(n)/pmExp },
{ (log(n) + (pmG[1] - 1) * log(log(n)) - lgamma(pmG[1])) / pmG[2] },
{ pmN[1] + pmN[2] * (sqrt(2*log(n)) - (log(4*pi) + log(log(n))) / (2*sqrt(2*log(n)))) },
{ exp(pmLN[1] + pmLN[2] * (sqrt(2*log(n)) - (log(4*pi) + log(log(n))) / (2*sqrt(2*log(n))))) },
stop("Wrong case"))
c_n <- function(n, k)
switch(k,
{ 1/pmExp },
{ 1/pmG[2] },
{ pmN[2] / sqrt(2*log(n)) },
{ pmLN[2] * d_n(n, k = k) / sqrt(2*log(n)) },
stop("Wrong case"))
res.p <- vector("list", length = ndf)
for(k in seq_len(ndf)) {
res.p[[k]] <- lapply(seq_along(n), function(i) {
c.n <- c_n(n[i], k = k)
d.n <- d_n(n[i], k = k)
ly <- log(n[i]) + log(c.n) + dFun[[k]](d.n + c.n * x, log = TRUE) +
(n[i]-1) * pFun[[k]](d.n + c.n * x, log.p = TRUE)
cbind(x = x, y = exp(ly))
})
}
dLambda <- exp(-exp(-x)-x)
xlim <- range(x)
ylim <- range(dLambda, unlist(lapply(res.p, function(x) lapply(x, function(x.) x.[,"y"]))))
opar <- par(mar = c(5, 4, 4, 2) + 0.1 - c(1, 1, 3, 0))
layout(matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE))
k <- 1
for(i in 1:2) {
for(j in 1:2) {
r <- res.p[[k]]
plot(x, dLambda, type = "l", xlim = xlim, ylim = ylim,
xlab = "x", ylab = "")
lines(r[[1]][,"x"], r[[1]][,"y"], col = "royalblue3")
lines(r[[2]][,"x"], r[[2]][,"y"], col = "maroon3")
lines(r[[3]][,"x"], r[[3]][,"y"], col = "darkorange2")
legend("topright", bty = "n", lty = rep(1, 4),
col = c("black", "royalblue3", "maroon3", "darkorange2"),
legend = as.expression(c("Gumbel",
lapply(1:3, function(i) substitute(n == n., list(n. = n[i]))))))
mtext(dfs[k], side = 4, adj = 0, line = 0.75)
k <- k+1
}
}
layout(1)
par(opar)
m <- 100
ns <- n * m
ns.max <- max(ns)
set.seed(271)
U <- runif(ns.max)
system.time(dat <- lapply(qFun, function(qF) qF(U)))
res.np <- vector("list", length = ndf)
for(k in seq_len(ndf)) {
res.np[[k]] <- lapply(seq_along(n), function(i) {
c.n <- c_n(n[i], k = k)
d.n <- d_n(n[i], k = k)
dat. <- head(dat[[k]], n = ns[i])
dat.blocks <- split(dat., f = rep(1:m, each = n[i]))
M <- sapply(dat.blocks, function(x) (max(x) - d.n) / c.n)
fit.dens <- density(M)
cbind(x = fit.dens$x, y = fit.dens$y)
})
}
ylim <- range(dLambda, unlist(lapply(res.np, function(x) lapply(x, function(x.) x.[,"y"]))))
opar <- par(mar = c(5, 4, 4, 2) + 0.1 - c(1, 1, 3, 0))
layout(matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE))
k <- 1
for(i in 1:2) {
for(j in 1:2) {
r <- res.np[[k]]
plot(x, dLambda, type = "l", xlim = xlim, ylim = ylim,
xlab = "x", ylab = "")
lines(r[[1]][,"x"], r[[1]][,"y"], col = "royalblue3")
lines(r[[2]][,"x"], r[[2]][,"y"], col = "maroon3")
lines(r[[3]][,"x"], r[[3]][,"y"], col = "darkorange2")
legend("topright", bty = "n", lty = rep(1, 4),
col = c("black", "royalblue3", "maroon3", "darkorange2"),
legend = as.expression(c("Gumbel",
lapply(1:3, function(i) substitute(n == n., list(n. = n[i]))))))
mtext(dfs[k], side = 4, adj = 0, line = 0.75)
k <- k+1
}
}
layout(1)
par(opar)
|
data2platonic <- function(datamatrix,shape=Rvcg::vcgSphere(),col="red",scale=FALSE,scalefactor=1) {
myplatonic <- shape
myplatonic <- Morpho::scalemesh(myplatonic,center="none",size=scalefactor)
if(scale)
matrix <- scale(matrix)
col1mesh <- rgb(t(col2rgb(col)), maxColorValue = 255)
matmesh <- lapply(1:nrow(datamatrix), function(x) x <- myplatonic)
matmesh <- lapply(1:nrow(datamatrix), function(x) x <- translate3d(matmesh[[x]], x = datamatrix[x, 1], y = datamatrix[x, 2], z = datamatrix[x,3]))
matmesh <- Morpho::mergeMeshes(matmesh)
matmesh$material$color <- rep(col1mesh, ncol(matmesh$vb))
matmesh$normals <- NULL
return(matmesh)
}
|
ldata <- vcdExtra::datasets("Lahman")
ldata <- ldata[-which(ldata$Item=="LahmanData"),]
ldata <- ldata[-grep("Labels", ldata$Item),]
ldata
dims <- t(matrix(as.numeric(unlist(strsplit(ldata$dim, "x"))), 2, nrow(ldata)))
title <- sub(" -.*", "", ldata$Title)
ldata <- data.frame(file=ldata$Item, class=ldata$class, nobs=dims[,1], nvar=dims[,2], title=title, stringsAsFactors = FALSE )
LahmanData <- ldata
save(LahmanData, file="data/LahmanData.RData")
|
denominator <- function(x, numerator = 1, quarter = TRUE, ...) {
if (!length(x)) return(character(0))
if (!is.numeric(x)) stop("`x` must be numeric")
if (!is.numeric(numerator)) stop("`numerator` must be numeric")
if (length(numerator) != 1 & length(numerator) != length(x))
stop("`numerator` must be either length one or the same length as `x`")
if (length(quarter) != 1) stop("`quarter` must be length one")
if (!is.logical(quarter) | is.na(quarter))
stop("`quarter` must be either `TRUE` or `FALSE`")
numeric <- x
denom <- ordinal(x, ...)
plural <- abs(numerator) != 1
denom[abs(x) == 1] <- gsub("1st$|first$", "whole", denom[abs(x) == 1])
denom[abs(x) == 2 & !plural] <- gsub(
"2nd$|second$", "half", denom[abs(x) == 2 & !plural]
)
denom[abs(x) == 2 & plural] <- gsub(
"2nd$|second$", "halves", denom[abs(x) == 2 & plural]
)
if (quarter) {
denom[abs(x) == 4] <- gsub("fourth$", "quarter", denom[abs(x) == 4])
}
denom[plural & abs(x) != 2] <- paste0(denom[plural & abs(x) != 2], "s")
denom <- gsub("^one-", "", denom)
args <- as.list(match.call()[-1])
args[["x"]] <- NULL
structure(
denom,
numeric = numeric,
nombre = "denominator",
args = args,
class = c("nombre", "character")
)
}
nom_denom <- denominator
|
"ecld.sged_const" <- function(object)
{
ecld.validate(object, sged.only=TRUE)
one <- if([email protected]) ecd.mp1 else 1
lambda <- object@lambda
s <- object@sigma
b <- object@beta
mu <- object@mu
d <- ecd(lambda=lambda, sigma=s, mu=mu, bare.bone=TRUE)
e_y <- function(x) exp(ecld.solve(object, x))
i1 <- ecd.integrate(d, e_y, mu, Inf)
i2 <- ecd.integrate(d, e_y, -Inf, mu)
C <- i1$value + i2$value
return(C)
}
"ecld.sged_cdf" <- function(object, x)
{
ecld.validate(object, sged.only=TRUE)
d <- ecd(lambda=object@lambda, sigma=object@sigma, bare.bone=TRUE)
e_y <- function(x) exp(ecld.solve(object, x))
C <- ecld.const(object)
cdf <- function(x) {
if (x < object@mu) {
c2 <- ecd.integrate(d, e_y, -Inf, x)
return(c2$value/C)
} else {
c1 <- ecd.integrate(d, e_y, x, Inf)
return(1-c1$value/C)
}
}
return(ecld.sapply(object, x, cdf))
}
"ecld.sged_moment" <- function(object, order)
{
ecld.validate(object, sged.only=TRUE)
if (length(order)>1) {
return(ecld.sapply(object, order, function(n) ecld.sged_moment(object,n) ))
}
d <- ecd(lambda=object@lambda, sigma=object@sigma, bare.bone=TRUE)
e_y <- function(x) exp(ecld.solve(object, x)) * (x-object@mu)^order
C <- ecld.const(object)
c1 <- ecd.integrate(d, e_y, object@mu, Inf)
c2 <- ecd.integrate(d, e_y, -Inf, object@mu)
return((c1$value + c2$value)/C)
}
"ecld.sged_mgf" <- function(object, t=1)
{
ecld.validate(object, sged.only=TRUE)
sigma <- object@sigma
d <- ecd(lambda=object@lambda, sigma=sigma, bare.bone=TRUE)
e_y <- function(x) exp(ecld.solve(object, x) + t*x)
C <- ecld.const(object)
xmax <- ecld.y_slope_trunc(object)
if (is.na(xmax)) {
stop("Failed to locate y_slope truncation point")
}
xmax2 <- .ecd.mpfr.N.sigma * sigma + object@mu
if (xmax > xmax2) xmax <- xmax2
c1 <- ecd.integrate(d, e_y, object@mu, xmax)
c2 <- ecd.integrate(d, e_y, -Inf, object@mu)
return(c1$value/C + c2$value/C)
}
"ecld.sged_imgf" <- function(object, k, t=1, otype="c")
{
if (length(k)>1) {
f <- function(k) ecld.sged_imgf(object, k, t=t, otype=otype)
return(ecld.sapply(object, k, f))
}
if (!(otype %in% c("c","p"))) {
stop(paste("Unknown option type:", otype))
}
ecld.validate(object, sged.only=TRUE)
d <- ecd(lambda=object@lambda, sigma=object@sigma, bare.bone=TRUE)
e_y <- function(x) exp(ecld.solve(object, x) + t*x)
C <- ecld.const(object)
if (otype=="c") {
if (k >= object@mu) {
xmax <- ecld.y_slope_trunc(object)
if (is.na(xmax)) {
stop("Failed to locate y_slope truncation point")
}
xmax2 <- .ecd.mpfr.N.sigma * object@sigma + object@mu
if (xmax > xmax2) xmax <- xmax2
c1 <- ecd.integrate(d, e_y, k, xmax)
return(c1$value/C)
} else {
Mp <- ecld.sged_imgf(object, k, otype="p")
M1 <- ecld.sged_mgf(object)
return(M1-Mp)
}
}
if (otype=="p") {
if (k < object@mu) {
c2 <- ecd.integrate(d, e_y, -Inf, k)
return(c2$value/C)
} else {
Mc <- ecld.sged_imgf(object, k, otype="c")
M1 <- ecld.sged_mgf(object)
return(M1-Mc)
}
}
stop(paste("Unknown option type:", otype))
}
"ecld.sged_ogf" <- function(object, k, otype="c")
{
if (length(k)>1) {
f <- function(k) ecld.sged_ogf(object, k, otype=otype)
return(ecld.mclapply(object, k, f))
}
if (!(otype %in% c("c","p"))) {
stop(paste("Unknown option type:", otype))
}
ecld.validate(object, sged.only=TRUE)
if (otype=="c") {
Mc <- ecld.imgf(object, k, otype="c")
ccdf <- 1-ecld.cdf(object, k)
return(Mc-exp(k)*ccdf)
}
if (otype=="p") {
Mp <- ecld.imgf(object, k, otype="p")
cdf <- ecld.cdf(object, k)
return(-Mp+exp(k)*cdf)
}
stop(paste("Unknown option type:", otype))
}
|
getWDI = function(indicator = "SP.POP.TOTL", name = NULL,
startDate = 1960, endDate = format(Sys.Date(), "%Y"),
printURL = FALSE, outputFormat = "wide"){
if(is.null(name))
name = indicator
url = paste("https://api.worldbank.org/v2/countries/all/indicators/", indicator,
"?date=", startDate, ":", endDate,
"&format=json&per_page=30000", sep = "")
if(printURL)
print(url)
wbData = fromJSON(url)[[2]]
wbData = data.frame(Country = sapply(wbData,
function(x) x[["country"]]["value"]),
ISO2_WB_CODE= sapply(wbData,
function(x) x[["country"]]["id"]),
Year = as.integer(sapply(wbData, "[[", "date")),
Value = as.numeric(sapply(wbData, function(x)
ifelse(is.null(x[["value"]]), NA, x[["value"]]))),
stringsAsFactors = FALSE)
if(outputFormat == "long"){
wbData$name = name
} else if(outputFormat == "wide"){
names(wbData)[4] = name
}
return(wbData)
}
|
uniroot.all <- function (f, interval, lower= min(interval),
upper= max(interval), tol= .Machine$double.eps^0.2,
maxiter= 1000, trace = 0, n = 100, ... ) {
if (!missing(interval) && length(interval) != 2)
stop("'interval' must be a vector of length 2")
if (!is.numeric(lower) || !is.numeric(upper) || lower >=
upper)
stop("lower < upper is not fulfilled")
xseq <- seq(lower,upper,len=n+1)
mod <- f(xseq,...)
Equi <- xseq[which(mod==0)]
ss <- mod[1:n]*mod[2:(n+1)]
ii <- which(ss<0)
for (i in ii)
Equi <- c(Equi, uniroot(f,lower=xseq[i],upper=xseq[i+1], maxiter = maxiter, tol = tol,
trace = trace, ...)$root)
return(Equi)
}
|
h_boot <- function (x, n.ahead, runs, ortho, cumulative, impulse, response,
ci, seed, y.names) {
if (!(is.null(seed)))
set.seed(abs(as.integer(seed)))
if (inherits(x, "varest")) {
VAR <- eval.parent(x)
}
else if (inherits(x, "svarest")) {
VAR <- eval.parent(x$var)
}
else {
stop("Bootstrap not implemented for this class.\n")
}
p <- VAR$p
K <- VAR$K
obs <- VAR$obs
total <- VAR$totobs
type <- VAR$type
B <- Bcoef_sh(VAR)
BOOT <- vector("list", runs)
ysampled <- matrix(0, nrow = total, ncol = K)
colnames(ysampled) <- names(VAR$varresult)
Zdet <- NULL
if (ncol(VAR$datamat) > (K * (p + 1))) {
Zdet <- as.matrix(VAR$datamat[, (K * (p + 1) + 1):ncol(VAR$datamat)])
}
resorig <- scale(resid(VAR), scale = FALSE)
B <- Bcoef_sh(VAR)
for (i in 1:runs) {
booted <- sample(c(1:obs), replace = TRUE)
resid <- resorig[booted, ]
lasty <- c(t(VAR$y[p:1, ]))
ysampled[c(1:p), ] <- VAR$y[c(1:p), ]
for (j in 1:obs) {
lasty <- lasty[1:(K * p)]
Z <- c(lasty, Zdet[j, ])
ysampled[j + p, ] <- B %*% Z + resid[j, ]
lasty <- c(ysampled[j + p, ], lasty)
}
varboot <- update(VAR, y = ysampled)
if (inherits(x, "svarest")) {
varboot <- update(x, x = varboot)
}
BOOT[[i]] <- h_irf(x = varboot, n.ahead = n.ahead, ortho = ortho,
cumulative = cumulative, impulse = impulse,
response = response, y.names = y.names)
}
lower <- ci/2
upper <- 1 - ci/2
mat.l <- matrix(NA, nrow = n.ahead + 1, ncol = length(response))
mat.u <- matrix(NA, nrow = n.ahead + 1, ncol = length(response))
Lower <- list()
Upper <- list()
idx1 <- length(impulse)
idx2 <- length(response)
idx3 <- n.ahead + 1
temp <- rep(NA, runs)
for (j in 1:idx1) {
for (m in 1:idx2) {
for (l in 1:idx3) {
for (i in 1:runs) {
if (idx2 > 1) {
temp[i] <- BOOT[[i]][[j]][l, m]
}
else {
temp[i] <- matrix(BOOT[[i]][[j]])[l, m]
}
}
mat.l[l, m] <- quantile(temp, lower, na.rm = TRUE)
mat.u[l, m] <- quantile(temp, upper, na.rm = TRUE)
}
}
colnames(mat.l) <- response
colnames(mat.u) <- response
Lower[[j]] <- mat.l
Upper[[j]] <- mat.u
}
names(Lower) <- impulse
names(Upper) <- impulse
result <- list(Lower = Lower, Upper = Upper)
return(result)
}
|
int_est1 <- function(x, lev) {
ec <- 0.57721566490153286
zeta3 <- 1.202056903159594285399
dum<-point_est1(x)
n<-length(x)
ah<-dum[1]
sh<-dum[2]
zcv <- qnorm(1 - (1 - lev)/2, 0, 1)
sah <- sqrt((-1 - (ah^4 - 11)/(10 * ah^2))/n)
ssh <- sqrt((((sh^2) * (-(-1 + ah^2) * (pi^2) * (3 * (11 + ah^2) *ec^2 + 5 * (ah^2) * (pi^2))
+ 360 * ah * (-1 + ah^3) * ec *(zeta3)))/(30 * (ah^2) * (pi^2)))/n)
return( rbind( c('alpha:', ah - zcv * sah, ah + zcv * sah), c('rho:', sh - zcv * ssh, sh + zcv * ssh) ) )
}
|
NULL
dim.blockmatrix <- function (x) {
return(dim(as.matrix(x$value)))
}
|
context("test Reaction as app")
source(paste(system.file("examples", package = "rODE"),
"Reaction.R", sep ="/"))
X <- 1; Y <- 5;
dt <- 0.1
reaction <- Reaction(c(X, Y, 0))
solver <- RK4(reaction)
expect_equal(solver@estimated_state, c(0, 0, 0))
expect_equal(solver@numEqn, 3)
test_that("rates are zero before step", {
expect_equal(solver@rate1, c(0, 0, 0))
expect_equal(solver@rate2, c(0, 0, 0))
expect_equal(solver@rate3, c(0, 0, 0))
expect_equal(solver@rate4, c(0, 0, 0))
})
solver <- step(solver)
test_that("get these values after one step", {
expect_equal(solver@ode@state, c(1.342695, 4.641994, 0.1), tolerance = 0.000001)
expect_equal(solver@estimated_state, c(1.345462, 4.638375, 0.100000), tolerance = 0.000001)
expect_equal(solver@numEqn, 3)
expect_equal(solver@rate1, c(2.5, -2.5, 1.0))
expect_equal(solver@rate2, c(3.232422, -3.357422, 1.000000), tolerance = 0.0000001)
expect_equal(solver@rate3, c(3.454625, -3.616246, 1.000000), tolerance = 0.0000001)
expect_equal(solver@rate4, c(4.687590, -5.033052, 1.000000), tolerance = 0.0000001)
})
while (solver@ode@state[3] <= 100.0) {
solver <- step(solver)
}
test_that("get this vector at the end of the loop", {
expect_equal(c(solver@ode@state[1], solver@ode@state[2], solver@ode@state[3]),
c(1.987618, 1.143675, 100.1))
})
|
distance <- function(box1,
box2,
type = c("vertical", "horizontal", "euclidean"),
half = FALSE,
center = FALSE) {
assert_input <- function(v) {
assert(
checkClass(v, "box"),
checkClass(v, "coords"),
checkNumeric(v),
checkTRUE(is.unit(v))
)
}
type <- match.arg(type)
if (missing(box2) && is.list(box1) && length(box1) == 2) {
box2 <- box1[[2]]
box1 <- box1[[1]]
}
assert_input(box1)
assert_input(box2)
box_coords1 <- prConvert2Coords(box1)
box_coords2 <- prConvert2Coords(box2)
type = match.arg(type)
converter_fn <- ifelse(type == "horizontal", prCnvrtX, prCnvrtY)
from = NA
to = NA
if (type == "vertical") {
if (converter_fn(box_coords1$y) > converter_fn(box_coords2$y)) {
if (center) {
from <- box_coords1$y
to <- box_coords2$y
} else {
from <- box_coords1$bottom
to <- box_coords2$top
}
} else {
if (center) {
from <- box_coords1$y
to <- box_coords2$y
} else {
from <- box_coords1$top
to <- box_coords2$bottom
}
}
ret <- converter_fn(to) - converter_fn(from)
} else if (type == "horizontal") {
if (prCnvrtX(box_coords1$x) < prCnvrtX(box_coords2$x)) {
if (center) {
from <- box_coords1$x
to <- box_coords2$x
} else {
from <- box_coords1$right
to <- box_coords2$left
}
} else {
if (center) {
from <- box_coords1$x
to <- box_coords2$x
} else {
from <- box_coords1$left
to <- box_coords2$right
}
}
ret <- converter_fn(to) - converter_fn(from)
} else if (type == "euclidean") {
ydist <- distance(box1 = box1, box2 = box2, type = "vertical", center = center)
xdist <- distance(box1 = box1, box2 = box2, type = "horizontal", center = center)
ret <- sqrt(prCnvrtY(ydist)^2 + prCnvrtX(xdist)^2)
} else {
stop("Unreachable code")
}
if (ret < 0) {
positive <- FALSE
ret <- -1 * ret
} else {
positive <- TRUE
}
if (half) {
ret <- ret / 2
}
ret <- unit(ret, "mm")
structure(
ret,
class = c("Gmisc_unit", class(ret)),
positive = positive,
from = from,
to = to,
type = type,
box_coords1 = box_coords1,
box_coords2 = box_coords2,
center = center)
}
print.Gmisc_unit <- function(x, ...) {
base_txt <- as.character(x)
repr <- paste(
base_txt,
paste0(" - positive = ", as.character(attr(x, "positive"))),
paste0(" - from ", as.character(attr(x, "from"))),
paste0(" - to ", as.character(attr(x, "to"))),
paste0(" - type: ", as.character(attr(x, "type"))),
paste0(" - center: ", as.character(attr(x, "center"))),
"",
sep = "\n")
cat(repr)
invisible(x)
}
|
nlsR2 <- function(nlsAns,
y,
p)
{
n <- length(y)
rsds <- residuals(nlsAns)
rr <- sum(rsds^2) / var(y) / (length(y) - 1)
R2 <- 1 - rr
adjR2 <- 1 - rr * (n - 1) / (n - p)
return(list(R2 = R2, adjR2 = adjR2))
}
|
context("Pretreatment functions of mixtCompLearn's parameters")
Sys.setenv(MC_DETERMINISTIC = 42)
test_that("imputModelIntern returns Gaussian when a numeric is given", {
data <- rnorm(100)
outModel <- imputModelIntern(data, name = "var")
expect_equal(outModel, "Gaussian")
})
test_that("imputModelIntern returns Poisson when an integer vector is given", {
data <- 1:100
outModel <- imputModelIntern(data, name = "var")
expect_equal(outModel, "Poisson")
})
test_that("imputModelIntern returns Multinomial when a character/factor is given", {
data <- letters
outModel <- imputModelIntern(data, name = "var")
expect_equal(outModel, "Multinomial")
data <- as.factor(letters)
outModel <- imputModelIntern(data, name = "var")
expect_equal(outModel, "Multinomial")
})
test_that("imputModelIntern returns LatentClass when the variable is named z_class", {
data <- 1:100
outModel <- imputModelIntern(data, name = "z_class")
expect_equal(outModel, "LatentClass")
})
test_that("imputModelIntern returns an error when a bad type is given", {
data <- list()
expect_error(outModel <- imputModelIntern(data, name = "var"))
})
test_that("imputModel works with data.frame", {
data <- data.frame(a = 1:3,
b = rnorm(3),
c = letters[1:3],
z_class = 1:3)
expectedModel <- list(a = list(type = "Poisson", paramStr = ""), b = list(type = "Gaussian", paramStr = ""), c = list(type = "Multinomial", paramStr = ""), z_class = list(type = "LatentClass", paramStr = ""))
outModel <- imputModel(data)
expect_equal(outModel, expectedModel)
})
test_that("imputModel works with list", {
data <- list(a = 1:3,
b = rnorm(3),
c = letters[1:3],
z_class = 1:3)
expectedModel <- list(a = list(type = "Poisson", paramStr = ""), b = list(type = "Gaussian", paramStr = ""), c = list(type = "Multinomial", paramStr = ""), z_class = list(type = "LatentClass", paramStr = ""))
outModel <- imputModel(data)
expect_equal(outModel, expectedModel)
})
test_that("imputModel returns an error with a matrix", {
data <- matrix(rnorm(50), ncol = 5, dimnames = list(NULL, letters[1:5]))
expect_error(outModel <- imputModel(data))
})
test_that("completeModel adds hyperparameters for functional data", {
model <- list(gauss = list(type = "Gaussian", paramStr = ""), func1 = list(type = "Func_CS", paramStr = "nSub: 3, nCoeff: 3"),
func2 = list(type = "Func_SharedAlpha_CS", paramStr = "nSub: 3, nCoeff: 3"), func3 = list(type = "Func_CS", paramStr = ""),
func4 = list(type = "Func_SharedAlpha_CS", paramStr = ""))
nInd <- 200
ratioPresent <- 0.95
var <- list()
var$z_class <- RMixtCompIO:::zParam()
var$func1 <- RMixtCompIO:::functionalInterPolyParam("func1")
var$func2 <- RMixtCompIO:::functionalInterPolyParam("func2")
var$func3 <- RMixtCompIO:::functionalInterPolyParam("func3")
var$func4 <- RMixtCompIO:::functionalInterPolyParam("func4")
data <- RMixtCompIO:::dataGeneratorNewIO(nInd, ratioPresent, var)$data
expect_warning(out <- completeModel(model, data))
expect_equal(out, list(gauss = list(type = "Gaussian", paramStr = ""), func1 = list(type = "Func_CS", paramStr = "nSub: 3, nCoeff: 3"),
func2 = list(type = "Func_SharedAlpha_CS", paramStr = "nSub: 3, nCoeff: 3"), func3 = list(type = "Func_CS", paramStr = "nSub: 2, nCoeff: 2"),
func4 = list(type = "Func_SharedAlpha_CS", paramStr = "nSub: 2, nCoeff: 2")))
})
test_that("formatDataBasicMode works with data.frame", {
dat <- data.frame(a = rnorm(20), b = as.character(rep(letters[1:2], 10)), c = as.factor(rep(letters[2:1], 10)), d = 1:20, z_class = letters[1:20])
dat[1,] = NA
model <- list(a = list(type = "Gaussian"), b = list(type = "Multinomial"), c = list(type = "Multinomial"), d = list(type = "Poisson"), z_class = list(type = "LatentClass"))
out <- formatDataBasicMode(dat, model)
expect_length(out, 2)
expect_named(out, c("data", "dictionary"))
expect_type(out$data, "list")
expect_named(out$data, c("a", "b", "c", "d", "z_class"))
expect_equal(out$data$a, c("?", as.character(dat$a[-1])))
expect_equal(out$data$b, c("?", rep(2:1, 9), 2))
expect_equal(out$data$c, c("?", rep(1:2, 9), 1))
expect_equal(out$data$d, c("?", as.character(dat$d[-1])))
expect_equal(out$data$z_class, c("?", 1:19))
expect_type(out$dictionary, "list")
expect_length(out$dictionary, 3)
expect_named(out$dictionary, c("b", "c", "z_class"))
expect_equal(out$dictionary$b, list(old = sort(letters[2:1]), new = c("1", "2")))
expect_equal(out$dictionary$c, list(old = sort(letters[2:1]), new = c("1", "2")))
expect_equal(out$dictionary$z_class, list(old = sort(letters[2:20]), new = as.character(1:19)))
})
test_that("formatDataBasicMode works with list", {
dat <- list(a = rnorm(20), b = as.character(rep(letters[1:2], 10)), c = as.factor(rep(letters[2:1], 10)), d = 1:20, z_class = letters[1:20])
dat$a[1] = NA
dat$b[1] = NA
dat$c[1] = NA
dat$d[1] = NA
dat$z_class[1] = NA
model <- list(a = list(type = "Gaussian"), b = list(type = "Multinomial"), c = list(type = "Multinomial"), d = list(type = "Poisson"), z_class = list(type = "LatentClass"))
out <- formatDataBasicMode(dat, model)
expect_length(out, 2)
expect_named(out, c("data", "dictionary"))
expect_type(out$data, "list")
expect_named(out$data, c("a", "b", "c", "d", "z_class"))
expect_equal(out$data$a, c("?", as.character(dat$a[-1])))
expect_equal(out$data$b, c("?", rep(2:1, 9), 2))
expect_equal(out$data$c, c("?", rep(1:2, 9), 1))
expect_equal(out$data$d, c("?", as.character(dat$d[-1])))
expect_equal(out$data$z_class, c("?", 1:19))
expect_type(out$dictionary, "list")
expect_length(out$dictionary, 3)
expect_named(out$dictionary, c("b", "c", "z_class"))
expect_equal(out$dictionary$b, list(old = sort(letters[2:1]), new = c("1", "2")))
expect_equal(out$dictionary$c, list(old = sort(letters[2:1]), new = c("1", "2")))
expect_equal(out$dictionary$z_class, list(old = sort(letters[2:20]), new = as.character(1:19)))
})
test_that("formatDataBasicMode works with a dictionary", {
dat <- list(a = rnorm(20), b = as.character(rep(letters[1:2], 10)), c = as.factor(rep(letters[2:1], 10)), d = 1:20, z_class = 1:20)
dat$a[1] = NA
dat$b[1] = NA
dat$c[1] = NA
dat$d[1] = NA
dat$z_class[1] = NA
model <- list(a = list(type = "Gaussian"), b = list(type = "Multinomial"), c = list(type = "Multinomial"), d = list(type = "Poisson"), z_class = list(type = "LatentClass"))
dictionary <- list(b = list(old = c("a", "b"), new = c("1", "2")),
c = list(old = c("a", "b"), new = c("1", "2")))
out <- formatDataBasicMode(dat, model, dictionary)
expect_length(out, 2)
expect_named(out, c("data", "dictionary"))
expect_type(out$data, "list")
expect_named(out$data, c("a", "b", "c", "d", "z_class"))
expect_equal(out$data$a, c("?", as.character(dat$a[-1])))
expect_equal(out$data$b, c("?", "2", rep(c("1", "2"), 9)))
expect_equal(out$data$c, c("?", "1", rep(c("2", "1"), 9)))
expect_equal(out$data$d, c("?", as.character(dat$d[-1])))
expect_equal(out$data$z_class, c("?", as.character(dat$z_class[-1])))
expect_equal(out$dictionary, dictionary)
dictionary$b = NULL
expect_error(out <- formatDataBasicMode(dat, model, dictionary))
})
test_that("checkNClass works with mixtComp object", {
resLearn <- list(algo = list(nClass = 2))
class(resLearn) = "MixtComp"
nClass <- NULL
expect_warning(out <- checkNClass(nClass, resLearn), regexp = NA)
expect_equal(out, 2)
nClass <- 3
expect_warning(out <- checkNClass(nClass, resLearn))
expect_equal(out, 2)
nClass <- 2:4
expect_warning(out <- checkNClass(nClass, resLearn))
expect_equal(out, 2)
nClass <- 3:4
expect_warning(out <- checkNClass(nClass, resLearn))
expect_equal(out, 2)
})
test_that("checkNClass works with mixtCompLearn object", {
resLearn <- list(algo = list(nClass = 2), nClass = 2:5)
class(resLearn) = c("MixtCompLearn", "MixtComp")
nClass <- NULL
expect_warning(out <- checkNClass(nClass, resLearn), regexp = NA)
expect_equal(out, 2)
nClass <- 3
expect_warning(out <- checkNClass(nClass, resLearn), regexp = NA)
expect_equal(out, 3)
nClass <- 3:4
expect_warning(out <- checkNClass(nClass, resLearn))
expect_equal(out, 3)
nClass <- 6:8
expect_warning(out <- checkNClass(nClass, resLearn))
expect_equal(out, 2)
})
test_that("performHierarchical works", {
model <- list("a" = list(type = "Gaussian"))
mode <- "basic"
for(hierarchicalMode in c("yes", "no", "auto"))
{
out <- performHierarchical(hierarchicalMode, mode, model)
expect_false(out)
}
mode = "expert"
out <- performHierarchical(hierarchicalMode = "yes", mode, model)
expect_true(out)
for(hierarchicalMode in c("no", "auto"))
{
out <- performHierarchical(hierarchicalMode, mode, model)
expect_false(out)
}
model$b = list(type = "Func_CS")
out <- performHierarchical(hierarchicalMode = "no", mode, model)
expect_false(out)
for(hierarchicalMode in c("yes", "auto"))
{
out <- performHierarchical(hierarchicalMode, mode, model)
expect_true(out)
}
})
test_that("changeClassNames works", {
rowNames <- c("k: 1, lambda", "k: 2, mean", "k: 2, sd", "k: 3, modality: 1", "k: 3, modality: 2", "k: 4, n", "k: 4, p", "k: 5, k", "k: 6, s: 0, alpha0",
"k: 7, s: 0, alpha1", "k: 8, s: 0, c: 0", "k: 9, s: 0", "k: 10, pi")
dictionary <- list(z_class = list(old = paste0("G", 1:10), new = 1:10))
out <- changeClassName(rowNames, dictionary)
expect_equal(out, c("k: G1, lambda", "k: G2, mean", "k: G2, sd", "k: G3, modality: 1", "k: G3, modality: 2", "k: G4, n", "k: G4, p", "k: G5, k", "k: G6, s: 0, alpha0",
"k: G7, s: 0, alpha1", "k: G8, s: 0, c: 0", "k: G9, s: 0", "k: G10, pi"))
})
test_that("formatOutputBasicMode works", {
dictionary <- list(z_class = list(old = c("setosa", "versicolor", "virginica"),
new = c("1", "2", "3")),
categ1 = list(old = c("a", "b"), new = c("1", "2")))
res0 <- list(algo = list(),
variable = list(type = list(z_class = "LatentClass", categ1 = "Multinomial"),
data = list(z_class = list(completed = c(3, 3, 1, 2),
stat = matrix(NA, nrow = 1, ncol = 3, dimnames = list(NULL, c("k: 1", "k: 2", "k: 3")))),
categ1 = list(completed = c(2, 1, 2))),
param = list(z_class = list(stat = matrix(NA, nrow = 3, ncol = 3, dimnames = list(c("k: 1", "k: 2", "k: 3"), NULL)),
log = matrix(NA, nrow = 3, ncol = 2, dimnames = list(c("k: 1", "k: 2", "k: 3"), NULL))),
categ1 = list(stat = matrix(NA, nrow = 6, ncol = 3, dimnames = list(c("k: 1, modality: 1", "k: 1, modality: 2",
"k: 2, modality: 1", "k: 2, modality: 2",
"k: 3, modality: 1", "k: 3, modality: 2"), NULL)),
log = matrix(NA, nrow = 6, ncol = 2, dimnames = list(c("k: 1, modality: 1", "k: 1, modality: 2",
"k: 2, modality: 1", "k: 2, modality: 2",
"k: 3, modality: 1", "k: 3, modality: 2"), NULL)))
)))
res <- formatOutputBasicMode(res0, dictionary)
expect_equal(res$algo$dictionary, dictionary)
expect_equal(res$variable$data$z_class$completed, c("virginica", "virginica", "setosa", "versicolor"))
expect_equal(colnames(res$variable$data$z_class$stat), c("k: setosa", "k: versicolor", "k: virginica"))
expect_equal(res$variable$data$categ1$completed, c("b", "a", "b"))
expect_equal(rownames(res$variable$param$z_class$stat), c("k: setosa", "k: versicolor", "k: virginica"))
expect_equal(rownames(res$variable$param$z_class$log), c("k: setosa", "k: versicolor", "k: virginica"))
expect_equal(rownames(res$variable$param$categ1$stat), c("k: setosa, modality: a", "k: setosa, modality: b",
"k: versicolor, modality: a", "k: versicolor, modality: b",
"k: virginica, modality: a", "k: virginica, modality: b"))
expect_equal(rownames(res$variable$param$categ1$log), c("k: setosa, modality: a", "k: setosa, modality: b",
"k: versicolor, modality: a", "k: versicolor, modality: b",
"k: virginica, modality: a", "k: virginica, modality: b"))
})
Sys.unsetenv("MC_DETERMINISTIC")
|
minimal_model_MRMC <- function() {
m <-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3
,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5
,5,5,5,5,5,5,5,5,5,5,5,5)
q <-c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1
,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1,1,2,2,2
,2,2,3,3,3,3,3,4,4,4,4,4)
c<-c(5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2
,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3
,2,1,5,4,3,2,1,5,4,3,2,1)
f<-c(
0,4,20,29,21,0,0,6,15,22,1,15,18,31,19,1,2,4,16,17,1,1,21,24,23,1,1,5,30
,40,2,19,31,56,42,2,0,2,30,32,1,7,13,28,19,0,1,7,7,31,7,15,28,41,9,0,2,5
,24,31,1,4,18,21,23,1,1,0,11,35,6,14,37,36,18,0,2,4,18,25,0,2,19,23,18,0,2
,6,10,30,2,25,40,29,24,1,1,4,24,32
)
h<-c(
50,30,11,5,1,15,29,29,1,0,39,31,8,10,3,10,8,25,45,14,52,25,13,4,1,27,28,29,1
,0,53,29,13,2,4,9,16,22,43,14,43,29,11,6,0,18,29,21,0,0,43,29,6,7,1,10,14,19
,32,23,61,19,12,9,3,16,29,34,1,0,52,29,10,4,3,10,16,23,43,15,35,29,18,9,0,17,27
,24,0,0,34,33,7,13,2,12,16,21,35,15
)
C<-5
M<-5
Q<-4
NI<-199
NL<-142
N <-C*M*Q
ff <- numeric(N)
harray<-array(0,dim=c(C,M,Q));
for(md in 1:M) {
for(cd in 1:C) {
for(qd in 1 : Q){
for(n in 1:cd){
ff[cd+(md-1)*C*Q+(qd-1)*C]<-ff[cd+(md-1)*C*Q+(qd-1)*C]+f[n+(md-1)*C*Q+(qd-1)*C]
}
harray[cd,md,qd] <- h[cd+(md-1)*C*Q+(qd-1)*C]
}}}
data <- list(N=N,Q=Q, M=M,m=m ,C=C , NL=NL,NI=NI
,c=c,q=q,
h=h, f=f,
ff=ff,
harray=harray,ModifiedPoisson=FALSE
)
Stan.model <- rstan::stan_model(
model_code="
data{
int <lower=0>N;
int <lower=0>M;
int <lower=0>C;
int <lower=0>Q;
int <lower=0>h[N];
int <lower=0>f[N];
int <lower=0>q[N];
int <lower=0>c[N];
int <lower=0>m[N];
int <lower=0>NL;
int <lower=0>NI;
int <lower=0>ff[N];
int <lower=0>harray[C,M,Q];
int ModifiedPoisson;//////Logical
}
transformed data {
int <lower=0> NX;
if(ModifiedPoisson==0) NX = NI;
if(ModifiedPoisson==1) NX =NL;
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
print(\" 2019 Dec 25 Non hierarchical MRMC model \")
}
parameters{
real w;
real <lower =0 > dz[C-1];
real mu[M,Q];
real <lower=0> v[M,Q];
}
transformed parameters {
real <lower =0> dl[C];
real <lower=0,upper=1> ppp[C,M,Q];
real <lower =0> l[C];
real z[C];
real aa[M,Q];
real <lower =0> bb[M,Q];
real <lower=0,upper=1> AA[M,Q];
real deno[C-1,M,Q];
real hit_rate[C,M,Q];
real <lower=0,upper=1>A[M];
z[1]=w;
for(md in 1 : M) {
for(qd in 1 : Q) {
aa[md,qd]=mu[md,qd]/v[md,qd];
bb[md,qd]=1/v[md,qd];
for(cd in 1 : C-1) z[cd+1] = z[cd] + dz[cd];
ppp[C,md,qd] = 1- Phi((z[C] -mu[md,qd])/v[md,qd]);
for(cd in 1 : C-1) ppp[cd,md,qd] = Phi((z[cd+1] -mu[md,qd])/v[md,qd]) - Phi((z[cd ] -mu[md,qd])/v[md,qd]);
for(cd in 1 : C) l[cd] = (-1)*log(Phi(z[cd]));
dl[C] = fabs(l[C]-0);
for(cd in 1:C-1) dl[cd]= fabs(l[cd]-l[cd+1]);
}
}
for(md in 1 : M) {
for(qd in 1 : Q) {
AA[md,qd]=Phi( (mu[md,qd]/v[md,qd])/sqrt((1/v[md,qd])^2+1) );//////Measures of modality performance
}}
for(md in 1 : M) {
A[md] = 0;
for(qd in 1 : Q) {
A[md] = A[md] + AA[md,qd];
}
A[md]= A[md]/Q;
}
for(md in 1 : M) {
for(qd in 1 : Q) {
deno[C-1,md,qd]=1-ppp[C,md,qd];
for(cd in 3:C){ deno[c[cd],md,qd]=deno[c[cd-1],md,qd]-ppp[c[cd-1],md,qd]; }
}}
for(md in 1 : M) {
for(qd in 1 : Q) {
for(cd in 1:C-1){
hit_rate[cd,md,qd]=ppp[cd,md,qd]/deno[cd,md,qd];
}
hit_rate[C,md,qd]=ppp[C,md,qd];
}}
}
model{
int s=0;
for(n in 1:N) {
target += poisson_lpmf(ff[n]|l[c[n]]*NX);
}
for(qd in 1 : Q) {
for(md in 1 : M) {
s=0;
for(cd in 1 : C){
target += binomial_lpmf(harray[cd,md,qd] | NL-s, hit_rate[c[cd],md,qd] );
s = s + harray[cd,md,qd]; }
}}
w ~ uniform(-3,3);
for(cd in 1:C-1) dz[cd] ~ uniform(0.001,7);
for(md in 1 : M) { for(qd in 1 : Q) {
mu[md,qd] ~ uniform(-11,11);
v[md,qd] ~ uniform(0.01,11);
}}
}
")
fit <- rstan::sampling(
object= Stan.model, data=data, verbose = FALSE,
seed=1, chains=1, warmup=11, iter=111,
sample_file =paste0(file.path(Sys.getenv("USERPROFILE"),"Desktop"),"\\samples"),
control = list(adapt_delta = 0.9999999,
max_treedepth = 15)
)
rstan::traceplot(fit,pars=c("w"))
rstan::check_hmc_diagnostics(fit)
}
|
termExtraction <- function(M, Field="TI", ngrams = 1, stemming=FALSE, language="english",remove.numbers=TRUE, remove.terms=NULL, keep.terms=NULL, synonyms=NULL, verbose=TRUE){
if (Field %in% c("ID","DE")){ngrams <- 1}
data("stopwords",envir=environment())
data("stop_words", envir=environment(), package = "tidytext")
stop_words <- stop_words %>% as.data.frame()
if (ngrams == 2){remove.terms <- c(remove.terms,stopwords$bigrams)}
switch(language,
english={stopwords=(stop_words$word)},
italian={stopwords=stopwords$it},
german={stopwords=stopwords$de},
french={stopwords=stopwords$fr},
spanish={stopwords=stopwords$es}
)
stopwords <- tolower(stopwords)
TERMS <- M %>%
select(.data$SR,!!Field)
names(TERMS) <- c("SR","text")
if (Field %in% c("ID","DE")){
listTerms <- strsplit(TERMS$text,";")
TERMS$text <- unlist(lapply(listTerms, function(l){
l <- tolower(paste(gsub(" ","_",trimES(trimws(l))), sep="", collapse=";"))
}))
} else {
TERMS <- TERMS %>%
mutate(text = tolower(gsub("[^[:alnum:][:blank:]\\-]", "", .data$text)),
text = gsub("-", "_",.data$text))
}
if (remove.numbers==TRUE){
TERMS <- TERMS %>%
mutate(text = gsub("[[:digit:]]","",.data$text))
}
if (length(keep.terms)>0 & class(keep.terms)=="character"){
keep.terms <- tolower(keep.terms)
if (Field %in% c("DE","ID")){
kt <- gsub(" |-","_",keep.terms)
} else {
kt <- gsub("-","_",keep.terms)
}
for (i in 1:length(keep.terms)){
TERMS <- TERMS %>%
mutate(text = gsub(keep.terms[i],kt[i],.data$text))
}
}
if (Field %in% c("ID","DE")){
TERMS <- TERMS %>%
mutate(text = gsub("_|-", " ", .data$text))
}
if (is.null(remove.terms)) remove.terms <- ""
TERMS <- extractNgrams(text=TERMS, Var="text", nword=ngrams,
stopwords=stopwords, custom_stopwords=tolower(remove.terms),
stemming=stemming, language=language, synonyms = synonyms)
TERMS <- TERMS %>%
dplyr::filter(!(.data$ngram %in% paste(rep("NA",ngrams),sep="",collapse=" "))) %>%
group_by(.data$SR) %>%
summarize(text = paste(.data$ngram, collapse=";"))
if (Field %in% c("ID","DE")){
TERMS <- TERMS %>%
mutate(text = gsub("_", " ", .data$text))
}
col_name <- paste(Field,"_TM",sep="")
M <- M[!names(M) %in% col_name]
M <- TERMS %>%
right_join(M, by = "SR")
names(M)[which(names(M) %in% "text")] <- col_name
if (verbose==TRUE){
s <- tableTag(M,col_name)
if (length(s>25)){print(s[1:25])}else{print(s)}
}
class(M) <- c("bibliometrixDB", "data.frame")
row.names(M) <- M$SR
return(M)
}
extractNgrams <- function(text, Var, nword, stopwords, custom_stopwords, stemming, language, synonyms){
stopwords <- c(stopwords,"elsevier", "springer", "wiley", "mdpi", "emerald")
custom_stopngrams <- c(custom_stopwords,"rights reserved", "john wiley", "john wiley sons", "science bv", "mdpi basel",
"mdpi licensee", "emerald publishing", "taylor francis", "paper proposes",
"we proposes", "paper aims", "articles published", "study aims")
ngram <- NULL
ngrams <- text %>%
drop_na(any_of(Var)) %>%
unnest_tokens(ngram, !!Var, token = "ngrams", n = nword) %>%
separate(.data$ngram, paste("word",1:nword,sep=""), sep = " ") %>%
dplyr::filter(if_all(starts_with("word"), ~ !.x %in% stopwords))
if (isTRUE(stemming)){
ngrams <- ngrams %>%
mutate(across(paste("word",1:nword,sep=""), ~SnowballC::wordStem(.x,language=language)))
}
ngrams <- ngrams %>%
unite(ngram, paste("word",1:nword,sep=""), sep = " ") %>%
dplyr::filter(!.data$ngram %in% custom_stopngrams) %>%
mutate(ngram = toupper(.data$ngram))
if (length(synonyms)>0 & class(synonyms)=="character"){
s <- strsplit(toupper(synonyms),";")
snew <- trimws(unlist(lapply(s,function(l) l[1])))
sold <- (lapply(s,function(l) trimws(l[-1])))
for (i in 1:length(s)){
ngrams <- ngrams %>%
mutate(
ngram = str_replace_all(.data$ngram, paste(sold[[i]], collapse="|",sep=""),snew[i])
)
}
}
return(ngrams)
}
|
bestset.noise <-
function (m = 100, n = 40, method = "exhaustive", nvmax = 3,
X = NULL, y=NULL, intercept=TRUE,
print.summary = TRUE, really.big = FALSE, ...)
{
leaps.out <- try(requireNamespace("leaps", quietly=TRUE), silent = TRUE)
if ((is.logical(leaps.out) == TRUE) & (leaps.out == TRUE)) {
if (is.null(X)) {
X <- matrix(rnorm(m * n), ncol = n)
colnames(X) <- paste("V", 1:n, sep = "")
}
else {
if(is.data.frame(X)){
if(intercept) X <- model.matrix(~., data=X)[,-1] else
X <- model.matrix(~-1+., data=X)
}
m <- dim(X)[1]
n <- dim(X)[2]
}
if (is.null(colnames(X)))
colnames(X) <- paste("V", 1:n, sep = "")
if(is.null(y))y <- rnorm(m)
u <- leaps::regsubsets(X, y, method = method, nvmax = nvmax,
nbest = 1, intercept=intercept, really.big = really.big,
...)
if(is.null(intercept))intercept <- TRUE
if(intercept){
x <- X[, summary(u)$which[nvmax, -1]]
u1 <- lm(y ~ x)} else {
x <- X[, summary(u)$which[nvmax, ]]
u1 <- lm(y ~ -1+x)}
if (print.summary)
print(summary(u1, corr = FALSE))
invisible(list(best=u1, regsubsets_obj=u))
}
else {
print("Error: package leaps is not installed properly")
}
}
|
rand_profile <- function(df, grouping = "pop", population = NULL, n = FALSE, keep_pop = FALSE){
meta <- NULL
pop <- NULL
x0 <- NULL
X0 <- NULL
x1 <- NULL
locus <- NULL
p <- NULL
p0 <- NULL
p1 <- NULL
p2 <- NULL
. <- NULL
stopifnot(length(population) <= 1)
grouping <- match.arg(grouping, c("pop", "meta"))
grouping_ <- quo(!!sym(grouping))
df <- df %>% select(starts_with(grouping)) %>%
distinct(!!grouping_, .keep_all = TRUE) %>% unnest(cols = ends_with("data")) %>%
ungroup()
if(grouping == "meta") df <- df %>% rename(pop = meta)
if(!is.null(population)){
if(any(population %in% df$pop)) df <- filter_(df, .dots = paste0("pop == '", population,"'"))
else cat(paste0("Population: '",population,"' is not in database with grouping '",grouping,"'"))
}
else{
pops_n <- df %>% select(pop, n) %>% distinct()
if(!n) pops_n <- mutate(pops_n, n = 1)
pops_n <- pops_n %>% sample_n(size = 1, replace = FALSE, weight = n) %>% select(pop)
df <- right_join(df, pops_n, by = "pop")
}
x0_profile <- df %>% filter(x0 == 0, X0 == 0) %>%
mutate(p = x1/n) %>%
select(pop, locus, p) %>%
mutate(x0 = rbinom(n = nrow(.), size = 2, prob = p)) %>%
select(pop, locus, x0)
df <- inner_join(df, x0_profile, by = c("pop", "locus", "x0"))
x0_x_profile <- df %>% filter(X0 == 0) %>%
bind_cols(dist_x0_cond_x_(n = .$n, x = .$x0 + .$x1)) %>%
rowwise() %>%
mutate(X0 = sample(0:2, size = 1, prob = c(p0, p1, p2))) %>%
select(pop, locus, x0, X0) %>% ungroup()
if(keep_pop) return(x0_x_profile)
x0_x_profile %>% select(-pop)
}
random_AIMs_profile <- function(df, grouping = "pop", population = NULL, n = FALSE, keep_pop = FALSE){
rand_profile(df = df, grouping = grouping, population = population, n = n, keep_pop = keep_pop)
}
|
globalVariables('priors')
GQD.mcmc <-
function(X,time,mesh=10,theta,sds,updates,burns=min(round(updates/2),25000),Dtype='Saddle',Trunc=c(4,4),RK.order=4,P=200,alpha=0,lower=min(na.omit(X))/2,upper=max(na.omit(X))*2,exclude=NULL,plot.chain=TRUE,Tag=NA,wrt=FALSE,print.output=TRUE,palette='mono')
{
solver =function(Xs, Xt, theta, N , delt , N2, tt , P , alpha, lower , upper, tro ){}
rm(list =c('solver'))
theta = theta+runif(length(theta),0.01,0.02)*sign(theta)
adapt=0
check_for_model=function()
{
txt=''
namess=c('G0','G1','G2','Q0','Q1','Q2')
func.list=rep(0,length(namess))
obs=objects(pos=1)
for(i in 1:length(namess))
{
if(sum(obs==namess[i])){func.list[i]=1}
}
check=F
if(sum(func.list)==0)
{
txt='
--------------------------------------------------------------------------------
No model has been defined yet! Try for example:
--------------------------------------------------------------------------------
GQD.remove()
G0=function(t){theta[1]*theta[2]}
G1=function(t){-theta[1]}
Q1=function(t){theta[3]*theta[3]}
model=GQD.mcmc(X,time,10,theta =rep(1,3),sds=rep(0.1,3),updates=10000)
--------------------------------------------------------------------------------
'
check=T
}
if((sum(func.list)>0)&&(sum(func.list[-c(1:3)])==0))
{
txt='
--------------------------------------------------------------------------------
At least one diffusion coefficient has to be defined! Try for example:
--------------------------------------------------------------------------------
GQD.remove()
G0=function(t){theta[1]*theta[1]}
model=GQD.mcmc(X,time,10,theta =c(0.1),sds=0.1,updates=10000)
--------------------------------------------------------------------------------
'
check=T
}
return(list(check=check,txt=txt))
}
check_for=check_for_model()
if(check_for[[1]]){stop(check_for[[2]])}
theta = theta+runif(length(theta),0.001,0.002)*sign(theta)
pow=function(x,p)
{
x^p
}
prod=function(a,b){a*b}
T.seq=time
TR.order=Trunc[1]
DTR.order=Trunc[2]
Dtypes =c('Saddle','Normal','Gamma','InvGamma','Beta')
Dindex = which(Dtypes==Dtype)
IntRange = c(lower,upper)
IntDelta =1/100
Xtr = min(IntRange)
b1 = '\n==============================================================================\n'
b2 = '==============================================================================\n'
warn=c(
'1. Missing input: Argument {X} is missing.\n'
,'2. Missing input: Argument {time} is missing.\n'
,'3. Missing input: Argument {theta} is missing.\n'
,'4. Missing input: Argument {sds} is missing.\n'
,'5. Input type: Argument {X} must be of type vector!.\n'
,'6. Input type: Argument {time} must be of type vector!.\n'
,'7. Input: Less starting parameters than model parameters.\n'
,'8. Input: More starting parameters than model parameters.\n'
,'9. Input: length(X) must be > 10.\n'
,'10. Input: length(time) must be > 10.\n'
,'11. Input: length(lower)!=1.\n'
,'12. Input: length(upper)!=1.\n'
,'13. Input: length(P)!=1.\n'
,'14. Input: length(mesh)!=1.\n'
,'15. Input: length(alpha)!=1.\n'
,'16. Input: length(Trunc)!=1.\n'
,'17. Input: length(RK.order)!=1.\n'
,'18. Density: Dtype has to be one of Saddle, Normal, Gamma, InvGamma or Beta.\n'
,'19. Density: Range [lower,upper] must be strictly positive for Dtype Gamma or InvGamma.\n'
,'20. Density: Dtype cannot be Beta for observations not in (0,1).\n'
,'21. Density: Argument {upper} must be > {lower}.\n'
,'22. Density: P must be >= 10.\n'
,'23. Density: Trunc[2] must be <= Trunc[1].\n'
,'24. ODEs : Large max(diff(time))/mesh may result in poor approximations. Try larger mesh.\n'
,'25. ODEs : max(diff(time))/mesh must be <1.\n'
,'26. ODEs : Runge Kutta scheme must be of order 4 or 10.\n'
,'27. ODEs : Argument {mesh} must be >= 5.\n'
,'28. Input: length(X)!=length(time).\n'
,'29. MCMC : Argument {burns} must be < {updates}.\n'
,'30. MCMC : Argument {updates} must be > 2.\n'
,'31. MCMC : length(theta)!=length(sds).\n'
,'32. Model: There has to be at least one model coefficient.\n'
,'33. Input: length(updates)!=1.\n'
,'34. Input: length(burns)!=1.\n'
,'35. Prior: priors(theta) must return a single value.\n'
,'36. Input: NAs not allowed.\n'
,'37. Input: length(Dtype)!=1.\n'
,'38. Input: NAs not allowed.\n'
,'39. Input: Time series contains values of small magnitude.\n This may result in numerical instabilities.\n It may be advisable to scale the data by a constant factor.\n'
)
warntrue = rep(F,40)
check.thetas = function(theta,tt)
{
t=tt
theta = theta+runif(length(theta),0.001,0.002)*sign(theta)
namess=c('G0','G1','G2','Q0','Q1','Q2')
func.list=rep(0,length(namess))
obs=objects(pos=1)
for(i in 1:length(namess))
{
if(sum(obs==namess[i])){func.list[i]=1}
}
pers.represented = rep(0,length(theta))
for(i in which(func.list==1))
{
for(j in 1:length(theta))
{
dresult1=eval(body(namess[i]))
theta[j] = theta[j]+runif(1,0.1,0.2)
dresult2=eval(body(namess[i]))
dff = abs(dresult1-dresult2)
if(any(round(dff,6)!=0)){pers.represented[j]=pers.represented[j]+1}
}
}
return(pers.represented)
}
check.thetas2 = function(theta)
{
namess=c('G0','G1','G2','Q0','Q1','Q2')
func.list=rep(0,length(namess))
obs=objects(pos=1)
for(i in 1:length(namess))
{
if(sum(obs==namess[i])){func.list[i]=1}
}
l=0
for(k in which(func.list==1))
{
str=body(namess[k])[2]
for(i in 1:length(theta))
{
for(j in 1:20)
{
str=sub(paste0('theta\\[',i,'\\]'),'clear',str)
}
}
l=l+length(grep('theta',str))
l
}
return(l)
}
if(missing(X)) {warntrue[1]=T}
if(missing(time)) {warntrue[2]=T}
if(missing(theta)) {warntrue[3]=T}
if(missing(sds)) {warntrue[4]=T}
if(!is.vector(X)) {warntrue[5]=T}
if(!is.vector(time)) {warntrue[6]=T}
if(check.thetas2(theta)!=0) {warntrue[7]=T}
if(!warntrue[7]){if(any(check.thetas(theta,T.seq)==0)) {warntrue[8]=T}}
if(length(X)<10) {warntrue[9]=T}
if(length(time)<10) {warntrue[10]=T}
if(length(lower)>1) {warntrue[11]=T}
if(length(upper)>1) {warntrue[12]=T}
if(length(P)!=1) {warntrue[13]=T}
if(length(mesh)!=1) {warntrue[14]=T}
if(length(alpha)!=1) {warntrue[15]=T}
if(length(Trunc)!=2) {warntrue[16]=T}
if(length(RK.order)!=1) {warntrue[17]=T}
if(length(updates)!=1) {warntrue[33]=T}
if(length(burns)!=1) {warntrue[34]=T}
if(length(Dtype)!=1) {warntrue[37]=T}
if(sum(Dindex)==0) {warntrue[18] =T}
if(!warntrue[18])
{
if((Dindex==3)|(Dindex==4)){if(lower[1]<=0) {warntrue[19] =T}}
if(Dindex==5){if(any(X<=0)|any(X>=1)) {warntrue[20] =T}}
}
if(!any(warntrue[c(11,12)])){if(upper<=lower) {warntrue[21] =T}}
if(!warntrue[13]){if(P<10) {warntrue[22] =T}}
if(!warntrue[16]){if(Trunc[2]>Trunc[1]) {warntrue[23] =T}}
excl=0
if(is.null(exclude)){excl=length(T.seq)-1+200}
if(!is.null(exclude)){excl=exclude}
test.this =max(diff(T.seq)[-excl])/mesh
if(test.this>0.1) {warntrue[24]=T}
if(test.this>=1) {warntrue[25]=T}
if(!warntrue[17]){if(!((RK.order==4)|(RK.order==10))) {warntrue[26]=T}}
if(!warntrue[14]){if(mesh<5) {warntrue[27]=T}}
if(length(X)!=length(time)) {warntrue[28]=T}
if(!any(warntrue[c(33,34)])){if(burns>updates) {warntrue[29]=T}}
if(!warntrue[33]){if(updates<2) {warntrue[30]=T}}
if(length(theta)!=length(sds)) {warntrue[31]=T}
if(any(is.na(X))||any(is.na(time))) {warntrue[36]=T}
if(any(warntrue))
{
prnt = b1
for(i in which(warntrue))
{
prnt = paste0(prnt,warn[i])
}
prnt = paste0(prnt,b2)
stop(prnt)
}
if(any(X<10^-2)){warntrue[39]=T}
if(any(warntrue))
{
prnt = b1
for(i in which(warntrue))
{
prnt = paste0(prnt,warn[i])
}
prnt = paste0(prnt,b2)
warning(prnt)
}
nnn=length(X)
homo=T
homo.res=T
delt=(diff(T.seq)/mesh)[1]
t=T.seq
if(is.null(exclude))
{
if(sum(round(diff(T.seq)-diff(T.seq)[1],10)==0)!=length(T.seq)-1){homo.res=F}
}
if(!is.null(exclude))
{
if(sum(round(diff(T.seq)[-excl]-c(diff(T.seq)[-excl])[1],10)==0)!=length(T.seq)-1-length(excl)){homo.res=F}
}
if(sum(objects(pos=1)=='priors')==1)
{
pp=function(theta){}
body(pp)=parse(text =body(priors)[2])
prior.list=paste0('d(theta)',':',paste0(body(priors)[2]))
if(length(priors(theta))!=1){stop(" ==============================================================================
Incorrect input: Prior distribution must return a single value only!
==============================================================================");}
}
if(sum(objects(pos=1)=='priors')==0)
{
prior.list=paste0('d(theta)',':',' None.')
pp=function(theta){1}
}
namess=c('G0','G1','G2','Q0','Q1','Q2')
func.list=rep(0,6)
obs=objects(pos=1)
for(i in 1:6)
{
if(sum(obs==namess[i])){func.list[i]=1}
}
state1=(sum(func.list[c(3,5,6)]==1)==0)
if(state1){DTR.order=2;TR.order=2;sol.state='Normally distributed diffusion.';}
if((state1&(Dtype!='Saddle'))){TR.order=2;DTR.order=2;sol.state='2nd Ord. Truncation + Std Normal Dist.';}
state2=!state1
if(state2)
{
state2.types=c('Saddlepoint Appr. ',
' Ext. Normal Appr. ',
' Ext. Gamma Appr. ',
' Ext. Inv. Gamma Appr.')
sol.state=paste0(TR.order,' Ord. Truncation +',DTR.order,'th Ord. ',state2.types[Dindex])
}
if(TR.order==2)
{
fpart=
'
using namespace arma;
using namespace Rcpp;
using namespace R;
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::export]]
vec prod(vec a,vec b)
{
return(a%b);
}
mat f(mat a,vec theta,vec t,int N2)
{
mat atemp(N2,2);'
}
if(TR.order==4)
{
fpart=
'
using namespace arma;
using namespace Rcpp;
using namespace R;
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::export]]
vec prod(vec a,vec b)
{
return(a%b);
}
mat f(mat a,vec theta,vec t,int N2)
{
mat atemp(N2,4);'
}
if(TR.order==6)
{
fpart=
'
using namespace arma;
using namespace Rcpp;
using namespace R;
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::export]]
vec prod(vec a,vec b)
{
return(a%b);
}
mat f(mat a,vec theta,vec t,int N2)
{
mat atemp(N2,6);'
}
if(TR.order==8)
{
fpart=
'
using namespace arma;
using namespace Rcpp;
using namespace R;
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::export]]
vec prod(vec a,vec b)
{
return(a%b);
}
mat f(mat a,vec theta,vec t,int N2)
{
mat atemp(N2,8);'
}
if(RK.order==10)
{
if(homo.res)
{
ODEpart= '
return atemp;
}
// [[Rcpp::export]]
List solver(vec Xs,vec Xt,vec theta,int N,double delt,int N2,vec tt,int P,double alpha,double lower,double upper,int tro)
{
mat x0(N2,tro);
mat xa(N2,tro);
mat xe(N2,tro);
mat fx0(N2,tro);
mat fx1(N2,tro);
mat fx2(N2,tro);
mat fx3(N2,tro);
mat fx4(N2,tro);
mat fx5(N2,tro);
mat fx6(N2,tro);
mat fx7(N2,tro);
mat fx8(N2,tro);
mat fx9(N2,tro);
mat fx10(N2,tro);
mat fx11(N2,tro);
mat fx12(N2,tro);
mat fx13(N2,tro);
mat fx14(N2,tro);
mat fx15(N2,tro);
mat fx16(N2,tro);
double whch =0;
x0.fill(0);
x0.col(0)=Xs;
vec d=tt;
for (int i = 1; i < N+1; i++)
{
fx0=f(x0,theta,d,N2)*delt;
fx1=f(x0+0.1*fx0,theta,d+0.100000000000000000000000000000000000000000000000000000000000*delt,N2)*delt;
fx2=f(x0+-0.915176561375291*fx0+1.45453440217827*fx1,theta,d+0.539357840802981787532485197881302436857273449701009015505500*delt,N2)*delt;
fx3=f(x0+0.202259190301118*fx0+0.606777570903354*fx2,theta,d+0.809036761204472681298727796821953655285910174551513523258250*delt,N2)*delt;
fx4=f(x0+0.184024714708644*fx0+0.197966831227192*fx2-0.0729547847313633*fx3,theta,d+0.309036761204472681298727796821953655285910174551513523258250*delt,N2)*delt;
fx5=f(x0+0.0879007340206681*fx0+0.410459702520261*fx3+0.482713753678866*fx4,theta,d+0.981074190219795268254879548310562080489056746118724882027805*delt,N2)*delt;
fx6=f(x0+0.085970050490246*fx0+0.330885963040722*fx3+0.48966295730945*fx4-0.0731856375070851*fx5,theta,d+0.833333333333333333333333333333333333333333333333333333333333*delt,N2)*delt;
fx7=f(x0+0.120930449125334*fx0+0.260124675758296*fx4+0.0325402621549091*fx5-0.0595780211817361*fx6,theta,d+0.354017365856802376329264185948796742115824053807373968324184*delt,N2)*delt;
fx8=f(x0+0.110854379580391*fx0-0.0605761488255006*fx5+0.321763705601778*fx6+0.510485725608063*fx7,theta,d+0.882527661964732346425501486979669075182867844268052119663791*delt,N2)*delt;
fx9=f(x0+0.112054414752879*fx0-0.144942775902866*fx5-0.333269719096257*fx6+0.49926922955688*fx7+0.509504608929686*fx8,theta,d+0.642615758240322548157075497020439535959501736363212695909875*delt,N2)*delt;
fx10=f(x0+0.113976783964186*fx0-0.0768813364203357*fx5+0.239527360324391*fx6+0.397774662368095*fx7+0.0107558956873607*fx8-0.327769124164019*fx9,theta,d+0.357384241759677451842924502979560464040498263636787304090125*delt,N2)*delt;
fx11=f(x0+0.0798314528280196*fx0-0.0520329686800603*fx5-0.0576954146168549*fx6+0.194781915712104*fx7+0.145384923188325*fx8-0.0782942710351671*fx9-0.114503299361099*fx10,theta,d+0.117472338035267653574498513020330924817132155731947880336209*delt,N2)*delt;
fx12=f(x0+0.985115610164857*fx0+0.330885963040722*fx3+0.48966295730945*fx4-1.37896486574844*fx5-0.861164195027636*fx6+5.78428813637537*fx7+3.28807761985104*fx8-2.38633905093136*fx9-3.25479342483644*fx10-2.16343541686423*fx11,theta,d+0.833333333333333333333333333333333333333333333333333333333333*delt,N2)*delt;
fx13=f(x0+0.895080295771633*fx0+0.197966831227192*fx2-0.0729547847313633*fx3-0.851236239662008*fx5+0.398320112318533*fx6+3.63937263181036*fx7+1.5482287703983*fx8-2.12221714704054*fx9-1.58350398545326*fx10-1.71561608285936*fx11-0.0244036405750127*fx12,theta,d+0.309036761204472681298727796821953655285910174551513523258250*delt,N2)*delt;
fx14=f(x0+-0.915176561375291*fx0+1.45453440217827*fx1+0*fx2+0*fx3-0.777333643644968*fx4+0*fx5-0.0910895662155176*fx6+0.0910895662155176*fx12+0.777333643644968*fx13,theta,d+0.539357840802981787532485197881302436857273449701009015505500*delt,N2)*delt;
fx15=f(x0+0.1*fx0-0.157178665799771*fx2+0.157178665799771*fx14,theta,d+0.100000000000000000000000000000000000000000000000000000000000*delt,N2)*delt;
fx16=f(x0+0.181781300700095*fx0+0.675*fx1+0.34275815984719*fx2+0*fx3+0.259111214548323*fx4-0.358278966717952*fx5-1.04594895940883*fx6+0.930327845415627*fx7+1.77950959431708*fx8+0.1*fx9-0.282547569539044*fx10-0.159327350119973*fx11-0.145515894647002*fx12-0.259111214548323*fx13-0.34275815984719*fx14-0.675*fx15,theta,d+delt,N2)*delt;
x0=x0+(0.0333333333333333333333333333333333333333333333333333333333333*fx0
+0.0250000000000000000000000000000000000000000000000000000000000*fx1
+0.0333333333333333333333333333333333333333333333333333333333333*fx2
+0.000000000000000000000000000000000000000000000000000000000000*fx3
+0.0500000000000000000000000000000000000000000000000000000000000*fx4
+0.000000000000000000000000000000000000000000000000000000000000*fx5
+0.0400000000000000000000000000000000000000000000000000000000000*fx6
+0.000000000000000000000000000000000000000000000000000000000000*fx7
+0.189237478148923490158306404106012326238162346948625830327194*fx8
+0.277429188517743176508360262560654340428504319718040836339472*fx9
+0.277429188517743176508360262560654340428504319718040836339472*fx10
+0.189237478148923490158306404106012326238162346948625830327194*fx11
-0.0400000000000000000000000000000000000000000000000000000000000*fx12
-0.0500000000000000000000000000000000000000000000000000000000000*fx13
-0.0333333333333333333333333333333333333333333333333333333333333*fx14
-0.0250000000000000000000000000000000000000000000000000000000000*fx15
+0.0333333333333333333333333333333333333333333333333333333333333*fx16);
xe = abs(fx1.col(1)-fx15.col(1))/360.0;
if(xe.max()>whch)
{
whch = xe.max();
}
d=d+delt;
}
'
}
if(!homo.res)
{
ODEpart= '
return atemp;
}
// [[Rcpp::export]]
List solver(vec Xs,vec Xt,vec theta,int N,double delt,int N2,vec tt,int P,double alpha,double lower,double upper,int tro)
{
mat x0(N2,tro);
mat xa(N2,tro);
mat xe(N2,tro);
mat fx0(N2,tro);
mat fx1(N2,tro);
mat fx2(N2,tro);
mat fx3(N2,tro);
mat fx4(N2,tro);
mat fx5(N2,tro);
mat fx6(N2,tro);
mat fx7(N2,tro);
mat fx8(N2,tro);
mat fx9(N2,tro);
mat fx10(N2,tro);
mat fx11(N2,tro);
mat fx12(N2,tro);
mat fx13(N2,tro);
mat fx14(N2,tro);
mat fx15(N2,tro);
mat fx16(N2,tro);
double whch =0;
x0.fill(0);
x0.col(0)=Xs;
vec d=tt;
for (int i = 1; i < N+1; i++)
{
fx0=f(x0,theta,d,N2)%delt;
fx1=f(x0+0.1*fx0,theta,d+0.100000000000000000000000000000000000000000000000000000000000*delt.col(0),N2)%delt;
fx2=f(x0+-0.915176561375291*fx0+1.45453440217827*fx1,theta,d+0.539357840802981787532485197881302436857273449701009015505500*delt.col(0),N2)%delt;
fx3=f(x0+0.202259190301118*fx0+0.606777570903354*fx2,theta,d+0.809036761204472681298727796821953655285910174551513523258250*delt.col(0),N2)%delt;
fx4=f(x0+0.184024714708644*fx0+0.197966831227192*fx2-0.0729547847313633*fx3,theta,d+0.309036761204472681298727796821953655285910174551513523258250*delt.col(0),N2)%delt;
fx5=f(x0+0.0879007340206681*fx0+0.410459702520261*fx3+0.482713753678866*fx4,theta,d+0.981074190219795268254879548310562080489056746118724882027805*delt.col(0),N2)%delt;
fx6=f(x0+0.085970050490246*fx0+0.330885963040722*fx3+0.48966295730945*fx4-0.0731856375070851*fx5,theta,d+0.833333333333333333333333333333333333333333333333333333333333*delt.col(0),N2)%delt;
fx7=f(x0+0.120930449125334*fx0+0.260124675758296*fx4+0.0325402621549091*fx5-0.0595780211817361*fx6,theta,d+0.354017365856802376329264185948796742115824053807373968324184*delt.col(0),N2)%delt;
fx8=f(x0+0.110854379580391*fx0-0.0605761488255006*fx5+0.321763705601778*fx6+0.510485725608063*fx7,theta,d+0.882527661964732346425501486979669075182867844268052119663791*delt.col(0),N2)%delt;
fx9=f(x0+0.112054414752879*fx0-0.144942775902866*fx5-0.333269719096257*fx6+0.49926922955688*fx7+0.509504608929686*fx8,theta,d+0.642615758240322548157075497020439535959501736363212695909875*delt.col(0),N2)%delt;
fx10=f(x0+0.113976783964186*fx0-0.0768813364203357*fx5+0.239527360324391*fx6+0.397774662368095*fx7+0.0107558956873607*fx8-0.327769124164019*fx9,theta,d+0.357384241759677451842924502979560464040498263636787304090125*delt.col(0),N2)%delt;
fx11=f(x0+0.0798314528280196*fx0-0.0520329686800603*fx5-0.0576954146168549*fx6+0.194781915712104*fx7+0.145384923188325*fx8-0.0782942710351671*fx9-0.114503299361099*fx10,theta,d+0.117472338035267653574498513020330924817132155731947880336209*delt.col(0),N2)%delt;
fx12=f(x0+0.985115610164857*fx0+0.330885963040722*fx3+0.48966295730945*fx4-1.37896486574844*fx5-0.861164195027636*fx6+5.78428813637537*fx7+3.28807761985104*fx8-2.38633905093136*fx9-3.25479342483644*fx10-2.16343541686423*fx11,theta,d+0.833333333333333333333333333333333333333333333333333333333333*delt.col(0),N2)%delt;
fx13=f(x0+0.895080295771633*fx0+0.197966831227192*fx2-0.0729547847313633*fx3-0.851236239662008*fx5+0.398320112318533*fx6+3.63937263181036*fx7+1.5482287703983*fx8-2.12221714704054*fx9-1.58350398545326*fx10-1.71561608285936*fx11-0.0244036405750127*fx12,theta,d+0.309036761204472681298727796821953655285910174551513523258250*delt.col(0),N2)%delt;
fx14=f(x0+-0.915176561375291*fx0+1.45453440217827*fx1+0*fx2+0*fx3-0.777333643644968*fx4+0*fx5-0.0910895662155176*fx6+0.0910895662155176*fx12+0.777333643644968*fx13,theta,d+0.539357840802981787532485197881302436857273449701009015505500*delt.col(0),N2)%delt;
fx15=f(x0+0.1*fx0-0.157178665799771*fx2+0.157178665799771*fx14,theta,d+0.100000000000000000000000000000000000000000000000000000000000*delt.col(0),N2)%delt;
fx16=f(x0+0.181781300700095*fx0+0.675*fx1+0.34275815984719*fx2+0*fx3+0.259111214548323*fx4-0.358278966717952*fx5-1.04594895940883*fx6+0.930327845415627*fx7+1.77950959431708*fx8+0.1*fx9-0.282547569539044*fx10-0.159327350119973*fx11-0.145515894647002*fx12-0.259111214548323*fx13-0.34275815984719*fx14-0.675*fx15,theta,d+delt.col(0),N2)%delt;
x0=x0+(0.0333333333333333333333333333333333333333333333333333333333333*fx0
+0.0250000000000000000000000000000000000000000000000000000000000*fx1
+0.0333333333333333333333333333333333333333333333333333333333333*fx2
+0.000000000000000000000000000000000000000000000000000000000000*fx3
+0.0500000000000000000000000000000000000000000000000000000000000*fx4
+0.000000000000000000000000000000000000000000000000000000000000*fx5
+0.0400000000000000000000000000000000000000000000000000000000000*fx6
+0.000000000000000000000000000000000000000000000000000000000000*fx7
+0.189237478148923490158306404106012326238162346948625830327194*fx8
+0.277429188517743176508360262560654340428504319718040836339472*fx9
+0.277429188517743176508360262560654340428504319718040836339472*fx10
+0.189237478148923490158306404106012326238162346948625830327194*fx11
-0.0400000000000000000000000000000000000000000000000000000000000*fx12
-0.0500000000000000000000000000000000000000000000000000000000000*fx13
-0.0333333333333333333333333333333333333333333333333333333333333*fx14
-0.0250000000000000000000000000000000000000000000000000000000000*fx15
+0.0333333333333333333333333333333333333333333333333333333333333*fx16);
xe = abs(fx1.col(1)-fx15.col(1))/360.0;
if(xe.max()>whch)
{
whch = xe.max();
}
d=d+delt.col(0);
}
'
}
}
if(RK.order==4)
{
if(homo.res)
{
ODEpart= '
return atemp;
}
// [[Rcpp::export]]
List solver(vec Xs,vec Xt,vec theta,int N,double delt,int N2,vec tt,int P,double alpha,double lower,double upper,int tro)
{
mat x0(N2,tro);
mat xa(N2,tro);
mat xe(N2,tro);
mat fx1(N2,tro);
mat fx2(N2,tro);
mat fx3(N2,tro);
mat fx4(N2,tro);
mat fx5(N2,tro);
mat fx6(N2,tro);
double whch =0;
x0.fill(0);
x0.col(0)=Xs;
vec d=tt;
for (int i = 1; i < N+1; i++)
{
fx1 = f(x0,theta,d,N2)*delt;
fx2 = f(x0+0.25*fx1,theta,d+0.25*delt,N2)*delt;
fx3 = f(x0+0.09375*fx1+0.28125*fx2,theta,d+0.375*delt,N2)*delt;
fx4 = f(x0+0.879381*fx1-3.277196*fx2+ 3.320892*fx3,theta,d+0.9230769*delt,N2)*delt;
fx5 = f(x0+2.032407*fx1-8*fx2+7.173489*fx3-0.2058967*fx4,theta,d+delt,N2)*delt;
fx6 = f(x0-0.2962963*fx1+2*fx2-1.381676*fx3+0.4529727*fx4-0.275*fx5,theta,d+0.5*delt,N2)*delt;
xa = x0+0.1185185*fx1+0.5189864*fx3+0.5061315*fx4-0.18*fx5+0.03636364*fx6;
x0 = x0+0.1157407*fx1+0.5489279*fx3+0.5353314*fx4-0.2*fx5;
xe = abs(x0.col(1)-xa.col(1));
if(xe.max()>whch)
{
whch = xe.max();
}
d=d+delt;
}
'
}
if(!homo.res)
{
ODEpart= '
return atemp;
}
// [[Rcpp::export]]
List solver(vec Xs,vec Xt,vec theta,int N, mat delt,int N2,vec tt,int P,double alpha,double lower,double upper,int tro)
{
mat x0(N2,tro);
mat xa(N2,tro);
mat xe(N2,tro);
mat fx1(N2,tro);
mat fx2(N2,tro);
mat fx3(N2,tro);
mat fx4(N2,tro);
mat fx5(N2,tro);
mat fx6(N2,tro);
double whch =0;
vec d=tt;
x0.fill(0);
x0.col(0)=Xs;
for (int i = 1; i < N+1; i++)
{
fx1 = f(x0,theta,d,N2)%delt;
fx2 = f(x0+0.25*fx1,theta,d+0.25*delt.col(0),N2)%delt;
fx3 = f(x0+0.09375*fx1+0.28125*fx2,theta,d+0.375*delt.col(0),N2)%delt;
fx4 = f(x0+0.879381*fx1-3.277196*fx2+ 3.320892*fx3,theta,d+0.9230769*delt.col(0),N2)%delt;
fx5 = f(x0+2.032407*fx1-8*fx2+7.173489*fx3-0.2058967*fx4,theta,d+delt.col(0),N2)%delt;
fx6 = f(x0-0.2962963*fx1+2*fx2-1.381676*fx3+0.4529727*fx4-0.275*fx5,theta,d+0.5*delt.col(0),N2)%delt;
xa = x0+0.1185185*fx1+0.5189864*fx3+0.5061315*fx4-0.18*fx5+0.03636364*fx6;
x0 = x0+0.1157407*fx1+0.5489279*fx3+0.5353314*fx4-0.2*fx5;
xe = abs(x0.col(1)-xa.col(1));
if(xe.max()>whch)
{
whch = xe.max();
}
d=d+delt.col(0);
}
'
}
}
if(DTR.order==2)
{
Inv=
'
mat u(N2,2);
u.col(0)=x0.col(0);
u.col(1)=x0.col(1)+x0.col(0)%u.col(0);
vec det=u.col(1)-u.col(0)%u.col(0);
vec b11=(u.col(1))/det;
vec b12=-u.col(0)/det;
vec b21=-u.col(0)/det;
vec b22=(1.0)/det;
'
switch(Dindex,
{
Dpart='
vec val = -0.5*log(2*3.141592653589793)-0.5*log(x0.col(1))-0.5*((Xt-x0.col(0))%(Xt-x0.col(0))/x0.col(1));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
})
}
if(DTR.order==4)
{
Inv=
'
mat u(N2,4);
u.col(0)=x0.col(0);
u.col(1)=x0.col(1)+x0.col(0)%u.col(0);
u.col(2)=x0.col(2)+x0.col(0)%u.col(1)+2*x0.col(1)%u.col(0);
u.col(3)=x0.col(3)+x0.col(0)%u.col(2)+3*x0.col(1)%u.col(1)+3*x0.col(2)%u.col(0);
vec det=u.col(1)%u.col(3)+u.col(0)%u.col(2)%u.col(1)+u.col(1)%u.col(0)%u.col(2)-u.col(2)%u.col(2)-u.col(1)%u.col(1)%u.col(1)-u.col(0)%u.col(0)%u.col(3);
vec b11=(u.col(1)%u.col(3)-u.col(2)%u.col(2))/det;
vec b12=(u.col(1)%u.col(2)-u.col(0)%u.col(3))/det;
vec b13=(u.col(0)%u.col(2)-u.col(1)%u.col(1))/det;
vec b21=(u.col(2)%u.col(1)-u.col(0)%u.col(3))/det;
vec b22=(u.col(3)-u.col(1)%u.col(1))/det;
vec b23=(u.col(1)%u.col(0)-u.col(2))/det;
vec b31=(u.col(0)%u.col(2)-u.col(1)%u.col(1))/det;
vec b32=(u.col(0)%u.col(1)-u.col(2))/det;
vec b33=(u.col(1)-u.col(0)%u.col(0))/det;
'
switch(Dindex,
{
Dpart='
vec p=(1.0/3.0) *(3*(x0.col(3)/6.0)%x0.col(1) - pow(x0.col(2)/2.0,2))/pow(x0.col(3)/6.0,2);
vec q=(1.0/27.0)*(27*pow(x0.col(3)/6.0,2)%(x0.col(0)-Xt) - 9*(x0.col(3)/6.0)%(x0.col(2)/2.0)%x0.col(1) + 2*pow(x0.col(2)/2.0,3))/pow(x0.col(3)/6.0,3);
vec chk=pow(q,2)/4.0 + pow(p,3)/27.0;
vec th=-(x0.col(2)/2.0)/(3*(x0.col(3)/6.0))+pow(-q/2.0+sqrt(chk),(1.0/3.0))-pow(q/2.0+sqrt(chk),(1.0/3.0));
vec K =x0.col(0)%th+(x0.col(1)%th%th)/2.0+(x0.col(2)%th%th%th)/6.0 +(x0.col(3)%th%th%th%th)/24.0;
vec K1=x0.col(0) +(x0.col(1)%th) +(x0.col(2)%th%th)/2.0 +(x0.col(3)%th%th%th)/6.0;
vec K2=x0.col(1) +(x0.col(2)%th) +(x0.col(3)%th%th)/2.0;
vec val=-0.5*log(2*3.141592653589793*K2)+(K-th%K1);
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart=
'
vec betas1 =+b12+2*b13%u.col(0);
vec betas2 =+b22+2*b23%u.col(0);
vec betas3 =+b32+2*b33%u.col(0);
vec K=exp(-betas1*pow(Xtr,1)-0.5*betas2*pow(Xtr,2)-0.333333333333333333*betas3*pow(Xtr,3));
vec lo =-(0.5/(u.col(0)-lower))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-lower),2))-exp(alpha));
vec up =-(0.5/(u.col(0)-upper))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas1%pow(tau,1)-0.5*betas2%pow(tau,2)-0.333333333333333333*betas3%pow(tau,3))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas1%pow(tau,1)-0.5*betas2%pow(tau,2)-0.333333333333333333*betas3%pow(tau,3))%rho%DT;
}
vec val = ((-betas1%pow(Xt,1)-0.5*betas2%pow(Xt,2)-0.333333333333333333*betas3%pow(Xt,3))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+b11+2*b12%u.col(0)+3*b13%u.col(1);
vec betas2 =+b21+2*b22%u.col(0)+3*b23%u.col(1);
vec betas3 =+b31+2*b32%u.col(0)+3*b33%u.col(1);
vec lo =-(0.5/(u.col(0)-lower))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-lower),2))-exp(alpha));
vec up =-(0.5/(u.col(0)-upper))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas1%log(tau)+(-betas2%tau-0.5*betas3%pow(tau,2)))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas1%log(tau)+(-betas2%tau-0.5*betas3%pow(tau,2)))%rho%DT;
}
vec val =((-betas1%log(Xt)+(-betas2%Xt-0.5*betas3%pow(Xt,2)))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+2*b11%u.col(0)+3*b12%u.col(1)+4*b13%u.col(2);
vec betas2 =+2*b21%u.col(0)+3*b22%u.col(1)+4*b23%u.col(2);
vec betas3 =+2*b31%u.col(0)+3*b32%u.col(1)+4*b33%u.col(2);
vec lo =-(0.5/(u.col(0)-lower))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-lower),2))-exp(alpha));
vec up =-(0.5/(u.col(0)-upper))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas2%log(tau)+(betas1/tau-betas3%tau))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas2%log(tau)+(betas1/tau-betas3%tau))%rho%DT;
}
vec val((-betas2%log(Xt)+(betas1/Xt-betas3%Xt))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+b11%(1-2*u.col(0))+b12%(2*u.col(0)-3*u.col(1))+b13%(3*u.col(1)-4*u.col(2));
vec betas2 =+b21%(1-2*u.col(0))+b22%(2*u.col(0)-3*u.col(1))+b23%(3*u.col(1)-4*u.col(2));
vec betas3 =+b31%(1-2*u.col(0))+b32%(2*u.col(0)-3*u.col(1))+b33%(3*u.col(1)-4*u.col(2));
vec lo =-(0.5/(u.col(0)-lower))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-lower),2))-exp(alpha));
vec up =-(0.5/(u.col(0)-upper))%(sqrt(exp(2*alpha)+4*pow((u.col(0)-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas2%log(tau)+(betas1/tau-betas3%tau))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u.col(0);
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas2%log(tau)+(betas1/tau-betas3%tau))%rho%DT;
}
vec val = ((-betas2%log(Xt)+(betas1/Xt-betas3%Xt))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}'
})
if(Dindex!=1)
{
Dpart=paste(Inv,Dpart)
}
}
if(DTR.order==6)
{
Inv=
'
vec u1 = x0.col(0);
vec u2 = x0.col(1)+x0.col(0)%u1;
vec u3 = x0.col(2)+x0.col(0)%u2+2*x0.col(1)%u1;
vec u4 = x0.col(3)+x0.col(0)%u3+3*x0.col(1)%u2+3*x0.col(2)%u1 ;
vec u5 = x0.col(4)+x0.col(0)%u4+4*x0.col(1)%u3+6*x0.col(2)%u2+4*x0.col(3)%u1 ;
vec u6 = x0.col(5)+x0.col(0)%u5+5*x0.col(1)%u4+10*x0.col(2)%u3+10*x0.col(3)%u2+5*x0.col(4)%u1 ;
vec det=-u1%(u1%(u4%u6-u5%u5)-u3%(u2%u6-u3%u5)+u4%(u2%u5-u3%u4))+u2%(u1%(u3%u6-u4%u5)-u2%(u2%u6-u3%u5)+u4%(u2%u4-u3%u3))+u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)-u3%(u1%(u3%u5-u4%u4)-u2%(u2%u5-u3%u4)+u3%(u2%u4-u3%u3))+u4%(u3%u5-u4%u4);
vec b11 = (u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4))/det ;
vec b12 = (-u1%(u4%u6-u5%u5)+u2%(u3%u6-u4%u5)-u3%(u3%u5-u4%u4))/det ;
vec b13 = (u1%(u3%u6-u4%u5)-u2%(u2%u6-u4%u4)+u3%(u2%u5-u3%u4))/det ;
vec b14 = (-u1%(u3%u5-u4%u4)+u2%(u2%u5-u3%u4)-u3%(u2%u4-u3%u3))/det ;
vec b21 = (-u1%(u4%u6-u5%u5)+u3%(u2%u6-u3%u5)-u4%(u2%u5-u3%u4))/det;
vec b22 = (-u2%(u2%u6-u3%u5)+u4%u6-u5%u5+u3%(u2%u5-u3%u4))/det ;
vec b23 = (u2%(u1%u6-u3%u4)-u3%u6-u3%(u1%u5-u3%u3)+u4%u5)/det ;
vec b24 = (-u2%(u1%u5-u2%u4)+u3%u5-u4%u4+u3%(u1%u4-u2%u3))/det ;
vec b31 = (u1%(u3%u6-u4%u5)-u2%(u2%u6-u3%u5)+u4%(u2%u4-u3%u3))/det;
vec b32 = (u1%(u2%u6-u3%u5)-u3%u6+u4%u5-u3%(u2%u4-u3%u3))/det ;
vec b33 = (-u1%(u1%u6-u3%u4)+u2%u6-u4%u4+u3%(u1%u4-u2%u3))/det;
vec b34 = (u1%(u1%u5-u2%u4)-u2%u5+u3%u4-u3%(u1%u3-u2%u2))/det ;
vec b41 = (-u1%(u3%u5-u4%u4)+u2%(u2%u5-u3%u4)-u3%(u2%u4-u3%u3))/det;
vec b42 = (-u1%(u2%u5-u3%u4)+u3%u5-u4%u4+u2%(u2%u4-u3%u3))/det;
vec b43 = (u1%(u1%u5-u3%u3)-u2%u5-u2%(u1%u4-u2%u3)+u3%u4)/det ;
vec b44 = (-u1%(u1%u4-u2%u3)+u2%u4-u3%u3+u2%(u1%u3-u2%u2))/det ;
'
switch(Dindex,
{
Dpart='
vec p=(1.0/3.0) *(3*(x0.col(3)/6.0)%x0.col(1) - pow(x0.col(2)/2.0,2))/pow(x0.col(3)/6.0,2);
vec q=(1.0/27.0)*(27*pow(x0.col(3)/6.0,2)%(x0.col(0)-Xt) - 9*(x0.col(3)/6.0)%(x0.col(2)/2.0)%x0.col(1) + 2*pow(x0.col(2)/2.0,3))/pow(x0.col(3)/6.0,3);
vec chk=pow(q,2)/4.0 + pow(p,3)/27.0;
vec th=-(x0.col(2)/2.0)/(3*(x0.col(3)/6.0))+pow(-q/2.0+sqrt(chk),(1.0/3.0))-pow(q/2.0+sqrt(chk),(1.0/3.0));
vec K =x0.col(0)%th+(x0.col(1)%th%th)/2.0+(x0.col(2)%th%th%th)/6.0 +(x0.col(3)%th%th%th%th)/24.0;
vec K1=x0.col(0) +(x0.col(1)%th) +(x0.col(2)%th%th)/2.0 +(x0.col(3)%th%th%th)/6.0;
vec K2=x0.col(1) +(x0.col(2)%th) +(x0.col(3)%th%th)/2.0;
vec val=-0.5*log(2*3.141592653589793*K2)+(K-th%K1);
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart=
'
vec betas1 =+b12+2*b13%u1+3*b14%u2;
vec betas2 =+b22+2*b23%u1+3*b24%u2;
vec betas3 =+b32+2*b33%u1+3*b34%u2;
vec betas4 =+b42+2*b43%u1+3*b44%u2;
vec lo =-(0.5/(u1-lower))%(sqrt(exp(2*alpha)+4*pow((u1-lower),2))-exp(alpha));
vec up =-(0.5/(u1-upper))%(sqrt(exp(2*alpha)+4*pow((u1-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas1%pow(tau,1)-0.5*betas2%pow(tau,2)-0.333333333333333333*betas3%pow(tau,3)-0.25*betas4%pow(tau,4))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas1%pow(tau,1)-0.5*betas2%pow(tau,2)-0.333333333333333333*betas3%pow(tau,3)-0.25*betas4%pow(tau,4))%rho%DT;
}
vec val=((-betas1%pow(Xt,1)-0.5*betas2%pow(Xt,2)-0.333333333333333333*betas3%pow(Xt,3)-0.25*betas4%pow(Xt,4))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+b11+2*b12%u1+3*b13%u2+4*b14%u3;
vec betas2 =+b21+2*b22%u1+3*b23%u2+4*b24%u3;
vec betas3 =+b31+2*b32%u1+3*b33%u2+4*b34%u3;
vec betas4 =+b41+2*b42%u1+3*b43%u2+4*b44%u3;
vec lo =-(0.5/(u1-lower))%(sqrt(exp(2*alpha)+4*pow((u1-lower),2))-exp(alpha));
vec up =-(0.5/(u1-upper))%(sqrt(exp(2*alpha)+4*pow((u1-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas1%log(tau)+(-betas2%tau-0.5*betas3%pow(tau,2)-0.33333333333333333333333*betas4%pow(tau,3)))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas1%log(tau)+(-betas2%tau-0.5*betas3%pow(tau,2)-0.33333333333333333333333*betas4%pow(tau,3)))%rho%DT;
}
vec val = ((-betas1%log(Xt)+(-betas2%Xt-0.5*betas3%pow(Xt,2)-0.33333333333333333333333*betas4%pow(Xt,3)))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+2*b11%u1+3*b12%u2+4*b13%u3+5*b14%u4;
vec betas2 =+2*b21%u1+3*b22%u2+4*b23%u3+5*b24%u4;
vec betas3 =+2*b31%u1+3*b32%u2+4*b33%u3+5*b34%u4;
vec betas4 =+2*b41%u1+3*b42%u2+4*b43%u3+5*b44%u4;
vec lo =-(0.5/(u1-lower))%(sqrt(exp(2*alpha)+4*pow((u1-lower),2))-exp(alpha));
vec up =-(0.5/(u1-upper))%(sqrt(exp(2*alpha)+4*pow((u1-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas2%log(tau)+(betas1/tau-betas3%tau-0.5*betas4%pow(tau,2)))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas2%log(tau)+(betas1/tau-betas3%tau-0.5*betas4%pow(tau,2)))%rho%DT;
}
vec val = ((-betas2%log(Xt)+(betas1/Xt-betas3%Xt-0.5*betas4%pow(Xt,2)))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+b11%(1-2*u1)+b12%(2*u1-3*u2)+b13%(3*u2-4*u3)+b14%(4*u3-5*u4);
vec betas2 =+b21%(1-2*u1)+b22%(2*u1-3*u2)+b23%(3*u2-4*u3)+b24%(4*u3-5*u4);
vec betas3 =+b31%(1-2*u1)+b32%(2*u1-3*u2)+b33%(3*u2-4*u3)+b34%(4*u3-5*u4);
vec betas4 =+b41%(1-2*u1)+b42%(2*u1-3*u2)+b43%(3*u2-4*u3)+b44%(4*u3-5*u4);
vec K=exp(-betas1*log(Xtr)+(betas1+betas2+betas3+betas4)*log(1-Xtr)+(betas3+betas4)*Xtr+0.5*betas4*pow(Xtr,2));
for (int i = 1; i <= lim; i++)
{
Xtr=Xtr+delt2;
K=K+exp(-betas1*log(Xtr)+(betas1+betas2+betas3+betas4)*log(1-Xtr)+(betas3+betas4)*Xtr+0.5*betas4*pow(Xtr,2))*delt2;
}
vec val =((-betas1%log(Xt)+(betas1+betas2+betas3+betas4)%log(1-Xt)+(betas3+betas4)%Xt+0.5*betas4%pow(Xt,2))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
})
if(Dindex!=1)
{
Dpart=paste(Inv,Dpart)
}
}
if(DTR.order==8)
{
Inv='
vec u1= x0.col(0);
vec u2= x0.col(1)+1*x0.col(0)%u1;
vec u3= x0.col(2)+1*x0.col(0)%u2+2*x0.col(1)%u1;
vec u4= x0.col(3)+1*x0.col(0)%u3+3*x0.col(1)%u2+3*x0.col(2)%u1;
vec u5= x0.col(4)+1*x0.col(0)%u4+4*x0.col(1)%u3+6*x0.col(2)%u2+4*x0.col(3)%u1;
vec u6= x0.col(5)+1*x0.col(0)%u5+5*x0.col(1)%u4+10*x0.col(2)%u3+10*x0.col(3)%u2+5*x0.col(4)%u1;
vec u7= x0.col(6)+1*x0.col(0)%u6+6*x0.col(1)%u5+15*x0.col(2)%u4+20*x0.col(3)%u3+15*x0.col(4)%u2+6*x0.col(5)%u1;
vec u8= x0.col(7)+1*x0.col(0)%u7+7*x0.col(1)%u6+21*x0.col(2)%u5+35*x0.col(3)%u4+35*x0.col(4)%u3+21*x0.col(5)%u2+7*x0.col(6)%u1;
vec det=-u1%(u1%(u4%(u6%u8-u7%u7)-u5%(u5%u8-u6%u7)+u6%(u5%u7-u6%u6))-u3%(u2%(u6%u8-u7%u7)-u5%(u3%u8-u4%u7)+u6%(u3%u7-u4%u6))+u4%
(u2%(u5%u8-u6%u7)-u4%(u3%u8-u4%u7)+u6%(u3%u6-u4%u5))-u5%(u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)+u5%(u3%u6-u4%u5)))+u2%(u1%
(u3%(u6%u8-u7%u7)-u5%(u4%u8-u5%u7)+u6%(u4%u7-u5%u6))-u2%(u2%(u6%u8-u7%u7)-u5%(u3%u8-u4%u7)+u6%(u3%u7-u4%u6))+u4%
(u2%(u4%u8-u5%u7)-u3%(u3%u8-u4%u7)+(u3%u5-u4%u4)%u6)-u5%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u4%u6)+u5%(u3%u5-u4%u4)))-u3%(u1%
(u3%(u5%u8-u6%u7)-u4%(u4%u8-u5%u7)+u6%(u4%u6-u5%u5))-u2%(u2%(u5%u8-u6%u7)-u4%(u3%u8-u4%u7)+u6%(u3%u6-u4%u5))+u3%
(u2%(u4%u8-u5%u7)-u3%(u3%u8-u4%u7)+(u3%u5-u4%u4)%u6)-u5%(u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4)))+u2%
(u4%(u6%u8-u7%u7)-u5%(u5%u8-u6%u7)+u6%(u5%u7-u6%u6))-u3%(u3%(u6%u8-u7%u7)-u5%(u4%u8-u5%u7)+u6%(u4%u7-u5%u6))+u4%
(u3%(u5%u8-u6%u7)-u4%(u4%u8-u5%u7)+u6%(u4%u6-u5%u5))+u4%(u1%(u3%(u5%u7-u6%u6)-u4%(u4%u7-u5%u6)+u5%(u4%u6-u5%u5))-u2%
(u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)+u5%(u3%u6-u4%u5))+u3%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u4%u6)+u5%(u3%u5-u4%u4))-u4%
(u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4)))-u5%(u3%(u5%u7-u6%u6)-u4%(u4%u7-u5%u6)+u5%(u4%u6-u5%u5));
vec b11= (u2%(u4%(u6%u8-u7%u7)-u5%(u5%u8-u6%u7)+u6%(u5%u7-u6%u6))-u3%(u3%(u6%u8-u7%u7)-u5%(u4%u8-u5%u7)+u6%(u4%u7-u5%u6))+u4%(u3%(u5%u8-u6%u7)-u4%(u4%u8-u5%u7)+u6%(u4%u6-u5%u5))-u5%(u3%(u5%u7-u6%u6)-u4%(u4%u7-u5%u6)+u5%(u4%u6-u5%u5)))/det;
vec b12= (-u1%(u4%(u6%u8-u7%u7)-u5%(u5%u8-u6%u7)+u6%(u5%u7-u6%u6))+u2%(u3%(u6%u8-u7%u7)-u5%(u4%u8-u5%u7)+u6%(u4%u7-u5%u6))-u3%(u3%(u5%u8-u6%u7)-u4%(u4%u8-u5%u7)+u6%(u4%u6-u5%u5))+u4%(u3%(u5%u7-u6%u6)-u4%(u4%u7-u5%u6)+u5%(u4%u6-u5%u5)))/det;
vec b13= (u1%(u3%(u6%u8-u7%u7)-u4%(u5%u8-u6%u7)+u5%(u5%u7-u6%u6))-u2%(u2%(u6%u8-u7%u7)-u4%(u4%u8-u5%u7)+u5%(u4%u7-u5%u6))+u3%(u2%(u5%u8-u6%u7)-u3%(u4%u8-u5%u7)+u5%(u4%u6-u5%u5))-u4%(u2%(u5%u7-u6%u6)-u3%(u4%u7-u5%u6)+u4%(u4%u6-u5%u5)))/det;
vec b14= (-u1%(u3%(u5%u8-u6%u7)-u4%(u4%u8-u6%u6)+u5%(u4%u7-u5%u6))+u2%(u2%(u5%u8-u6%u7)-u4%(u3%u8-u5%u6)+u5%(u3%u7-u5%u5))-u3%(u2%(u4%u8-u6%u6)-u3%(u3%u8-u5%u6)+u5%(u3%u6-u4%u5))+u4%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u5%u5)+u4%(u3%u6-u4%u5)))/det;
vec b15= (u1%(u3%(u5%u7-u6%u6)-u4%(u4%u7-u5%u6)+u5%(u4%u6-u5%u5))-u2%(u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)+u5%(u3%u6-u4%u5))+u3%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u4%u6)+u5%(u3%u5-u4%u4))-u4%(u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4)))/det;
vec b21= (-u1%(u4%(u6%u8-u7%u7)-u5%(u5%u8-u6%u7)+u6%(u5%u7-u6%u6))+u3%(u2%(u6%u8-u7%u7)-u5%(u3%u8-u4%u7)+u6%(u3%u7-u4%u6))-u4%(u2%(u5%u8-u6%u7)-u4%(u3%u8-u4%u7)+u6%(u3%u6-u4%u5))+u5%(u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)+u5%(u3%u6-u4%u5)))/det;
vec b22= (-u2%(u2%(u6%u8-u7%u7)-u5%(u3%u8-u4%u7)+u6%(u3%u7-u4%u6))+u3%(u2%(u5%u8-u6%u7)-u4%(u3%u8-u4%u7)+u6%(u3%u6-u4%u5))+u4%(u6%u8-u7%u7)-u5%(u5%u8-u6%u7)-u4%(u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)+u5%(u3%u6-u4%u5))+u6%(u5%u7-u6%u6))/det;
vec b23= (u2%(u1%(u6%u8-u7%u7)-u4%(u3%u8-u4%u7)+u5%(u3%u7-u4%u6))-u3%(u1%(u5%u8-u6%u7)-u3%(u3%u8-u4%u7)+u5%(u3%u6-u4%u5))-u3%(u6%u8-u7%u7)+u4%(u5%u8-u6%u7)+u4%(u1%(u5%u7-u6%u6)-u3%(u3%u7-u4%u6)+u4%(u3%u6-u4%u5))-u5%(u5%u7-u6%u6))/det;
vec b24= (-u2%(u1%(u5%u8-u6%u7)-u4%(u2%u8-u4%u6)+u5%(u2%u7-u4%u5))+u3%(u1%(u4%u8-u6%u6)-u3%(u2%u8-u4%u6)+u5%(u2%u6-u4%u4))+u3%(u5%u8-u6%u7)-u4%(u4%u8-u6%u6)-u4%(u1%(u4%u7-u5%u6)-u3%(u2%u7-u4%u5)+u4%(u2%u6-u4%u4))+u5%(u4%u7-u5%u6))/det;
vec b25= (u2%(u1%(u5%u7-u6%u6)-u4%(u2%u7-u3%u6)+u5%(u2%u6-u3%u5))-u3%(u1%(u4%u7-u5%u6)-u3%(u2%u7-u3%u6)+u5%(u2%u5-u3%u4))-u3%(u5%u7-u6%u6)+u4%(u4%u7-u5%u6)+u4%(u1%(u4%u6-u5%u5)-u3%(u2%u6-u3%u5)+u4%(u2%u5-u3%u4))-u5%(u4%u6-u5%u5))/det;
vec b31= (u1%(u3%(u6%u8-u7%u7)-u5%(u4%u8-u5%u7)+u6%(u4%u7-u5%u6))-u2%(u2%(u6%u8-u7%u7)-u5%(u3%u8-u4%u7)+u6%(u3%u7-u4%u6))+u4%(u2%(u4%u8-u5%u7)-u3%(u3%u8-u4%u7)+(u3%u5-u4%u4)%u6)-u5%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u4%u6)+u5%(u3%u5-u4%u4)))/det;
vec b32= (u1%(u2%(u6%u8-u7%u7)-u5%(u3%u8-u4%u7)+u6%(u3%u7-u4%u6))-u3%(u2%(u4%u8-u5%u7)-u3%(u3%u8-u4%u7)+(u3%u5-u4%u4)%u6)-u3%(u6%u8-u7%u7)+u5%(u4%u8-u5%u7)+u4%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u4%u6)+u5%(u3%u5-u4%u4))-u6%(u4%u7-u5%u6))/det;
vec b33= (-u1%(u1%(u6%u8-u7%u7)-u4%(u3%u8-u4%u7)+u5%(u3%u7-u4%u6))+u3%(u1%(u4%u8-u5%u7)-u2%(u3%u8-u4%u7)+u5%(u3%u5-u4%u4))+u2%(u6%u8-u7%u7)-u4%(u4%u8-u5%u7)-u4%(u1%(u4%u7-u5%u6)-u2%(u3%u7-u4%u6)+u4%(u3%u5-u4%u4))+u5%(u4%u7-u5%u6))/det;
vec b34= (u1%(u1%(u5%u8-u6%u7)-u4%(u2%u8-u4%u6)+u5%(u2%u7-u4%u5))-u3%(u1%(u3%u8-u5%u6)-u2%(u2%u8-u4%u6)+u5%(u2%u5-u3%u4))-u2%(u5%u8-u6%u7)+u4%(u3%u8-u5%u6)+u4%(u1%(u3%u7-u5%u5)-u2%(u2%u7-u4%u5)+u4%(u2%u5-u3%u4))-u5%(u3%u7-u5%u5))/det;
vec b35= (-u1%(u1%(u5%u7-u6%u6)-u4%(u2%u7-u3%u6)+u5%(u2%u6-u3%u5))+u3%(u1%(u3%u7-u4%u6)-u2%(u2%u7-u3%u6)+(u2%u4-u3%u3)%u5)+u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)-u4%(u1%(u3%u6-u4%u5)-u2%(u2%u6-u3%u5)+u4%(u2%u4-u3%u3))+u5%(u3%u6-u4%u5))/det;
vec b41= (-u1%(u3%(u5%u8-u6%u7)-u4%(u4%u8-u5%u7)+u6%(u4%u6-u5%u5))+u2%(u2%(u5%u8-u6%u7)-u4%(u3%u8-u4%u7)+u6%(u3%u6-u4%u5))-u3%(u2%(u4%u8-u5%u7)-u3%(u3%u8-u4%u7)+(u3%u5-u4%u4)%u6)+u5%(u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4)))/det;
vec b42= (-u1%(u2%(u5%u8-u6%u7)-u4%(u3%u8-u4%u7)+u6%(u3%u6-u4%u5))+u2%(u2%(u4%u8-u5%u7)-u3%(u3%u8-u4%u7)+(u3%u5-u4%u4)%u6)+u3%(u5%u8-u6%u7)-u4%(u4%u8-u5%u7)-u4%(u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4))+u6%(u4%u6-u5%u5))/det;
vec b43= (u1%(u1%(u5%u8-u6%u7)-u3%(u3%u8-u4%u7)+u5%(u3%u6-u4%u5))-u2%(u1%(u4%u8-u5%u7)-u2%(u3%u8-u4%u7)+u5%(u3%u5-u4%u4))-u2%(u5%u8-u6%u7)+u3%(u4%u8-u5%u7)+u4%(u1%(u4%u6-u5%u5)-u2%(u3%u6-u4%u5)+u3%(u3%u5-u4%u4))-u5%(u4%u6-u5%u5))/det;
vec b44= (-u1%(u1%(u4%u8-u6%u6)-u3%(u2%u8-u4%u6)+u5%(u2%u6-u4%u4))+u2%(u1%(u3%u8-u5%u6)-u2%(u2%u8-u4%u6)+u5%(u2%u5-u3%u4))+u2%(u4%u8-u6%u6)-u3%(u3%u8-u5%u6)-u4%(u1%(u3%u6-u4%u5)-u2%(u2%u6-u4%u4)+u3%(u2%u5-u3%u4))+u5%(u3%u6-u4%u5))/det;
vec b45= (u1%(u1%(u4%u7-u5%u6)-u3%(u2%u7-u3%u6)+u5%(u2%u5-u3%u4))-u2%(u1%(u3%u7-u4%u6)-u2%(u2%u7-u3%u6)+(u2%u4-u3%u3)%u5)-u2%(u4%u7-u5%u6)+u3%(u3%u7-u4%u6)+u4%(u1%(u3%u5-u4%u4)-u2%(u2%u5-u3%u4)+u3%(u2%u4-u3%u3))-u5%(u3%u5-u4%u4))/det;
vec b51= (u1%(u3%(u5%u7-u6%u6)-u4%(u4%u7-u5%u6)+u5%(u4%u6-u5%u5))-u2%(u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)+u5%(u3%u6-u4%u5))+u3%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u4%u6)+u5%(u3%u5-u4%u4))-u4%(u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4)))/det;
vec b52= (u1%(u2%(u5%u7-u6%u6)-u4%(u3%u7-u4%u6)+u5%(u3%u6-u4%u5))-u2%(u2%(u4%u7-u5%u6)-u3%(u3%u7-u4%u6)+u5%(u3%u5-u4%u4))-u3%(u5%u7-u6%u6)+u4%(u4%u7-u5%u6)+u3%(u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)+u4%(u3%u5-u4%u4))-u5%(u4%u6-u5%u5))/det;
vec b53= (-u1%(u1%(u5%u7-u6%u6)-u3%(u3%u7-u4%u6)+u4%(u3%u6-u4%u5))+u2%(u1%(u4%u7-u5%u6)-u2%(u3%u7-u4%u6)+u4%(u3%u5-u4%u4))+u2%(u5%u7-u6%u6)-u3%(u4%u7-u5%u6)-u3%(u1%(u4%u6-u5%u5)-u2%(u3%u6-u4%u5)+u3%(u3%u5-u4%u4))+u4%(u4%u6-u5%u5))/det;
vec b54= (u1%(u1%(u4%u7-u5%u6)-u3%(u2%u7-u4%u5)+u4%(u2%u6-u4%u4))-u2%(u1%(u3%u7-u5%u5)-u2%(u2%u7-u4%u5)+u4%(u2%u5-u3%u4))-u2%(u4%u7-u5%u6)+u3%(u3%u7-u5%u5)+u3%(u1%(u3%u6-u4%u5)-u2%(u2%u6-u4%u4)+u3%(u2%u5-u3%u4))-u4%(u3%u6-u4%u5))/det;
vec b55= (-u1%(u1%(u4%u6-u5%u5)-u3%(u2%u6-u3%u5)+u4%(u2%u5-u3%u4))+u2%(u1%(u3%u6-u4%u5)-u2%(u2%u6-u3%u5)+u4%(u2%u4-u3%u3))+u2%(u4%u6-u5%u5)-u3%(u3%u6-u4%u5)-u3%(u1%(u3%u5-u4%u4)-u2%(u2%u5-u3%u4)+u3%(u2%u4-u3%u3))+u4%(u3%u5-u4%u4))/det;
'
switch(Dindex,
{
Dpart='
vec p=(1.0/3.0) *(3*(x0.col(3)/6.0)%x0.col(1) - pow(x0.col(2)/2.0,2))/pow(x0.col(3)/6.0,2);
vec q=(1.0/27.0)*(27*pow(x0.col(3)/6.0,2)%(x0.col(0)-Xt) - 9*(x0.col(3)/6.0)%(x0.col(2)/2.0)%x0.col(1) + 2*pow(x0.col(2)/2.0,3))/pow(x0.col(3)/6.0,3);
vec chk=pow(q,2)/4.0 + pow(p,3)/27.0;
vec th=-(x0.col(2)/2.0)/(3*(x0.col(3)/6.0))+pow(-q/2.0+sqrt(chk),(1.0/3.0))-pow(q/2.0+sqrt(chk),(1.0/3.0));
vec K =x0.col(0)%th+(x0.col(1)%th%th)/2.0+(x0.col(2)%th%th%th)/6.0 +(x0.col(3)%th%th%th%th)/24.0;
vec K1=x0.col(0) +(x0.col(1)%th) +(x0.col(2)%th%th)/2.0 +(x0.col(3)%th%th%th)/6.0;
vec K2=x0.col(1) +(x0.col(2)%th) +(x0.col(3)%th%th)/2.0;
vec val=-0.5*log(2*3.141592653589793*K2)+(K-th%K1);
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart=
'
vec betas1 =+b12+2*b13%u1+3*b14%u2+4*b15%u3;
vec betas2 =+b22+2*b23%u1+3*b24%u2+4*b25%u3;
vec betas3 =+b32+2*b33%u1+3*b34%u2+4*b35%u3;
vec betas4 =+b42+2*b43%u1+3*b44%u2+4*b45%u3;
vec betas5 =+b52+2*b53%u1+3*b54%u2+4*b55%u3;
vec lo =-(0.5/(u1-lower))%(sqrt(exp(2*alpha)+4*pow((u1-lower),2))-exp(alpha));
vec up =-(0.5/(u1-upper))%(sqrt(exp(2*alpha)+4*pow((u1-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas1%pow(tau,1)-0.5*betas2%pow(tau,2)-0.333333333333333333*betas3%pow(tau,3)-0.25*betas4%pow(tau,4)-0.2*betas5%pow(tau,5))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas1%pow(tau,1)-0.5*betas2%pow(tau,2)-0.333333333333333333*betas3%pow(tau,3)-0.25*betas4%pow(tau,4)-0.2*betas5%pow(tau,5))%rho%DT;
}
vec val =((-betas1%pow(Xt,1)-0.5*betas2%pow(Xt,2)-0.333333333333333333*betas3%pow(Xt,3)-0.25*betas4%pow(Xt,4)-0.2*betas5%pow(Xt,5))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+b11+2*b12%u1+3*b13%u2+4*b14%u3+4*b15%u4;
vec betas2 =+b21+2*b22%u1+3*b23%u2+4*b24%u3+4*b25%u4;
vec betas3 =+b31+2*b32%u1+3*b33%u2+4*b34%u3+4*b35%u4;
vec betas4 =+b41+2*b42%u1+3*b43%u2+4*b44%u3+4*b45%u4;
vec betas5 =+b51+2*b52%u1+3*b53%u2+4*b54%u3+4*b55%u4;
vec lo =-(0.5/(u1-lower))%(sqrt(exp(2*alpha)+4*pow((u1-lower),2))-exp(alpha));
vec up =-(0.5/(u1-upper))%(sqrt(exp(2*alpha)+4*pow((u1-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas1%log(tau)+(-betas2%tau-0.5*betas3%pow(tau,2)-0.33333333333333333333333*betas4%pow(tau,3)-0.25*betas5%pow(tau,4)))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas1%log(tau)+(-betas2%tau-0.5*betas3%pow(tau,2)-0.33333333333333333333333*betas4%pow(tau,3)-0.25*betas5%pow(tau,4)))%rho%DT;
}
vec val =((-betas1%log(Xt)+(-betas2%Xt-0.5*betas3%pow(Xt,2)-0.33333333333333333333333*betas4%pow(Xt,3)-0.25*betas5%pow(Xt,4)))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+2*b11%u1+3*b12%u2+4*b13%u3+5*b14%u4+6*b15%u5;
vec betas2 =+2*b21%u1+3*b22%u2+4*b23%u3+5*b24%u4+6*b25%u5;
vec betas3 =+2*b31%u1+3*b32%u2+4*b33%u3+5*b34%u4+6*b35%u5;
vec betas4 =+2*b41%u1+3*b42%u2+4*b43%u3+5*b44%u4+6*b45%u5;
vec betas5 =+2*b51%u1+3*b52%u2+4*b53%u3+5*b54%u4+6*b55%u5;
vec lo =-(0.5/(u1-lower))%(sqrt(exp(2*alpha)+4*pow((u1-lower),2))-exp(alpha));
vec up =-(0.5/(u1-upper))%(sqrt(exp(2*alpha)+4*pow((u1-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas2%log(tau)+(betas1/tau-betas3%tau-0.5*betas4%pow(tau,2)-0.3333333333333333*betas5%pow(tau,3)))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas2%log(tau)+(betas1/tau-betas3%tau-0.5*betas4%pow(tau,2)-0.3333333333333333*betas5%pow(tau,3)))%rho%DT;
}
vec val =((-betas2%log(Xt)+(betas1/Xt-betas3%Xt-0.5*betas4%pow(Xt,2)-0.3333333333333333*betas5%pow(Xt,3)))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
},
{
Dpart='
vec betas1 =+b11%(1-2*u1)+b12%(2*u1-3*u2)+b13%(3*u2-4*u3)+b14%(4*u3-5*u4)+b15%(5*u4-6*u5);
vec betas2 =+b21%(1-2*u1)+b22%(2*u1-3*u2)+b23%(3*u2-4*u3)+b24%(4*u3-5*u4)+b25%(5*u4-6*u5);
vec betas3 =+b31%(1-2*u1)+b32%(2*u1-3*u2)+b33%(3*u2-4*u3)+b34%(4*u3-5*u4)+b35%(5*u4-6*u5);
vec betas4 =+b41%(1-2*u1)+b42%(2*u1-3*u2)+b43%(3*u2-4*u3)+b44%(4*u3-5*u4)+b45%(5*u4-6*u5);
vec betas5 =+b51%(1-2*u1)+b52%(2*u1-3*u2)+b53%(3*u2-4*u3)+b54%(4*u3-5*u4)+b55%(5*u4-6*u5);
vec lo =-(0.5/(u1-lower))%(sqrt(exp(2*alpha)+4*pow((u1-lower),2))-exp(alpha));
vec up =-(0.5/(u1-upper))%(sqrt(exp(2*alpha)+4*pow((u1-upper),2))-exp(alpha));
vec DT = (up-lo)/(1.00*P);
vec tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
vec rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
vec K=exp(-betas2%log(tau)+(betas1/tau-betas3%tau-0.5*betas4%pow(tau,2)-0.3333333333333333*betas5%pow(tau,3)))%rho%DT;
for (int i = 1; i <= P; i++)
{
lo=lo+DT;
tau = exp(alpha)*lo/(1-pow(lo,2))+u1;
rho = exp(alpha)*(1+pow(lo,2))/pow((1-pow(lo,2)),2);
K=K+exp(-betas2%log(tau)+(betas1/tau-betas3%tau-0.5*betas4%pow(tau,2)-0.3333333333333333*betas5%pow(tau,3)))%rho%DT;
}
vec val = ((-betas2%log(Xt)+(betas1/Xt-betas3%Xt-0.5*betas4%pow(Xt,2)-0.3333333333333333*betas5%pow(Xt,3)))-log(K));
List ret;
ret["like"] = val;
ret["max"] = whch;
return(ret);
}
'
})
if(Dindex!=1)
{
Dpart=paste(Inv,Dpart)
}
}
strcheck=function(str)
{
k=0
if(length(grep('/',str))!=0)
{
warning('C++ uses integer division when denominators are of type int. Use decimal values if possible (e.g. 0.5 i.s.o. 1/2).',call. = F)
k=k+1
}
if(length(grep('\\^',str))!=0)
{
stop('^-Operator not defined in C++: Use pow(x,p) instead (e.g. pow(x,2) i.s.o. x^2).',call. = F)
k=k+1
}
return(k)
}
for(i in which(func.list==1))
{
strcheck(body(namess[i])[2])
}
if(state1)
{
if(!homo.res)
{
delt=cbind(diff(T.seq)/mesh,diff(T.seq)/mesh)
if(!is.null(exclude))
{
diffs=diff(T.seq)/mesh
diffs[excl]=1/20/mesh
delt=cbind(diffs,diffs)
}
}
MAT=rbind(
c('(1+0*a.col(0))','a.col(0)' ,''),
c('','2*a.col(1)','(1+0*a.col(0))'),
c('','3*a.col(2)',''),
c('','4*a.col(3)',''))
namess2=c('G0','G1','Q0')
func.list2=rep(0,3)
obs=objects(pos=1)
for(i in 1:3)
{
if(sum(obs==namess2[i])){func.list2[i]=1}
}
func.list.timehomo=func.list2*0
for(i in which(func.list2==1))
{
result=eval(body(namess2[i]))
func.list.timehomo[i]=2-(sum(diff(result)==0)==(length(result)-1))
}
if(any(func.list.timehomo==2)){homo=F}
dims=rep('(',2)
for(i in 1:2)
{
for(j in which(func.list2==1))
{
if(MAT[i,j]!='')
{
dims[i]=paste0(dims[i],'+(',body(namess2[j])[2],')',c('*','%')[func.list.timehomo[j]],MAT[i,j])
}
}
dims[i]=paste0(dims[i],')')
}
if(any(dims=='()')){dims[which(dims=='()')]='0*a.col(0)'}
for(i in 1:2)
{
dims[i]=paste0(paste0(paste0(' atemp.col(',i-1,')='),dims[i]),';')
}
txt.full=paste(fpart,'\n',dims[1],'\n',dims[2],ODEpart,Dpart)
type.sol =" Generalized Ornstein-Uhlenbeck "
if(wrt){write(txt.full,file='GQD.mcmc.cpp')}
stre="Compiling C++ code. Please wait."
cat(stre, " \r")
flush.console()
sourceCpp(code=txt.full)
cat(' ','\r')
}
if(state2)
{
if((!homo.res)&(TR.order==4))
{
delt=cbind(diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh)
if(!is.null(exclude))
{
diffs=diff(T.seq)/mesh
diffs[excl]=1/20/mesh
delt=cbind(diffs,diffs,diffs,diffs)
}
}
if((!homo.res)&(TR.order==6))
{
delt=cbind(diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh)
if(!is.null(exclude))
{
diffs=diff(T.seq)/mesh
diffs[excl]=1/20/mesh
delt=cbind(diffs,diffs,diffs,diffs,diffs,diffs)
}
}
if((!homo.res)&(TR.order==8))
{
delt=cbind(diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh,diff(T.seq)/mesh)
if(!is.null(exclude))
{
diffs=diff(T.seq)/mesh
diffs[excl]=1/20/mesh
delt=cbind(diffs,diffs,diffs,diffs,diffs,diffs,diffs,diffs)
}
}
if(TR.order==4)
{
MAT=rbind(
c('(1+0*a.col(0))','a.col(0)','(a.col(1)+a.col(0)%a.col(0))','','',''),
c('','(2*a.col(1))','(2*a.col(2)+4*a.col(0)%a.col(1))','(1+0*a.col(0))','a.col(0)','(a.col(1)+a.col(0)%a.col(0))'),
c('','(3*a.col(2))','(3*a.col(3)+6*a.col(0)%a.col(2)+6*a.col(1)%a.col(1))','','(3*a.col(1))','(3*a.col(2)+6*a.col(0)%a.col(1))'),
c('','(4*a.col(3))','(8*a.col(0)%a.col(3)+24*a.col(1)%a.col(2))','','(6*a.col(2))','(6*a.col(3)+12*a.col(0)%a.col(2)+12*a.col(1)%a.col(1))'))
}
if(TR.order==6)
{
MAT = rbind(
c('(1+0*a.col(0))','(a.col(0))' ,'(1*a.col(1)+1*a.col(0)%a.col(0))' ,'','',''),
c('','(2*a.col(1))','(2*a.col(2)+4*a.col(0)%a.col(1))' ,'(1+0*a.col(0))','( a.col(0))','(a.col(1)+a.col(0)%a.col(0))'),
c('','(3*a.col(2))','(3*a.col(3)+6*a.col(0)%a.col(2)+6*a.col(1)%a.col(1))' ,'','( 3*a.col(1))','(3*a.col(2)+6*a.col(0)%a.col(1))'),
c('','(4*a.col(3))','(4*a.col(4)+8*a.col(0)%a.col(3)+24*a.col(1)%a.col(2))' ,'','( 6*a.col(2))','(6*a.col(3)+12*a.col(0)%a.col(2)+12*a.col(1)%a.col(1))'),
c('','(5*a.col(4))','(5*a.col(5)+10*a.col(0)%a.col(4)+40*a.col(1)%a.col(3)+30*a.col(2)%a.col(2))' ,'','(10*a.col(3))','(10*a.col(4)+20*a.col(0)%a.col(3)+60*a.col(1)%a.col(2))'),
c('','(6*a.col(5))','( +12*a.col(0)%a.col(5)+60*a.col(1)%a.col(4)+120*a.col(2)%a.col(3))','','(15*a.col(4))','(15*a.col(5)+30*a.col(0)%a.col(4)+120*a.col(1)%a.col(3)+90*a.col(2)%a.col(2))'))
}
if(TR.order==8)
{
MAT = rbind(
c('(1+0*a.col(0))',' (a.col(0))','(1*a.col(1) +1*a.col(0)%a.col(0))' ,'','',''),
c('','(2*a.col(1))','(2*a.col(2) +4*a.col(0)%a.col(1))' ,'(1+0*a.col(0))','( a.col(0))','(a.col(1)+a.col(0)%a.col(0))'),
c('','(3*a.col(2))','(3*a.col(3) +6*a.col(0)%a.col(2) +6*a.col(1)%a.col(1))' ,'','( 3*a.col(1))','( 3*a.col(2) +6*a.col(0)%a.col(1))'),
c('','(4*a.col(3))','(4*a.col(4) +8*a.col(0)%a.col(3) +24*a.col(1)%a.col(2))' ,'','( 6*a.col(2))','( 6*a.col(3)+12*a.col(0)%a.col(2) +12*a.col(1)%a.col(1))'),
c('','(5*a.col(4))','(5*a.col(5)+10*a.col(0)%a.col(4) +40*a.col(1)%a.col(3) +30*a.col(2)%a.col(2))' ,'','(10*a.col(3))','(10*a.col(4)+20*a.col(0)%a.col(3) +60*a.col(1)%a.col(2))'),
c('','(6*a.col(5))','(6*a.col(6)+12*a.col(0)%a.col(5) +60*a.col(1)%a.col(4) +120*a.col(2)%a.col(3))' ,'','(15*a.col(4))','(15*a.col(5)+30*a.col(0)%a.col(4)+120*a.col(1)%a.col(3) +90*a.col(2)%a.col(2))'),
c('','(7*a.col(6))','(7*a.col(7)+14*a.col(0)%a.col(6) +84*a.col(1)%a.col(5) +210*a.col(2)%a.col(4)+140*a.col(3)%a.col(3))','','(21*a.col(5))','(21*a.col(6)+42*a.col(0)%a.col(5)+210*a.col(1)%a.col(4)+420*a.col(2)%a.col(3))'),
c('','(8*a.col(7))','( +16*a.col(0)%a.col(7)+112*a.col(1)%a.col(6) +336*a.col(2)%a.col(5)+560*a.col(4)%a.col(3))','','(28*a.col(6))','(28*a.col(7)+56*a.col(0)%a.col(6)+336*a.col(1)%a.col(5)+840*a.col(2)%a.col(4)+560*a.col(3)%a.col(3))'))
}
namess2=c('G0','G1','G2','Q0','Q1','Q2')
func.list2=rep(0,6)
obs=objects(pos=1)
for(i in 1:6)
{
if(sum(obs==namess[i])){func.list2[i]=1}
}
func.list.timehomo=func.list2*0
for(i in which(func.list2==1))
{
result=eval(body(namess2[i]))
func.list.timehomo[i]=2-(sum(diff(result)==0)==(length(result)-1))
}
if(any(func.list.timehomo==2)){homo=F}
dims=rep('(',TR.order)
for(i in 1:TR.order)
{
for(j in which(func.list2==1))
{
if(MAT[i,j]!='')
{
dims[i]=paste0(dims[i],'+(',body(namess2[j])[2],')',c('*','%')[func.list.timehomo[j]],MAT[i,j])
}
}
dims[i]=paste0(dims[i],')')
}
if(any(dims=='()')){dims[which(dims=='()')]='0*a.col(0)'}
for(i in 1:TR.order)
{
dims[i]=paste0(paste0(paste0(' atemp.col(',i-1,')='),dims[i]),';')
}
if(TR.order==4)
{
txt.full=paste(fpart,'\n',dims[1],'\n',dims[2],'\n',dims[3],'\n',dims[4],ODEpart,Dpart)
}
if(TR.order==6)
{
txt.full=paste(fpart,'\n',dims[1],'\n',dims[2],'\n',dims[3],'\n',dims[4],'\n',dims[5],'\n',dims[6],ODEpart,Dpart)
}
if(TR.order==8)
{
txt.full=paste(fpart,'\n',dims[1],'\n',dims[2],'\n',dims[3],'\n',dims[4],'\n',dims[5],'\n',dims[6],'\n',dims[7],'\n',dims[8],ODEpart,Dpart)
}
type.sol =" Generalized Quadratic Diffusion (GQD) "
if(wrt){write(txt.full,file='GQD.mcmc.cpp')}
stre="Compiling C++ code. Please wait."
cat(stre, " \r")
flush.console()
sourceCpp(code=txt.full)
cat(' ','\r')
}
if(sum(objects(pos=1)=='priors')==1)
{
pp=function(theta){}
body(pp)=parse(text =body(priors)[2])
prior.list=paste0('d(theta)',':',paste0(body(priors)[2]))
}
if(sum(objects(pos=1)=='priors')==0)
{
prior.list=paste0('d(theta)',':',' None.')
pp=function(theta){1}
}
if(length(pp(theta))!=1){stop("Prior density must return only a single value!")}
namess4=namess
trim <- function (x) gsub("([[:space:]])", "", x)
for(i in 1:6)
{
if(sum(obs==namess4[i]))
{
namess4[i]=paste0(namess4[i],' : ',trim(body(namess4[i])[2]))
}
}
namess4=matrix(namess4,length(namess4),1)
prior.list = trim(prior.list)
buffer0=c('================================================================')
buffer1=c('----------------------------------------------------------------')
buffer2=c('................................................................')
buffer3=c('... ... ... ... ... ... ... ... ... ... ... ')
buffer4=c('_____________________ Drift Coefficients _______________________')
buffer5=c('___________________ Diffusion Coefficients _____________________')
buffer6=c('_____________________ Prior Distributions ______________________')
buffer7=c('_______________________ Model/Chain Info _______________________')
Info=c(buffer0,type.sol,buffer0,buffer4,namess4[1:3],buffer5,namess4[4:6],buffer6,'',prior.list)
Info=data.frame(matrix(Info,length(Info),1))
colnames(Info)=''
if(print.output)
{
print(Info,row.names = FALSE,right=F)
}
tme=Sys.time()
par.matrix=matrix(0,length(theta),updates)
muvec = theta
covvec = diag(sds^2)
errs = rep(0,updates)
ll = rep(0,updates)
acc=ll
kk=0
par.matrix[,1]=theta
prop.matrix = par.matrix
retries = 0
max.retries = 0
retry.count = 0
retry.indexes = c()
success = TRUE
rs=solver(X[-nnn],X[-1],c(0,theta),mesh,delt,nnn-1,T.seq[-nnn],P,alpha,lower,upper,TR.order)
if(is.na(sum(rs$like)))
{
retry.count =1
while(is.na(sum(rs$like))&&(retry.count<=10))
{
theta.start=theta+rnorm(length(theta),sd=sds)
rs = solver(X[-nnn],X[-1],c(0,theta.start),mesh,delt,nnn-1,T.seq[-nnn],P,alpha,lower,upper,TR.order)
if(is.na(sum(rs$like[-excl]))){retry.count=retry.count+1}
}
}
lold=sum(rs$like)
if(rs$max>0.1){warning('Probable that starting values were chosen poorly.')}
errs[1] =rs$max
ll[1]=lold
if(adapt==0)
{
pb <- txtProgressBar(1,updates,1,style = 1,width = 65)
failed.chain=F
i=2
while(i<=updates)
{
theta.temp=theta
theta=theta+rnorm(length(theta),sd=sds)
prop.matrix[,i] = theta
rs = solver(X[-nnn],X[-1],c(0,theta),mesh,delt,nnn-1,T.seq[-nnn],P,alpha,lower,upper,TR.order)
lnew=sum(rs$like[-excl])
rat=min(exp(lnew-lold)*pp(theta)/pp(theta.temp),1)
if(is.na(rat))
{
retry.count =1
retries=retries+1
retry.indexes[retries] = i
max.retries=max.retries+1
while(is.na(rat)&&(retry.count<=10))
{
i = max(i-10,2)
theta = par.matrix[,i]
theta=theta+rnorm(length(theta),sd=sds)
prop.matrix[,i] = theta
rs = solver(X[-nnn],X[-1],c(0,theta),mesh,delt,nnn-1,T.seq[-nnn],P,alpha,lower,upper,TR.order)
lnew=sum(rs$like[-excl])
rat=min(exp(lnew-lold)*pp(theta)/pp(theta.temp),1)
if(is.na(rat)){retry.count=retry.count+1}
}
}
u=runif(1)
is.true =(rat>u)
is.false=!is.true
theta=theta*is.true+theta.temp*is.false
lold=lnew*is.true +lold*is.false
errs[i]=rs$max*is.true+errs[i-1]*is.false
par.matrix[,i]=theta
ll[i]=lold
kk=kk+is.true
acc[i]=is.true
if(max.retries>2000){print('Fail: Failed evaluation limit exceeded!');failed.chain=T;break;}
if(any(is.na(theta))){print('Fail: Samples were NA! ');failed.chain=T;break;}
setTxtProgressBar(pb, i)
i = i+1
}
close(pb)
acc = cumsum(acc)/(1:updates)
}
if(adapt!=0)
{
pb <- txtProgressBar(1,updates,1,style = 1,width = 65)
failed.chain=F
for(i in 2:updates)
{
theta.temp=theta
if(i>min(5000,round(burns/2)))
{
theta=theta+(1-adapt)*rnorm(length(theta),sd=sqrt(2.38^2/length(theta)*diag(covvec)))+adapt*rnorm(length(theta),sd=sqrt(0.1^2/length(theta)*diag(covvec)))
}else
{
theta=theta+rnorm(length(theta),sd=sds)
}
prop.matrix[,i] = theta
rs =solver(X[-nnn],X[-1],c(0,theta),mesh,delt,nnn-1,T.seq[-nnn],P,alpha,lower,upper,TR.order)
lnew=sum(rs$like[-excl])
rat=min(exp(lnew-lold)*pp(theta)/pp(theta.temp),1)
u=runif(1)
is.true =(rat>u)
is.false=!is.true
theta=theta*is.true+theta.temp*is.false
lold=lnew*is.true +lold*is.false
errs[i]=rs$max*is.true+errs[i-1]*is.false
par.matrix[,i]=theta
ll[i]=lold
kk=kk+is.true
acc[i]=kk/i
muvec=muvec +1/(i)*(theta-muvec)
covvec = covvec+1/(i)*((theta-muvec)%*%t(theta-muvec)-covvec)
if(any(is.na(theta))){print('Fail');failed.chain=T;break;}
setTxtProgressBar(pb, i)
}
close(pb)
}
tme.eval = function(start_time)
{
start_time = as.POSIXct(start_time)
dt = difftime(Sys.time(), start_time, units="secs")
format(.POSIXct(dt,tz="GMT"), "%H:%M:%S")
}
tme=tme.eval(tme)
if(dim(par.matrix)[1]>1)
{
theta =apply(par.matrix[,-c(1:burns)],1,mean)
}
if(dim(par.matrix)[1]==1)
{
theta=mean(par.matrix[,-c(1:burns)])
}
meanD=mean(-2*ll[-c(1:burns)])
rs=solver(X[-nnn],X[-1],c(0,theta),mesh,delt,nnn-1,T.seq[-nnn],P,alpha,lower,upper,TR.order)
pd=meanD-(-2*sum(rs$like[-excl]))
DIC=meanD+pd
actual.p=length(theta)
model.inf=list(elapsed.time=tme,time.homogeneous=c('Yes','No')[2-homo],p=actual.p,DIC=DIC,pd=pd,N=length(X)-length(excl)+1,Tag=Tag,burns=burns)
Info2=c( buffer7,
paste0("Chain Updates : ",updates),
paste0("Burned Updates : ",burns),
paste0("Time Homogeneous : ",c('Yes','No')[2-homo]),
paste0("Data Resolution : ",c(paste0('Homogeneous: dt=',round(max(diff(T.seq)[-excl]),4)),paste0('Variable: min(dt)=',round(min(diff(T.seq)[-excl]),4),', max(dt)=',round(max(diff(T.seq)[-excl]),4)))[2-homo.res]),
paste0("
paste0("Density approx. : ",sol.state),
paste0('Elapsed time : ',tme),
buffer3,
paste0("dim(theta) : ",round(actual.p,3)),
paste0("DIC : ",round(DIC,3)),
paste0("pd (eff. dim(theta)): ",round(pd,3)),
buffer1)
Info2=data.frame(matrix(Info2,length(Info2),1))
colnames(Info2)=''
if(print.output)
{
print(Info2,row.names = FALSE,right=F)
}
if(plot.chain)
{
nper=length(theta)
if(nper==1){par(mfrow=c(1,2))}
if(nper==2){par(mfrow=c(2,2))}
if(nper==3){par(mfrow=c(2,2))}
if(nper>3)
{
d1=1:((nper)+1)
d2=d1
O=outer(d1,d2)
test=O-((nper)+1)
test[test<0]=100
test=test[1:4,1:4]
test
wh=which(test==min(test))
d1=d1[col(test)[wh[1]]]
d2=d2[row(test)[wh[1]]]
par(mfrow=c(d1,d2))
}
if(palette=='mono')
{
cols =rep('
}else{
cols=rainbow_hcl(nper, start = 10, end = 275,c=100,l=70)
}
ylabs=paste0('theta[',1:nper,']')
for(i in 1:nper)
{
plot(prop.matrix[i,],col='gray90',type='s',main=ylabs[i],xlab='Iteration',ylab='')
lines(par.matrix[i,],col=cols[i],type='s')
abline(v=burns,lty='dotdash')
if(adapt!=0){abline(v=min(5000,round(burns/2)),lty='dotted',col='red')}
}
plot(acc,type='l',ylim=c(0,1),col='darkblue',main='Accept. Rate',xlab='Iteration',ylab='%/100')
abline(h=seq(0,1,1/10),lty='dotted')
abline(v=burns,lty='dotdash')
abline(h=0.4,lty='solid',col='red',lwd=1.2)
abline(h=0.2,lty='solid',col='red',lwd=1.2)
box()
}
ret=list(par.matrix=t(par.matrix),acceptance.rate=acc,elapsed.time=tme,model.info=model.inf,failed.chain=failed.chain,covvec=covvec,prop.matrix=t(prop.matrix),errors = errs)
class(ret) = 'GQD.mcmc'
return(ret)
}
|
abs_units = c("cm", "inches", "mm", "points", "picas", "bigpts", "dida", "cicero",
"scaledpts", "lines", "char", "strwidth", "strheight", "grobwidth",
"grobheight", "strascent", "strdescent", "mylines", "mychar",
"mystrwidth", "mystrheight", "centimetre", "centimetres", "centimeter",
"centimeters", "in", "inch", "line", "millimetre", "millimetres",
"millimeter", "millimeters", "point", "pt")
.is_abs_unit.unit = function(x) {
unit = unitType(x)
if(all(unit %in% abs_units)) {
return(TRUE)
} else {
return(FALSE)
}
}
.is_abs_unit.unit.list = function(x) {
all(sapply(x, function(y) {
if(inherits(y, "unit.arithmetic")) .is_abs_unit.unit.arithmetic(y)
else if(inherits(y, "unit.list")) .is_abs_unit.unit.list(y)
else if(inherits(y, "unit")) .is_abs_unit.unit(y)
else FALSE
}))
}
.is_abs_unit.unit.arithmetic = function(x) {
all(sapply(x, function(y) {
if(inherits(y, "unit.arithmetic")) .is_abs_unit.unit.arithmetic(y)
else if(inherits(y, "unit.list")) .is_abs_unit.unit.list(y)
else if(inherits(y, "unit")) .is_abs_unit.unit(y)
else TRUE
}))
}
.is_abs_unit.default = function(x) {
FALSE
}
is_abs_unit = function(u) {
NULL
}
is_abs_unit_v3 = function(u) {
if(inherits(u, "unit.arithmetic")) .is_abs_unit.unit.arithmetic(u)
else if(inherits(u, "unit.list")) .is_abs_unit.unit.list(u)
else if(inherits(u, "unit")) .is_abs_unit.unit(u)
else FALSE
}
is_abs_unit_v4 = function(u) {
u = unitType(u, recurse = TRUE)
u = unlist(u)
all(u %in% abs_units)
}
rv = R.Version()
if(getRversion() >= "4.0.0" && as.numeric(rv$`svn rev`) >= 77889) {
is_abs_unit = is_abs_unit_v4
} else {
is_abs_unit = is_abs_unit_v3
}
|
testIndQBinom = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels = NULL , hash = FALSE, stat_hash = NULL,
pvalue_hash = NULL) {
pvalue = log(1);
stat = 0;
csIndex[which(is.na(csIndex))] = 0;
if( hash ) {
csIndex2 = csIndex[which(csIndex!=0)]
csIndex2 = sort(csIndex2)
xcs = c(xIndex,csIndex2)
key = paste(as.character(xcs) , collapse=" ");
if( !is.null(stat_hash[key]) ){
stat = stat_hash[key];
pvalue = pvalue_hash[key];
results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
}
if ( !is.na( match(xIndex, csIndex) ) ) {
if( hash ) {
stat_hash[key] <- 0;
pvalue_hash[key] <- log(1);
}
results <- list(pvalue = log(1), stat = 0, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
if( any(xIndex < 0) || any(csIndex < 0) ) {
message(paste("error in testIndPois : wrong input of xIndex or csIndex"))
results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
xIndex = unique(xIndex);
csIndex = unique(csIndex);
x = dataset[ , xIndex];
cs = dataset[ , csIndex];
if ( length(cs)!=0 ) {
if ( is.null(dim(cs)[2]) ) {
if ( identical(x, cs) ) {
if ( hash ) {
stat_hash[key] <- 0;
pvalue_hash[key] <- log(1);
}
results <- list(pvalue = log(1), stat = 0, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results)
}
} else {
for ( col in 1:dim(cs)[2] ) {
if (identical(x, cs[, col]) ) {
if( hash ) {
stat_hash[key] <- 0
pvalue_hash[key] <- log(1)
}
results <- list(pvalue = log(1), stat = 0, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
}
}
}
if (length(cs) == 0) {
fit1 <- glm(target ~ 1, family = quasibinomial(link = logit), weights = wei ,model = FALSE)
fit2 <- glm(target ~ x, family = quasibinomial(link = logit), weights = wei ,model = FALSE)
} else {
fit1 = glm(target ~., data = as.data.frame( cs ), family = quasibinomial(link = logit), weights = wei, model = FALSE)
fit2 = glm(target ~., data = as.data.frame( dataset[, c(csIndex, xIndex)] ), family = quasibinomial(link = logit), weights = wei, model = FALSE)
}
mod <- anova(fit1, fit2, test = "F")
stat <- mod[2, 5]
df1 <- mod[2, 3]
df2 <- mod[2, 1]
pvalue = pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE )
if ( is.na(pvalue) || is.na(stat) ) {
pvalue = log(1);
stat = 0;
} else {
if ( hash ) {
stat_hash[key] <- stat;
pvalue_hash[key] <- pvalue;
}
}
results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results)
}
|
cutoffSensitivityPlot <- function(predTest,depTest,metric=c("accuracy","expMisclassCost","misclassCost"),costType=c("costRatio","costMatrix","costVector"),costs=NULL,resolution=1/50) {
checkDepVector(depTest)
if (metric=="expMisclassCost" && costType=="costVector") stop("Cost vectors are not supported for the misclassification cost. Use expMisclassCost.")
tmp <- unique(depTest)
depvalues <- tmp[order(tmp)]
yp = cbind(as.factor(depTest),predTest)
perc_list <- seq(0,1,resolution)[-1]
perf = mat.or.vec(length(perc_list),1)
for (perc_idx in 1:length(perc_list)) {
if (metric=="accuracy") {
perf[perc_idx]<-dynAccuracy(predTest,depTest,dyn.cutoff=FALSE,cutoff=perc_list[perc_idx])[[1]]
multiplier<-1
} else if (metric=="expMisclassCost") {
perf[perc_idx]<-expMisclassCost(predTest,depTest,costType=costType, costs=costs,cutoff=perc_list[perc_idx])[[1]]
multiplier<- -1
} else if (metric=="misclassCost") {
perf[perc_idx]<-misclassCost(predTest,depTest,costType=costType, costs=costs,cutoff=perc_list[perc_idx])[[1]]
multiplier<- -1
}
}
plot(perc_list,perf,type="b",xlab="Cutoff",ylab=metric, main="Cutoff sensitivity Chart")
perf2<-multiplier*perf
lines(perc_list[which(perf2==max(perf2))],rep(max(perf2),length(perc_list[which(perf2==max(perf2))])),type="b",col=34)
lines(perc_list[which(perf2==max(perf2))],rep(max(perf2),length(perc_list[which(perf2==max(perf2))])),type="h",col=34)
text(perc_list[which(perf2==max(perf2))][1],abs(min(perf)), paste("optimal cutoff =",perc_list[which(perf2==max(perf2))][1]), cex=1)
}
|
nobs.tvgarch <- function (object, ...)
{
return(length(object$sigma2))
}
|
set.genomic.region.subregion <- function(x, regions, subregions, split = TRUE) {
if(!is.factor(regions$Name) | !is.factor(subregions$Name)) stop("regions$Name and subregions$Name should be factors with levels ordered in the genome order")
if(typeof(x@snps$chr) != "integer")
stop("x@snps$chr should be either a vector of integers, or a factor with same levels as regions$Chr")
regions$Start <- regions$Start + 1
w <- duplicated(regions$Name)
if(any(w)) {
regions <- regions[!w,]
}
n <- nrow(regions)
chr1 <- regions$Chr[1:(n-1)]
chr2 <- regions$Chr[2:n]
b <- (chr1 < chr2) | (chr1 == chr2 & regions$Start[1:(n-1)] <= regions$Start[2:n])
if(!all(b)) {
regions <- regions[ order(regions$Chr, regions$Start), ]
}
n <- nrow(subregions)
chr1 <- subregions$Chr[1:(n-1)]
chr2 <- subregions$Chr[2:n]
b <- (chr1 < chr2) | (chr1 == chr2 & subregions$Start[1:(n-1)] <= subregions$Start[2:n])
if(!all(b)) {
subregions <- subregions[ order(subregions$Chr, subregions$Start), ]
}
R <- .Call("label_multiple_genes", PACKAGE = "Ravages", regions$Chr, regions$Start, regions$End, x@snps$chr, x@snps$pos)
R.genename <- unlist(lapply(R, function(z) paste(levels(regions$Name)[unlist(z)], collapse=",")))
R.genename[which(R.genename=="")] <- NA
x@snps$genomic.region <- R.genename
x@snps$genomic.region <- factor(x@snps$genomic.region, levels = unique(x@snps$genomic.region))
if(any(grepl(x@snps$genomic.region, pattern = ","))){
x <- bed.matrix.split.genomic.region(x, genomic.region = x@snps$genomic.region, split.pattern = ",")
}
Rsub <- .Call("label_multiple_genes", PACKAGE = "Ravages", subregions$Chr, subregions$Start, subregions$End, x@snps$chr, x@snps$pos)
Rsub.genename <- unlist(lapply(Rsub, function(z) paste(subregions$Name[unlist(z)], collapse=",")))
Rsub.genename[which(Rsub.genename=="")] <- NA
x@snps$SubRegion <- Rsub.genename
x@snps$SubRegion <- factor(x@snps$SubRegion, levels = unique(x@snps$SubRegion))
x
}
|
context("ui slider")
test_that("test slider_input", {
expect_is(slider_input("slider_input", 10, 0, 20), "shiny.tag")
expect_error(slider_input())
si_str <- as.character(slider_input("slider_input", 10, 0, 20))
expect_true(grepl(
paste("<div id=\"slider_input\" class=\"ui slider ss-slider labeled\" data-min=\"0\"",
"data-max=\"20\" data-step=\"1\" data-start=\"10\">"),
si_str
))
})
test_that("test sliderInput", {
expect_error(sliderInput("slider_input"), "\"value\" is missing")
si_str <- as.character(sliderInput("slider_input", "Label", 10, 0, 20))
expect_true(grepl(
"<form class=\"ui form \">\n <label>Label</label>\n",
si_str
))
expect_true(grepl(
"data-max=\"0\" data-step=\"1\" data-start=\"20\"></div>",
si_str
))
})
test_that("test range_input", {
expect_is(range_input("range_input", 10, 15, 0, 20), "shiny.tag")
expect_error(range_input())
si_str <- as.character(range_input("range_input", 10, 15, 0, 20))
expect_true(grepl(paste(
"<div id=\"range_input\" class=\"ui range slider ss-slider \" data-min=\"0\" data-max=\"20\"",
"data-step=\"1\" data-start=\"10\" data-end=\"15\">"
), si_str))
})
|
ZAP <- function (mu.link = "log", sigma.link = "logit")
{
mstats <- checklink("mu.link", "ZAP", substitute(mu.link),
c("1/mu^2", "log", "identity"))
dstats <- checklink("sigma.link", "ZAP", substitute(sigma.link),
c("logit", "probit", "cloglog", "cauchit", "log", "own"))
structure(
list(family = c("ZAP", "Zero Adjusted Poisson"),
parameters = list(mu=TRUE, sigma=TRUE),
nopar = 2,
type = "Discrete",
mu.link = as.character(substitute(mu.link)),
sigma.link = as.character(substitute(sigma.link)),
mu.linkfun = mstats$linkfun,
sigma.linkfun = dstats$linkfun,
mu.linkinv = mstats$linkinv,
sigma.linkinv = dstats$linkinv,
mu.dr = mstats$mu.eta,
sigma.dr = dstats$mu.eta,
dldm = function(y,mu,sigma) {dldm0 <- PO()$dldm(y,mu) + dPO(0,mu)*PO()$dldm(0,mu)/(1-dPO(0,mu))
dldm <- ifelse(y==0, 0 , dldm0)
dldm},
d2ldm2 = function(y,mu,sigma) {dldm0 <- PO()$dldm(y,mu) + dPO(0,mu)*PO()$dldm(0,mu)/(1-dPO(0,mu))
dldm <- ifelse(y==0, 0 , dldm0)
d2ldm2 <- -dldm*dldm
d2ldm2},
dldd = function(y,mu,sigma) {dldd <- ifelse(y==0, 1/sigma, -1/(1-sigma))
dldd},
d2ldd2 = function(y,mu,sigma) {d2ldd2 <- -1/(sigma*(1-sigma))
d2ldd2},
d2ldmdd = function(y,mu,sigma) {d2ldmdd <- 0
d2ldmdd
},
G.dev.incr = function(y,mu,sigma,...) -2*dZAP(y,mu,sigma,log=TRUE),
rqres = expression(rqres(pfun="pZAP", type="Discrete", ymin=0, y=y, mu=mu, sigma=sigma)) ,
mu.initial = expression(mu <- (y+mean(y))/2),
sigma.initial = expression(sigma <-rep(0.3, length(y))),
mu.valid = function(mu) all(mu > 0) ,
sigma.valid = function(sigma) all(sigma > 0 & sigma < 1),
y.valid = function(y) all(y >= 0),
mean = function(mu, sigma) {
c <- (1 - sigma) / (1- exp(-mu))
return( c * mu )
},
variance = function(mu, sigma, nu) {
c <- (1 - sigma) / (1- exp(-mu))
return(c * mu + c * mu^2 - c^2 * mu^2 )
}
),
class = c("gamlss.family","family"))
}
dZAP<-function(x, mu = 5, sigma = 0.1, log = FALSE)
{
if (any(mu <= 0) ) stop(paste("mu must be greater than 0", "\n", ""))
if (any(sigma <= 0) | any(sigma >= 1) ) stop(paste("sigma must be between 0 and 1", "\n", ""))
if (any(x < 0) ) stop(paste("x must be 0 or greater than 0", "\n", ""))
ly <- max(length(x),length(mu),length(sigma))
x <- rep(x, length = ly)
sigma <- rep(sigma, length = ly)
mu <- rep(mu, length = ly)
logfy <- rep(0, ly)
logfy <- ifelse((x==0), log(sigma), log(1-sigma) + dPO(x,mu,log=T) - log(1-dPO(0,mu)) )
if(log == FALSE) fy <- exp(logfy) else fy <- logfy
fy
}
pZAP <- function(q, mu = 5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
{
if (any(mu <= 0) ) stop(paste("mu must be greater than 0", "\n", ""))
if (any(sigma <= 0) | any(sigma >= 1) ) stop(paste("sigma must be between 0 and 1", "\n", ""))
if (any(q < 0) ) stop(paste("y must be 0 or greater than 0", "\n", ""))
ly <- max(length(q),length(mu),length(sigma))
q <- rep(q, length = ly)
sigma <- rep(sigma, length = ly)
mu <- rep(mu, length = ly)
cdf <- rep(0,ly)
cdf1 <- ppois(q, lambda = mu, lower.tail = TRUE, log.p = FALSE)
cdf2 <- ppois(0, lambda = mu, lower.tail = TRUE, log.p = FALSE)
cdf3 <- sigma+((1-sigma)*(cdf1-cdf2)/(1-cdf2))
cdf <- ifelse((q==0),sigma, cdf3)
if(lower.tail == TRUE) cdf <- cdf else cdf <-1-cdf
if(log.p==FALSE) cdf <- cdf else cdf <- log(cdf)
cdf
}
qZAP <- function(p, mu = 5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
{
if (any(mu <= 0) ) stop(paste("mu must be greater than 0", "\n", ""))
if (any(sigma <= 0) ) stop(paste("sigma must be greater than 0", "\n", ""))
if (any(p <= 0) | any(p >= 1)) stop(paste("p must be between 0 and 1", "\n", ""))
if (log.p == TRUE) p <- exp(p) else p <- p
if (lower.tail == TRUE) p <- p else p <- 1 - p
ly <- max(length(p),length(mu),length(sigma))
p <- rep(p, length = ly)
sigma <- rep(sigma, length = ly)
mu <- rep(mu, length = ly)
pnew <- (p-sigma)/(1-sigma)
pnew2 <- ppois(0, lambda = mu, lower.tail = TRUE, log.p = FALSE)*(1-pnew) + pnew
suppressWarnings(q <- ifelse((pnew > 0 ), qpois(pnew2, lambda = mu, ), 0))
q
}
rZAP <- function(n, mu=5, sigma=0.1)
{
if (any(mu <= 0) ) stop(paste("mu must greated than 0", "\n", ""))
if (any(sigma <= 0) ) stop(paste("sigma must greated than 0", "\n", ""))
if (any(n <= 0)) stop(paste("n must be a positive integer", "\n", ""))
n <- ceiling(n)
p <- runif(n)
r <- qZAP(p, mu = mu, sigma = sigma)
as.integer(r)
}
|
semLme <- function(formula, data, classes, burnin = 40, samples = 200, trafo = "None",
adjust = 2, bootstrap.se = FALSE, b = 100) {
call <- match.call()
o.classes <- classes
o.data <- data
o.formula <- formula
lambda <- result_lambda <- b.lambda <- m.lambda <- se <- ci <- NULL
if (trafo == "log") {
classes <- log.est(y = classes)
}
if (trafo == "bc") {
suppressWarnings(lambda.est <-
lambda.lme.est(
formula = formula,
data = data,
classes = classes,
burnin = burnin,
samples = samples,
adjust = adjust
))
lambda <- lambda.est$lambda
result_lambda <- lambda.est$it.lambda
b.lambda <- lambda.est$b.lambda
m.lambda <- lambda.est$m.lambda
BoxCoxClasses <- boxcox.lme.est(dat = classes, lambda = lambda, inverse = FALSE)
classes <- BoxCoxClasses[[1]]
}
data <- midpoints.est(formula = formula, data = data, classes = classes)
formula <- as.formula(gsub(".*~", "pseudoy~", formula))
regclass <- lmer(formula, data = data)
resulty <- matrix(ncol = c(burnin + samples), nrow = nrow(data))
resultcoef <- matrix(ncol = c(burnin + samples), nrow = length(regclass@beta))
result_ranef <- vector("list", burnin + samples)
result_sigmae <- vector(mode = "numeric", length = burnin + samples)
result_r2c <- vector(mode = "numeric", length = burnin + samples)
result_r2m <- vector(mode = "numeric", length = burnin + samples)
result_icc <- vector(mode = "numeric", length = burnin + samples)
VaCovMa <- vector("list", burnin + samples)
for (j in 1:(burnin + samples)) {
data$predict <- predict(regclass, data)
sigmahat <- sigma(regclass)
for (i in 1:(length(classes) - 1)) {
if (nrow(data[data$yclassl == i, ]) != 0) {
mean <- data$predict[data$yclassl == i]
pseudoy <- rtruncnorm(length(mean), a = classes[i], b = classes[i + 1], mean = mean, sd = sigmahat)
data$pseudoy[data$yclassl == i] <- pseudoy
}
}
regclass <- lmer(formula, data = data)
resultcoef[, j] <- regclass@beta
result_ranef[[j]] <- as.matrix(ranef(regclass)[[1]])
result_sigmae[j] <- sigmahat
r_squared <- r.squaredGLMM(regclass)
if (is.matrix(r_squared)) {
result_r2m[j] <- unname(r_squared[1, 1])
result_r2c[j] <- unname(r_squared[1, 2])
} else {
result_r2m[j] <- unname(r_squared[1])
result_r2c[j] <- unname(r_squared[2])
}
result_icc[j] <- icc.est(model = regclass)
resulty[, j] <- data$pseudoy
VaCovMa[[j]] <- as.matrix(unclass(VarCorr(regclass))[[1]][1:ncol(ranef(regclass)[[1]]), ])
}
parameter.ma <- list(ranef = result_ranef, VaCov = VaCovMa)
parameter.final.ma <- parameters.est.ma(parameter = parameter.ma, burnin = burnin)
colnames(parameter.final.ma$VaCov) <- colnames(VaCovMa[[1]])
rownames(parameter.final.ma$VaCov) <- rownames(VaCovMa[[1]])
colnames(parameter.final.ma$ranef) <- colnames(result_ranef[[1]])
parameter <- list(
coef = resultcoef,
sigmae = result_sigmae,
r2m = result_r2m, r2c = result_r2c,
icc = result_icc
)
parameter.final <- parameters.est(parameter = parameter, burnin = burnin)
names(parameter.final$coef) <- rownames(summary(regclass)$coefficients)
if (bootstrap.se == TRUE) {
result_se <- standardErrorLME.est(
formula = o.formula,
data = o.data,
classes = o.classes,
burnin = burnin,
samples = samples,
trafo = trafo,
adjust = adjust,
b = b,
coef = parameter.final$coef,
sigmae = parameter.final$sigmae,
VaCov = parameter.final.ma$VaCov,
nameRI = names(ranef(regclass)),
nameRS = names(ranef(regclass)[[1]])[2],
regmodell = regclass,
lambda = m.lambda
)
se <- result_se$se
ci <- result_se$ci
rownames(ci) <- names(parameter.final$coef)
}
est <- list(
pseudo.y = resulty, coef = parameter.final$coef, ranef = parameter.final.ma$ranef,
sigmae = parameter.final$sigmae, VaCov = parameter.final.ma$VaCov,
se = se, ci = ci, lambda = lambda, bootstraps = b, r2m = parameter.final$r2m,
r2c = parameter.final$r2c, icc = parameter.final$icc, formula = o.formula,
transformation = trafo, n.classes = length(classes) - 1, conv.coef = resultcoef,
conv.sigmae = result_sigmae, conv.lambda = result_lambda, conv.VaCov = VaCovMa,
b.lambda = b.lambda, m.lambda = m.lambda, burnin = burnin, samples = samples,
classes = o.classes, original.y = data$y, call = call
)
class(est) <- c("sem", "lme")
return(est)
}
|
mc_link_function <- function(beta, X, offset, link) {
assert_that(noNA(beta))
assert_that(noNA(X))
if (!is.null(offset))
assert_that(noNA(offset))
link_name <- c("logit", "probit", "cauchit", "cloglog", "loglog",
"identity", "log", "sqrt", "1/mu^2", "inverse")
link_func <- c("mc_logit", "mc_probit", "mc_cauchit", "mc_cloglog",
"mc_loglog", "mc_identity", "mc_log", "mc_sqrt",
"mc_invmu2", "mc_inverse")
names(link_func) <- link_name
if (!link %in% link_name) {
if (!exists(link, envir = -1, mode = "function")) {
stop(gettextf(paste0(
"%s link function not recognised or found. ",
"Available links are: ",
paste(link_name, collapse = ", "),
"."),
sQuote(link)), domain = NA)
} else {
match_args <- sort(names(formals(link))) %in%
sort(c("beta", "X", "offset"))
if (length(match_args) != 3L || !all(match_args)) {
stop(gettextf(paste(
"Provided link function must have %s, %s and %s",
"as arguments to be valid."),
sQuote("beta"), sQuote("X"), sQuote("offset")),
domain = NA)
}
}
output <- do.call(link,
args = list(beta = beta, X = X,
offset = offset))
if (!is.list(output)) {
stop("Provided link funtion doesn't return a list.")
}
if (!identical(sort(names(output)), c("D","mu"))) {
stop(paste0("Provided link funtion isn't return ",
"a list with names ", sQuote("mu"),
" and ", sQuote("D"), "."))
}
if (!(identical(dim(output$D), dim(X)) &&
is.matrix(output$D))) {
stop(paste0("Returned ", sQuote("D"),
" object by user defined link function ",
"isn't a matrix of correct dimensions."))
}
print(is.vector(output$mu, mode = "vector"))
print(class(output$mu))
if (!(length(output$mu) == nrow(X) &&
is.vector(output$mu, mode = "numeric"))) {
stop(paste0("Returned ", sQuote("mu"),
" object by user defined link function ",
"isn't a vector of correct length."))
is.vector(output$mu, mode = "vector")
}
} else {
link <- link_func[link]
output <- do.call(link,
args = list(beta = beta, X = X,
offset = offset))
}
return(output)
}
mc_logit <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu <- make.link("logit")$linkinv(eta = eta)
return(list(mu = mu, D = X * (mu * (1 - mu))))
}
mc_probit <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu <- make.link("probit")$linkinv(eta = eta)
Deri <- make.link("probit")$mu.eta(eta = eta)
return(list(mu = mu, D = X * Deri))
}
mc_cauchit <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu = make.link("cauchit")$linkinv(eta = eta)
Deri <- make.link("cauchit")$mu.eta(eta = eta)
return(list(mu = mu, D = X * Deri))
}
mc_cloglog <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu = make.link("cloglog")$linkinv(eta = eta)
Deri <- make.link("cloglog")$mu.eta(eta = eta)
return(list(mu = mu, D = X * Deri))
}
mc_loglog <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu <- exp(-exp(-eta))
Deri <- exp(-exp(-eta) - eta)
return(list(mu = mu, D = X * Deri))
}
mc_identity <- function(beta, X, offset) {
eta <- X %*% beta
if (!is.null(offset)) {
eta <- eta + offset
}
return(list(mu = as.numeric(eta), D = X))
}
mc_log <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu = make.link("log")$linkinv(eta = eta)
return(list(mu = mu, D = X * mu))
}
mc_sqrt <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu = make.link("sqrt")$linkinv(eta = eta)
return(list(mu = mu, D = X * (2 * as.numeric(eta))))
}
mc_invmu2 <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu <- make.link("1/mu^2")$linkinv(eta = eta)
Deri <- make.link("1/mu^2")$mu.eta(eta = eta)
return(list(mu = mu, D = X * Deri))
}
mc_inverse <- function(beta, X, offset) {
eta <- as.numeric(X %*% beta)
if (!is.null(offset)) {
eta <- eta + offset
}
mu <- make.link("inverse")$linkinv(eta = eta)
Deri <- make.link("inverse")$mu.eta(eta = eta)
return(list(mu = mu, D = X * Deri))
}
|
ps.model <- function(dat, form.ps) {
fm <- glm( formula = as.formula( form.ps ), data = dat, family = binomial(link = "logit"));
ps.hat <- as.numeric(predict(fm, newdata = dat, type = "response"));
ps.hat <- pmin(pmax(0.000001, ps.hat), 0.999999);
return( list( ps.hat = ps.hat, fm = fm ) );
}
psw.balance.core <- function( Xmat, Xname, weight, W, Z ) {
out1 <- NULL ;
for ( j in 1 : length( Xname ) ) {
this.name <- Xname[j];
this.var <- as.numeric( Xmat[ , this.name ] );
tmp <- calc.std.diff( var.value = this.var, wt = rep(1, length = nrow( Xmat ) ), Z = Z );
out1 <- rbind(out1, tmp );
}
rownames(out1) <- Xname;
colnames(out1) <- c("treated.mean","treated.sd","control.mean","control.sd","std.diff.pct");
std.diff.before <- out1;
out1 <- NULL;
for ( j in 1 : length( Xname ) ) {
this.name <- Xname[j];
this.var <- as.numeric( Xmat[ , this.name ] );
tmp <- calc.std.diff( var.value = this.var, wt = W, Z = Z);
out1 <- rbind(out1, tmp);
}
rownames(out1) <- Xname;
colnames(out1) <- c("treated.mean","treated.sd","control.mean","control.sd","std.diff.pct");
std.diff.after <- out1;
diff.plot( diff.before = std.diff.before[ , "std.diff.pct" ], diff.after = std.diff.after[ , "std.diff.pct" ], name = Xname, weight = weight );
return( list( std.diff.before = std.diff.before,
std.diff.after = std.diff.after ) );
}
psw.wt.core <- function( dat, beta.hat, omega, Q, trt.var, out.var, family, Xname, weight, delta=0.002, K=4 ) {
n <- nrow(dat);
mu1.hat <- sum( ( omega / Q ) * dat[ , trt.var ] * dat[ , out.var ] ) / sum( ( omega / Q ) * dat[ , trt.var ] );
mu0.hat <- sum( ( omega / Q ) * ( 1 - dat[ , trt.var ]) * dat[ , out.var ] ) / sum( ( omega / Q ) * ( 1 - dat[ , trt.var ] ) );
if ( family == "gaussian" ) {
est <- mu1.hat - mu0.hat;
}
if ( family == "binomial" ) {
est.risk <- mu1.hat - mu0.hat;
est.rr <- mu1.hat / mu0.hat;
est.or <- ( mu1.hat / ( 1 - mu1.hat ) ) / ( mu0.hat / ( 1 - mu0.hat ) );
est.lor <- log( est.or );
}
Amat <- Bmat <- 0;
for (i in 1 : n) {
Xi <- as.numeric( c( 1, dat[ i, Xname ] ) );
Zi <- dat[ i, trt.var ];
Yi <- dat[ i, out.var ];
ei <- calc.ps.Xbeta( Xmat = Xi, beta = beta.hat );
ei.deriv1 <- calc.ps.deriv1( Xmat = Xi, beta = beta.hat );
ei.deriv2 <- calc.ps.deriv2( Xi = Xi, beta = beta.hat );
omega.ei <- omega[ i ];
omegaei.deriv <- omega.derive.ei( ps = ei, weight = weight, delta = delta, K=K );
Qi <- Q[i];
Qi.deriv <- 2*Zi - 1;
phi <- c( Zi * ( Yi - mu1.hat ) * omega.ei / Qi,
( 1 - Zi ) * ( Yi - mu0.hat ) * omega.ei / Qi,
( Zi -ei ) / ( ei * ( 1 - ei ) ) * ei.deriv1 );
Bmat <- Bmat + outer( phi, phi );
first.row <- c( - Zi * omega.ei / Qi, 0, Zi * ( Yi - mu1.hat ) * ei.deriv1 * ( Qi * omegaei.deriv - omega.ei * Qi.deriv ) / Qi^2 );
second.row <- c( 0, - ( 1 - Zi ) * omega.ei / Qi, ( 1 - Zi ) * ( Yi - mu0.hat ) * ei.deriv1 * ( Qi * omegaei.deriv - omega.ei * Qi.deriv ) / Qi^2 );
tmp0 <- matrix( 0, nrow = length( beta.hat ), ncol = 2 );
tmp1 <- - ei * ( 1 - ei ) * colVec( Xi ) %*% Xi;
third.row <- cbind( tmp0, tmp1 );
phi.deriv <- rbind( first.row, second.row, third.row );
Amat <- Amat + phi.deriv;
}
Amat <- Amat/n;
Bmat <- Bmat/n;
Amat.inv <- solve( Amat );
var.mat <- ( Amat.inv %*% Bmat %*% t( Amat.inv ) ) / n;
var.mat <- var.mat[ c( 1 : 2 ), c( 1 : 2 ) ];
if ( family == "gaussian" ) {
tmp1 <- c( 1, -1 );
var.est <- rowVec( tmp1 ) %*% var.mat %*% colVec( tmp1 );
std <- sqrt( as.numeric( var.est ) );
ans <- list( est = est, std = std );
}
if ( family == "binomial" ) {
tmp.risk <- c( 1, -1 );
var.risk <- rowVec( tmp.risk ) %*% var.mat %*% colVec( tmp.risk );
std.risk <- sqrt( as.numeric( var.risk ) );
tmp.rr <- c( 1 / mu1.hat, - mu1.hat / ( mu0.hat^2 ) );
var.rr <- rowVec( tmp.rr ) %*% var.mat %*% colVec( tmp.rr );
std.rr <- sqrt( as.numeric( var.rr ) );
tmp1 <- ( 1 - mu0.hat ) / ( mu0.hat * ( 1 - mu1.hat )^2 );
tmp0 <- - mu1.hat / ( ( 1 - mu1.hat ) * mu0.hat^2 );
tmp.or <- c( tmp1, tmp0 );
var.or <- rowVec( tmp.or ) %*% var.mat %*% colVec( tmp.or );
std.or <- sqrt( as.numeric( var.or ) );
tmp1 <- 1/( mu1.hat * ( 1 - mu1.hat ) );
tmp0 <- - 1/( mu0.hat * ( 1 - mu0.hat ) );
tmp.lor <- c( tmp1, tmp0 );
var.lor <- rowVec( tmp.lor ) %*% var.mat %*% colVec( tmp.lor );
std.lor <- sqrt( as.numeric( var.lor ) );
ans <- list( est.risk = est.risk, std.risk = std.risk,
est.rr = est.rr, std.rr = std.rr,
est.or = est.or, std.or = std.or,
est.lor = est.lor, std.lor = std.lor );
}
return( ans );
}
psw.aug.core <- function(dat, beta.hat, omega, Q, out.ps, out.outcome, trt.var, out.var, weight, family, K, delta=0.002){
n <- nrow( dat );
W <- omega/Q;
tmp1 <- W*dat[ , trt.var ];
mu1.hat <- sum( tmp1*(dat[ , out.var ] - out.outcome$Y.hat.m1) )/sum( tmp1 );
mu2.hat <- sum( omega*out.outcome$Y.hat.m1 )/sum( omega );
tmp0 <- W*( 1 - dat[ , trt.var ] );
mu3.hat <- sum( tmp0*(dat[ , out.var ] - out.outcome$Y.hat.m0) )/sum( tmp0 );
mu4.hat <- sum( omega*out.outcome$Y.hat.m0 )/sum( omega );
if ( family == "gaussian" ) {
est <- mu1.hat + mu2.hat - mu3.hat - mu4.hat;
}
if ( family == "binomial" ) {
p1.hat <- mu1.hat + mu2.hat;
p0.hat <- mu3.hat + mu4.hat;
est.risk <- p1.hat - p0.hat;
est.rr <- p1.hat/p0.hat;
est.or <- (p1.hat/(1-p1.hat)) / (p0.hat/(1-p0.hat));
est.lor <- log( est.or );
}
alpha1.hat <- as.numeric( coef(out.outcome$fm1) );
alpha0.hat <- as.numeric( coef(out.outcome$fm0) );
beta.hat <- as.numeric( coef(out.ps$fm) );
n.alpha1 <- length( alpha1.hat );
n.alpha0 <- length( alpha0.hat );
n.beta <- length( beta.hat );
n <- nrow(dat);
Amat <- Bmat <- 0;
for (i in 1:n) {
Xi <- as.numeric( c(1, dat[i, names(coef(out.ps$fm))[-1] ] ) );
Vi <- as.numeric( c(1, dat[i, names(coef(out.outcome$fm1))[-1] ] ) );
Zi <- dat[ i, trt.var ] ;
Yi <- dat[ i, out.var ] ;
Wi <- W[i];
Qi <- Q[i];
omegai <- omega[i];
ei <- calc.ps.Xbeta(Xmat=Xi, beta=beta.hat);
ei.deriv1 <- calc.ps.deriv1(Xmat=Xi, beta=beta.hat);
ei.deriv2 <- calc.ps.deriv2( Xi=Xi, beta=beta.hat);
Wi.deriv.beta <- calc.W.derive.beta( Zi=Zi, Xi=Xi, omega.ei=omegai, beta.hat=beta.hat, ei=ei, Qi=Qi, weight=weight, delta=delta, K=K );
omegai.deriv.beta <- calc.omega.derive.beta( Xi=Xi, beta.hat=beta.hat, ei=ei, weight=weight, delta=delta, K=K );
if ( family == "gaussian" ) {
m1.hat <- sum(Vi*alpha1.hat);
m0.hat <- sum(Vi*alpha0.hat);
m1.deriv.alpha1 <- Vi;
m0.deriv.alpha0 <- Vi;
s1.deriv.alpha1 <- -outer(Vi, Vi);
s0.deriv.alpha0 <- -outer(Vi, Vi);
}
if ( family == "binomial" ) {
tmp1 <- sum(Vi*alpha1.hat);
tmp0 <- sum(Vi*alpha0.hat);
m1.hat <- exp(tmp1)/(1+exp(tmp1));
m0.hat <- exp(tmp0)/(1+exp(tmp0));
m1.deriv.alpha1 <- m1.hat*(1-m1.hat)*Vi;
m0.deriv.alpha0 <- m0.hat*(1-m0.hat)*Vi;
s1.deriv.alpha1 <- -m1.hat*(1-m1.hat)*outer(Vi, Vi);
s0.deriv.alpha0 <- -m0.hat*(1-m0.hat)*outer(Vi, Vi);
}
this.phi.row1 <- Wi*Zi*( Yi - m1.hat - mu1.hat );
this.phi.row2 <- omegai*( m1.hat - mu2.hat );
this.phi.row3 <- Wi*(1-Zi)*( Yi - m0.hat - mu3.hat );
this.phi.row4 <- omegai*( m0.hat - mu4.hat );
this.phi.row5 <- Zi*( Yi - m1.hat )*Vi;
this.phi.row6 <- (1-Zi)*( Yi - m0.hat )*Vi;
this.phi.row7 <- (Zi-ei)/ei/(1-ei)*ei.deriv1;
this.phi <- c( this.phi.row1, this.phi.row2, this.phi.row3,
this.phi.row4, this.phi.row5, this.phi.row6, this.phi.row7 );
Bmat <- Bmat + outer( this.phi, this.phi );
quad1 <- diag( c( -Wi*Zi, -omegai, -Wi*(1-Zi), -omegai ) );
quad2 <- matrix(0, nrow=n.alpha1+n.alpha0+n.beta, ncol=4);
tmp <- c( -Wi*Zi*m1.deriv.alpha1, rep(0, n.alpha0), Zi*( Yi - m1.hat - mu1.hat )*Wi.deriv.beta,
omegai*m1.deriv.alpha1, rep(0, n.alpha0), ( m1.hat - mu2.hat )*omegai.deriv.beta,
rep(0, n.alpha1), -Wi*(1-Zi)*m0.deriv.alpha0, (1-Zi)*( Yi - m0.hat - mu3.hat )*Wi.deriv.beta,
rep(0, n.alpha1), omegai*m0.deriv.alpha0, ( m0.hat - mu4.hat )*omegai.deriv.beta );
quad3 <- matrix( tmp, byrow = TRUE, nrow = 4,
ncol = n.alpha1 + n.alpha0 + n.beta );
quad4.blk1 <- cbind( Zi*s1.deriv.alpha1, matrix(0, nrow=n.alpha1, ncol=n.alpha0 + n.beta) );
quad4.blk2 <- cbind( matrix(0, nrow=n.alpha0, ncol=n.alpha1 ), (1-Zi)*s0.deriv.alpha0, matrix(0, nrow=n.alpha0, ncol=n.beta) );
quad4.blk3 <- cbind( matrix(0, nrow=n.beta, ncol=n.alpha1+n.alpha0), -ei*(1-ei)*outer(Xi, Xi) );
quad4 <- rbind( quad4.blk1, quad4.blk2, quad4.blk3 );
this.phi.deriv <- rbind( cbind(quad1, quad3) ,
cbind(quad2, quad4) );
Amat <- Amat + this.phi.deriv;
}
Amat <- Amat/n;
Bmat <- Bmat/n;
Amat.inv <- solve(Amat) ;
var.mat <- ( Amat.inv %*% Bmat %*% t(Amat.inv) )/n;
var.mat <- var.mat[ c(1:4), c(1:4) ];
if ( family == "gaussian" ) {
tmp <- c( 1, 1, -1, -1 );
var.est <- rowVec(tmp) %*% var.mat %*% colVec(tmp);
std <- sqrt( as.numeric(var.est) );
ans <- list( est = est, std = std );
}
if ( family == "binomial" ) {
tmp.risk <- c( 1, 1, -1, -1 );
var.risk <- rowVec( tmp.risk ) %*% var.mat %*% colVec( tmp.risk );
std.risk <- sqrt( as.numeric( var.risk ) );
ans <- list( est.risk = est.risk, std.risk = std.risk );
}
return( ans );
}
psw.spec.test.core <- function( X.mat, V.mat, V.name, trt, beta.hat, omega, Q, trans.type, weight, K, delta=0.002 ) {
n <- nrow( X.mat );
W <- omega/Q;
mu.B1.hat <- as.numeric( t(V.mat) %*% colVec( W * trt ) ) / sum( W * trt ) ;
mu.B0.hat <- as.numeric( t(V.mat) %*% colVec( W * ( 1 - trt ) ) ) / sum( W * ( 1 - trt ) ) ;
eta.B1.hat <- g.fun( x=mu.B1.hat, fun.type = trans.type ) ;
eta.B0.hat <- g.fun( x=mu.B0.hat, fun.type = trans.type ) ;
theta.hat <- c( eta.B1.hat, eta.B0.hat, beta.hat ) ;
B.hat <- eta.B1.hat - eta.B0.hat ;
n.mu.B1 <- length( mu.B1.hat ) ;
n.mu.B0 <- length( mu.B0.hat ) ;
n.beta <- length( beta.hat ) ;
n.theta <- length(theta.hat) ;
D.mat <- cbind( diag(n.mu.B1), -diag(n.mu.B0),
matrix(0, nrow=n.mu.B1, ncol=n.beta) ) ;
Meat.mat <- Bread.mat <- 0 ;
for ( i in 1 : n ) {
Vi <- as.numeric( V.mat[i, ] );
Xi <- as.numeric( X.mat[i, ] );
Zi <- trt[i];
Wi <- W[i];
omegai <- omega[i];
Qi <- Q[i];
ei <- calc.ps.Xbeta(Xmat=Xi, beta=beta.hat);
ei.deriv <- calc.ps.deriv1( Xmat = Xi, beta = beta.hat );
omegai.deriv <- omega.derive.ei( ps = ei, weight = weight, delta = delta, K = K );
Qi.deriv <- 2 * Zi - 1;
Wi.deriv.beta <- ei.deriv * ( Qi*omegai.deriv - omegai*Qi.deriv ) / Qi^2;
tmp1 <- Wi * Zi * ( Vi - g.inv( x = eta.B1.hat, fun.type = trans.type ) );
tmp2 <- Wi * ( 1 - Zi ) * ( Vi - g.inv( x = eta.B0.hat, fun.type = trans.type ) );
tmp3 <- ( Zi - ei ) * Xi;
this.phi <- c( tmp1, tmp2, tmp3 );
Meat.mat <- Meat.mat + outer( this.phi, this.phi );
Block.11 <- - Wi * Zi *( diag( g.inv.deriv( x = eta.B1.hat, fun.type = trans.type ) ) );
Block.12 <- matrix( 0, nrow = n.mu.B1, ncol = n.mu.B0 );
Block.13 <- Zi * ( colVec( Vi - g.inv( x = eta.B1.hat, fun.type = trans.type ) ) %*% rowVec( Wi.deriv.beta ) );
Block.21 <- matrix( 0, nrow = n.mu.B0, ncol = n.mu.B1 ) ;
Block.22 <- - Wi * ( 1 - Zi ) * ( diag( g.inv.deriv( x = eta.B0.hat, fun.type = trans.type ) ) );
Block.23 <- (1-Zi)*( colVec( Vi - g.inv( x = eta.B0.hat, fun.type = trans.type ) ) %*% rowVec( Wi.deriv.beta ) );
Block.31 <- matrix( 0, nrow = n.beta, ncol = n.mu.B1 );
Block.32 <- matrix( 0, nrow = n.beta, ncol = n.mu.B0 );
Block.33 <- -ei * ( 1 - ei ) * outer( Xi, Xi ) ;
Bread.mat <- Bread.mat + rbind( cbind( Block.11, Block.12, Block.13 ) ,
cbind( Block.21, Block.22, Block.23 ) ,
cbind( Block.31, Block.32, Block.33 ) );
}
B.mat <- Meat.mat/n;
A.mat <- Bread.mat/n;
A.mat.inv <- solve( A.mat );
Sigma.theta.hat <- ( A.mat.inv %*% B.mat %*% t( A.mat.inv ) )/n;
Sigma.B.hat <- D.mat %*% Sigma.theta.hat %*% t( D.mat );
df <- qr(Sigma.B.hat)$rank;
test.stat <- as.numeric( t(B.hat) %*% solve(Sigma.B.hat) %*% B.hat );
pvalue <- pchisq( test.stat, df = df, lower.tail = FALSE );
names(eta.B1.hat) <- paste("eta.B1.", V.name, sep="");
names(eta.B0.hat) <- paste("eta.B0.", V.name, sep="");
names(B.hat) <- c( paste("B.hat.", V.name, sep="") )
rownames(Sigma.B.hat) <- colnames(Sigma.B.hat) <- names(B.hat);
return( list( weight = weight,
V.name = V.name,
g.B1.hat = eta.B1.hat,
g.B0.hat = eta.B0.hat,
B.hat = B.hat,
var.B.hat = Sigma.B.hat,
test.stat = test.stat,
df = df,
pvalue = pvalue
) );
}
mirror.hist.core <- function( ps.above, ps.below, wt.above, wt.below, add.weight,
label.above, label.below, nclass = 50 ) {
x0 <- ps.above ; wt0 <- wt.above ;
x1 <- ps.below ; wt1 <- wt.below ;
if ( is.null(nclass) ) {
breaks <- hist( c(x0, x1), nclass=nclass, plot=F )$breaks ;
} else {
breaks <- hist( c(x0, x1), nclass=nclass, plot=F )$breaks ;
}
fm.x0 <- hist( x0, breaks=breaks, plot = F ) ;
fm.x1 <- hist( x1, breaks=breaks, plot = F ) ;
x.range <- range( c(x0, x1) ) ;
y.range <- c( -max( fm.x1$counts ), max( fm.x0$counts ) ) ;
par( las=1, lwd = 2, mar=c(5, 5, 4, 2), bty="n" );
plot( x=x.range, y=y.range, xaxt="n", yaxt="n", type="n" ,
xlim=x.range, ylim=y.range, ylab="", xlab="", cex.lab=1 ) ;
axis( side=1, at=pretty( x.range ), cex.axis=1 ) ;
axis( side=2, at=pretty( y.range ) , labels=abs( pretty( y.range ) ), cex.axis=1 ) ;
title( xlab="Propensity score", cex.lab=1 )
title( ylab="Frequency", cex.lab=1 );
abline( h=0, lty=1 );
fm <- fm.x0 ;
for (i in 1:(length(breaks)-1)) {
x <- c( fm$breaks[i], fm$breaks[i], fm$breaks[i+1], fm$breaks[i+1] ) ;
y <- c(0, fm$counts[i], fm$counts[i], 0) ;
polygon(x, y) ;
if ( add.weight ) {
tmp <- ( breaks[i] <= x0 & x0 <= breaks[i+1] ) ;
y2 <- c(0, sum(wt0[tmp]), sum(wt0[tmp]), 0) ;
polygon(x, y2, col="light green", border="black") ;
}
}
fm <- fm.x1 ;
for (i in 1:(length(breaks)-1)) {
x <- c( fm$breaks[i], fm$breaks[i], fm$breaks[i+1], fm$breaks[i+1] ) ;
y <- c(0, -fm$counts[i], -fm$counts[i], 0) ;
polygon(x, y) ;
if ( add.weight ) {
tmp <- ( breaks[i] <= x1 & x1 <= breaks[i+1] ) ;
y2 <- c(0, -sum(wt1[tmp]), -sum(wt1[tmp]), 0) ;
polygon(x, y2, col="dark green", border="black") ;
}
}
}
rowVec <- function(x) {
return( t(x) );
}
colVec <- function(x) {
return( t(t(x)) );
}
outcome.model <- function(dat, form, trt.var, family) {
fm1 <- glm( as.formula( form ), data = dat[ dat[ , trt.var ] == 1, ] );
fm0 <- glm( as.formula( form ), data = dat[ dat[ , trt.var ] == 0, ] );
Y.hat.m1 <- as.numeric( predict( fm1, newdata = dat ) );
Y.hat.m0 <- as.numeric( predict( fm0, newdata = dat ) );
return( list( Y.hat.m1 = Y.hat.m1 ,
Y.hat.m0 = Y.hat.m0 ,
fm1 = fm1 , fm0 = fm0 ) );
}
calc.omega <- function(ps, weight, delta=0.002, K=4) {
ans <- 0;
if (weight == "ATE") {
ans <- 1;
} else if (weight == "MW") {
ans <- calc.omega.MW( ps = ps, delta = delta );
} else if (weight == "ATT") {
ans <- ps;
} else if (weight == "ATC") {
ans <- 1-ps;
} else if (weight == "OVERLAP") {
ans <- 4*ps*(1-ps);
} else if (weight == "TRAPEZOIDAL") {
ans <- calc.omega.trapzd(ps=ps, delta=delta, K=K);
} else {
stop("Error in calc.omega: weight method does not exist!");
}
return( ans );
}
calc.ps.Xbeta <- function( Xmat, beta ) {
Xmat <- as.matrix(Xmat);
tmp <- as.numeric(rowVec(beta) %*% Xmat);
tmp <- exp(tmp);
names(tmp) <- NULL;
return( tmp/(1 + tmp) );
}
calc.ps.deriv1 <- function(Xmat, beta) {
Xmat <- as.matrix(Xmat);
tmp.ps <- calc.ps.Xbeta( Xmat=Xmat, beta=beta );
ans <- tmp.ps*(1 - tmp.ps)*t(Xmat);
names(ans) <- rownames(ans) <- NULL;
return( t(ans) );
}
calc.ps.deriv2 <- function( Xi, beta ) {
Xi <- colVec(Xi);
tmp.ps <- calc.ps.Xbeta( Xmat=Xi, beta=beta );
tmp.deriv1 <- calc.ps.deriv1( Xmat=Xi, beta=beta );
ans <- Xi %*% rowVec(tmp.deriv1);
names(ans) <- rownames(ans) <- NULL;
ans <- (1 - 2*tmp.ps)*ans;
return( ans );
}
calc.omega.MW <- function (ps, delta) {
ans <- 0;
if (ps <= 0.5 - delta) {
ans <- 2*ps;
} else if (ps >= 0.5 + delta) {
ans <- 2*(1 - ps);
} else {
ans <- approx.omega.MW(ps, delta);
}
return( ans );
}
approx.omega.MW <- function (ps, delta) {
A <- solve.A.MW(delta);
ans <- rowVec(A) %*% c(1, ps, ps^2, ps^3);
ans;
}
solve.A.MW <- function(delta) {
if ( delta < 0.00001 ) {
stop("*** ERROR in solve.a: delta too small ***");
}
tmp1 <- 0.5 - delta;
tmp2 <- 0.5 + delta;
D <- matrix(c(1, tmp1, tmp1^2, tmp1^3,
0, 1, 2*tmp1, 3*tmp1^2,
1, tmp2, tmp2^2, tmp2^3,
0, 1, 2*tmp2, 3*tmp2^2),
ncol = 4, nrow = 4, byrow = TRUE);
C <- 2*c(tmp1, 1, tmp1, -1);
A <- solve(D) %*% C;
A;
}
calc.omega.trapzd <- function (ps, delta, K) {
ans <- 0;
if ( (0 < ps) & (ps <= 1/K - delta) ) {
ans <- K*ps;
} else if ( (1/K + delta <= ps) & (ps <= 1 - 1/K - delta) ) {
ans <- 1;
} else if ( (1 - 1/K + delta <= ps) & (ps < 1) ) {
ans <- K*(1 - ps);
} else {
ans <- approx.omega.trapzd(ps, delta, K);
}
ans;
}
approx.omega.trapzd <- function (ps, delta, K) {
A <- 0;
if ( (1/K - delta < ps) & (ps < 1/K + delta) ) {
A <- solve.A.trapzd1st(delta=delta, K=K);
} else {
A <- solve.A.trapzd2nd(delta=delta, K=K);
}
ans <- rowVec(A) %*% c(1, ps, ps^2, ps^3);
ans;
}
solve.A.trapzd1st <- function (delta, K) {
if ( delta < 0.00001 ) {
stop("*** ERROR in solve.a: delta too small ***");
}
tmp1 <- 1/K - delta;
tmp2 <- 1/K + delta;
D <- matrix(c(1, tmp1, tmp1^2, tmp1^3,
0, 1, 2*tmp1, 3*tmp1^2,
1, tmp2, tmp2^2, tmp2^3,
0, 1, 2*tmp2, 3*tmp2^2),
ncol = 4, nrow = 4, byrow = TRUE);
C <- 2*c(K*tmp1, K, 1, 0);
A <- solve(D) %*% C;
A;
}
solve.A.trapzd2nd <- function (delta, K) {
if ( delta < 0.00001 ) {
stop("*** ERROR in solve.a: delta too small ***");
}
tmp1 <- 1 - 1/K - delta;
tmp2 <- 1 - 1/K + delta;
D <- matrix(c(1, tmp1, tmp1^2, tmp1^3,
0, 1, 2*tmp1, 3*tmp1^2,
1, tmp2, tmp2^2, tmp2^3,
0, 1, 2*tmp2, 3*tmp2^2),
ncol = 4, nrow = 4, byrow = TRUE);
C <- 2*c(1, 0, K*(1/K - delta), -K);
A <- solve(D) %*% C;
A;
}
omega.derive.ei <- function(ps, weight, delta=0.002, K=4) {
if (weight == "ATE") {
ans <- 0;
} else if (weight == "ATT") {
ans <- 1;
} else if (weight == "ATC") {
ans <- -1;
} else if (weight == "OVERLAP") {
ans <- 4*(1 - 2*ps);
} else if (weight == "MW") {
ans <- omega.derive.ei.MW(ps, delta);
} else if (weight == "TRAPEZOIDAL") {
ans <- omega.derive.ei.trapzd(ps, delta, K);
} else {
stop( "User defined first-order derivative of omega function is not provided!" );
}
ans;
}
omega.derive.ei.MW <- function (ps, delta) {
if ( (0 < ps) & (ps <= 0.5 - delta) ) {
ans <- 2;
} else if ( (0.5 + delta <= ps) & (ps <1) ) {
ans <- -2;
} else {
A <- solve.A.MW(delta);
ans <- A[2] + 2*A[3]*ps + 3*A[4]*ps^2;
}
ans;
}
omega.derive.ei.trapzd <- function (ps, delta, K) {
if ( (0 < ps) & (ps <= 1/K - delta) ) {
ans <- K;
} else if ( (1/K - delta < ps) & (ps < 1/K + delta) ) {
A <- solve.A.trapzd1st(delta = delta, K = K);
ans <- A[2] + 2*A[3]*ps + 3*A[4]*ps^2;
} else if ( (1/K + delta <= ps) & (ps <= 1 - 1/K - delta) ) {
ans <- 0;
} else if ( (1 - 1/K + delta <= ps) & (ps < 1) ) {
ans <- -K;
} else {
A <- solve.A.trapzd2nd(delta = delta, K = K);
ans <- A[2] + 2*A[3]*ps + 3*A[4]*ps^2;
}
ans;
}
calc.W.derive.beta <- function(Zi, Xi, omega.ei, beta.hat, ei, Qi, weight, delta, K ) {
ei.deriv1 <- calc.ps.deriv1( Xmat=Xi, beta=beta.hat );
omegaei.deriv <- omega.derive.ei( ps = ei, weight = weight, delta = delta, K = K );
Qi.deriv.ei <- 2*Zi - 1;
ans <- ei.deriv1*(Qi*omegaei.deriv - omega.ei*Qi.deriv.ei)/Qi^2;
return( ans );
}
calc.omega.derive.beta <- function(Xi, beta.hat, ei, weight, delta=0.002, K=4 ) {
ei.deriv1 <- calc.ps.deriv1( Xmat=Xi, beta=beta.hat );
omegaei.deriv <- omega.derive.ei( ps=ei, weight=weight, delta=delta, K=K );
ans <- omegaei.deriv * ei.deriv1;
return( ans );
}
calc.std.diff <- function(var.value, wt, Z) {
Z1.mean <- wtd.mean( x = var.value[Z==1], weights = wt[Z==1] );
Z1.var <- wtd.var( x = var.value[Z==1], weights = wt[Z==1] );
Z0.mean <- wtd.mean( x = var.value[Z==0], weights = wt[Z==0] );
Z0.var <- wtd.var( x = var.value[Z==0], weights = wt[Z==0] );
std.diff <- 100 * ( Z1.mean - Z0.mean ) / sqrt( ( Z1.var + Z0.var ) / 2 ) ;
ans <- c( Z1.mean, sqrt(Z1.var), Z0.mean, sqrt(Z0.var), std.diff );
return( ans );
}
diff.plot <- function( diff.before, diff.after, name, weight ) {
par( las=1, lwd = 2, mar=c(5, max( nchar(name) ) + 4, 4, 2), bty="n" );
x.range <- range( c(diff.before, diff.after) );
y.range <- c(1, length(name));
ord <- order( diff.before, decreasing = T );
plot( x=x.range, y=y.range, xaxt="n", yaxt="n", type="n",
xlim=x.range, ylim=y.range, ylab="", xlab="Standardized mean difference" );
axis( side=1, at=pretty( x.range ) );
axis( side=2, at=length(name):1, labels=name[ord], tick=F );
abline( v=0, lty=1, col="gray" );
points( y = length(name):1, x = diff.before[ord], pch=4 );
points( y = length(name):1, x = diff.after[ord], pch=21 );
legend("topleft", legend=c("Unadjusted", weight), pch=c(4, 21) );
}
g.fun <- function( x, fun.type ) {
if ( length(x) != length(fun.type) ) {
print("*** ERROR in g.fun() ***");
return( rep(NA, length(x)) );
} else {
m <- length(x);
ans <- rep(0, m);
for (j in 1:m) {
if ( fun.type[j] == "log" ) {
ans[j] <- log( max( 1e-6, x[j] ) );
} else if ( fun.type[j] == "logit" ) {
ans[j] <- logit( min( max( 1e-6, x[j]), 1-1e-6 ) );
} else if ( fun.type[j] == "Fisher" ) {
ans[j] <- 0.5*( log( 1 + x[j] ) - log( 1 - x[j] ) );
} else if (fun.type[j] == "sqrt") {
ans[j] <- sqrt( x[j] );
} else {
ans[j] <- x[j] ;
}
}
return( ans ) ;
}
}
g.inv <- function( x, fun.type ) {
if ( length(x) != length(fun.type) ) {
print("*** ERROR in g.inv() ***") ;
return( rep(NA, length(x)) ) ;
} else {
m <- length(x) ;
ans <- rep(0, m) ;
for (j in 1:m) {
if ( fun.type[j] == "log" ) {
ans[j] <- exp( x[j] ) ;
} else if ( fun.type[j] == "logit" ) {
ans[j] <- inv.logit( x[j] ) ;
} else if ( fun.type[j] == "Fisher" ) {
ans[j] <- ( (exp(2*x[j])-1)/(exp(2*x)+1) );
} else if ( fun.type[j] == "sqrt") {
ans[j] <- x[j]^2;
} else {
ans[j] <- x[j] ;
}
}
return( ans ) ;
}
}
g.inv.deriv <- function( x, fun.type ) {
if ( length(x) != length(fun.type) ) {
print("*** ERROR in g.inv.deriv() ***") ;
return( rep(NA, length(x)) ) ;
} else {
m <- length(x) ;
ans <- rep(0, m) ;
for (j in 1:m) {
if ( fun.type[j] == "log" ) {
ans[j] <- exp( x[j] ) ;
} else if ( fun.type[j] == "logit" ) {
tmp <- inv.logit( x[j] ) ;
ans[j] <- tmp*(1-tmp) ;
} else if ( fun.type[j] == "Fisher" ) {
tmp <- 2*exp( 2*x[j] );
ans[j] <- tmp^2;
} else if ( fun.type[j] == "sqrt" ) {
ans[j] <- 2*x[j];
} else {
ans[j] <- 1 ;
}
}
return( ans ) ;
}
}
|
bootCRCumInc <- function (df, exit, event, exposure, entry = NULL, weights = NULL, ipwvars=NULL, rep = 0, print.attr=T, seed=54321)
{
df$exit <- df[[deparse(substitute(exit))]]
df$event <- df[[deparse(substitute(event))]]
if(is.factor(df$event)) df$event <- as.numeric(df$event)-1
df$exposure <- df[[deparse(substitute(exposure))]]
if(is.factor(df$exposure)) df$exposure <- as.numeric(df$exposure)-1
df$entry <- df[[deparse(substitute(entry))]]
df$weights <- df[[deparse(substitute(weights))]]
set.seed(seed)
e3 <- any(df$event==3)
e4 <- any(df$event==4)
if (rep == 0) {
stop("\n", "`n` boostrapping repetitions missing", call. = FALSE)
}
else {
nboot <- rep
bI1o.all <- NULL
bI1x.all <- NULL
bI2o.all <- NULL
bI2x.all <- NULL
R1.all <- NULL
R2.all <- NULL
bI3o.all <- NULL
bI3x.all <- NULL
R3.all <- NULL
bI4o.all <- NULL
bI4x.all <- NULL
R4.all <- NULL
ttx.all <- NULL
datx <- df[which(df$exposure == 1), ]
dato <- df[which(df$exposure == 0), ]
for (b in 1:nboot) {
b.data <- rbind(datx[sample(1:nrow(datx),replace=T),],dato[sample(1:nrow(dato),replace=T),])
b.data$exit <- jitter(b.data$exit)
dat_boot <- do.call(CRCumInc,list(b.data,substitute(exit), substitute(event), substitute(exposure), substitute(entry), substitute(weights), substitute(ipwvars), F))
ttx.all[[b]] <- dat_boot$time
R1.all[[b]] <- dat_boot$R1
R2.all[[b]] <- dat_boot$R2
if(e3) R3.all[[b]] <- dat_boot$R3
if(e4) R4.all[[b]] <- dat_boot$R4
rm(dat_boot)
}
ttx <- sort(c(0, df$exit[df$event > 0]))
R1.lower <- NULL
R1.upper <- NULL
R2.lower <- NULL
R2.upper <- NULL
if(e3){
R3.lower <- NULL
R3.upper <- NULL
}
if(e4){
R4.lower <- NULL
R4.upper <- NULL
}
for (i in 1:length(ttx)) {
tty1 <- NULL
tty2 <- NULL
tty3 <- NULL
tty4 <- NULL
for (b in 1:nboot) {
tty1[b] <- approx(ttx.all[[b]], R1.all[[b]], xout = ttx[i], method = "constant", f = 0)$y
tty2[b] <- approx(ttx.all[[b]], R2.all[[b]], xout = ttx[i], method = "constant", f = 0)$y
if(e3) tty3[b] <- approx(ttx.all[[b]], R3.all[[b]], xout = ttx[i], method = "constant", f = 0)$y
if(e4) tty4[b] <- approx(ttx.all[[b]], R4.all[[b]], xout = ttx[i], method = "constant", f = 0)$y
}
ttq <- quantile(tty1, probs = c(0.025, 0.975), na.rm = T)
R1.lower[i] <- ttq[1]
R1.upper[i] <- ttq[2]
ttq <- quantile(tty2, probs = c(0.025, 0.975), na.rm = T)
R2.lower[i] <- ttq[1]
R2.upper[i] <- ttq[2]
if(e3){
ttq <- quantile(tty3, probs = c(0.025, 0.975), na.rm = T)
R3.lower[i] <- ttq[1]
R3.upper[i] <- ttq[2]
}
if(e4){
ttq <- quantile(tty4, probs = c(0.025, 0.975), na.rm = T)
R4.lower[i] <- ttq[1]
R4.upper[i] <- ttq[2]
}
}
CRCumInc_boot <- as.data.frame(cbind(R1.lower, R1.upper, R2.lower, R2.upper))
if(e3) CRCumInc_boot <- as.data.frame(cbind(CRCumInc_boot,R3.lower,R3.upper))
if(e4) CRCumInc_boot <- as.data.frame(cbind(CRCumInc_boot,R4.lower,R4.upper))
if(print.attr) print(attributes(CRCumInc_boot)[1:2])
invisible(CRCumInc_boot)
}
}
|
library(tidyverse)
library(data.table)
library(lubridate)
library(countrycode)
options(scipen=999)
inspect <- FALSE
df <- pred_frame <- data.frame(readRDS("output-data/country_daily_excess_deaths_with_covariates.RDS"))
df <- df[order(df$date), ]
pred_frame <- pred_frame[order(pred_frame$date), ]
dv <- "daily_excess_deaths_per_100k"
exclude <- c("daily_total_deaths",
"daily_total_deaths_per_100k",
"daily_expected_deaths",
"daily_expected_deaths_per_100k",
"daily_excess_deaths",
"daily_tests",
"daily_covid_cases",
"daily_covid_deaths",
"daily_vaccinations",
"iso3c",
"country",
"continent",
"imf_economy",
"wdi_life_expectancy_at_birth_dist_average",
"wdi_life_expectancy_at_birth_contiguous_country_average")
predictors <- setdiff(colnames(df), c(dv, exclude))
saveRDS(predictors, "output-data/model-objects/predictors.RDS")
source("scripts/shared-functions/expand_categorical.R")
temp <- expand_categorical(df, predictors)
df <- temp[[1]]
predictors <- temp[[2]]
X <- df
Y <- df[, dv]
if(inspect){
library(ggplot2)
pdat <- df
ggplot(pdat[pdat$date >= as.Date('2021-10-01'), ],
aes(x=as.Date(date, origin = '1970-01-01'), y=daily_excess_deaths_per_100k, col = iso3c))+
geom_line()+
theme(legend.pos = 'none')+
geom_vline(aes(xintercept = as.Date('2021-12-01')))+
geom_vline(aes(xintercept = as.Date('2021-12-31')))+
geom_line(data = pdat[pdat$date >= as.Date('2021-10-01') & pdat$iso3c == 'USA', ], size = 2)+
geom_vline(aes(xintercept = Sys.Date()-28), size = 2)
}
Y <- Y[!X$date > Sys.Date()-21]
X <- X[!X$date > Sys.Date()-21, ]
Y <- Y[!(X$iso3c == "IND_Mumbai_City" & X$date > 18744)]
X <- X[!(X$iso3c == "IND_Mumbai_City" & X$date > 18744),]
Y <- Y[!X$iso3c %in% c("PER", "ECU")]
X <- X[!X$iso3c %in% c("PER", "ECU"), ]
ids <- paste0(X$iso3c, "_", round(X$date/7, 0))
for(i in setdiff(colnames(X), c("iso3c", "region"))){
X[, i] <- ave(X[, i], ids, FUN = function(x){mean(x, na.rm = T)})
}
X <- X[!duplicated(ids), ]
Y <- Y[!duplicated(ids)]
export <- pred_frame[!duplicated(ids), c("iso3c", "country", "date", "region", "subregion", "population", "median_age", "aged_65_older", "life_expectancy", "daily_covid_deaths_per_100k", "daily_covid_cases_per_100k", "daily_tests_per_100k", "cumulative_daily_covid_cases_per_100k",
"cumulative_daily_covid_deaths_per_100k",
"cumulative_daily_tests_per_100k", "demography_adjusted_ifr",
"daily_covid_cases",
"daily_tests",
"daily_covid_deaths",
"daily_excess_deaths",
dv)]
source("scripts/shared-functions/impute_missing.R")
X <- impute_missing(X[, c(predictors, "iso3c")])
ncol(X)
for(i in colnames(X)){
if(is.numeric(X[, i])){
if(is.nan(sd(X[, i]))){
print(i)
X[,i] <- NULL
} else if(sd(X[, i]) == 0){
print(i)
X[,i] <- NULL
}
}
}
ncol(X)
NAm <- X[, grep("NA_matrix", colnames(X))]
library(DescTools)
NA_dimension_reduction <- FindCorr(cor(NAm), cutoff = 0.99)
for(i in grep("NA_matrix", colnames(X))){
X[X[, i] != 1, i] <- 0
}
source('scripts/aux_generate_model_loop.R')
main_estimate_models <- 10
saveRDS(main_estimate_models, "output-data/model-objects/main_estimate_models_n.RDS")
set.seed(112358)
generate_model_loop(
X_full = X[!is.na(Y), ],
Y_full = Y[!is.na(Y)],
B = 200,
include_main_estimate = T,
main_estimate_model_n = main_estimate_models,
main_estimate_learning_rate = 0.001,
bootstrap_learning_rate = 0.003)
calibration = F
if(calibration){
X_cv <- X[!is.na(Y), ]
Y_cv <- Y[!is.na(Y)]
weights <- log(X_cv$population)
iso3c <- X_cv$iso3c
cv_folds <- function(x, n = 10){
x <- sample(x)
split(x, cut(seq_along(x), n, labels = FALSE))}
folds <- cv_folds(unique(iso3c), n = 10)
saveRDS(folds, "output-data/model-objects/folds.RDS")
results <- data.frame(target = Y_cv,
preds = rep(NA, length(Y_cv)),
weights = weights,
iso3c = iso3c)
vars <- list()
for(i in 1:length(folds)){
train_x <- X_cv[!iso3c %in% folds[[i]], ]
train_y <- Y_cv[!iso3c %in% folds[[i]]]
train_w <- weights[!iso3c %in% folds[[i]]]
test_x <- X_cv[iso3c %in% folds[[i]], ]
test_y <- Y_cv[iso3c %in% folds[[i]]]
test_w <- weights[iso3c %in% folds[[i]]]
library(agtboost)
gbt_fit <- gbt.train(train_y,
as.matrix(train_x[, setdiff(colnames(X_cv), c("iso3c", "region"))]),
learning_rate = 0.01,
nrounds = 1500,
verbose = 10,
algorithm = "vanilla",
weights = train_w/mean(train_w))
print(i)
print("cross-validation round completed.")
results$preds[results$iso3c %in% folds[[i]]] <- predict(gbt_fit,
newdata = as.matrix(test_x[, setdiff(colnames(X_cv), c("iso3c", "region"))]))
}
mean((abs(results$target - results$preds)^2)*results$weights/mean(results$weights))
write_csv(results, "output-data/results_gradient_booster.csv")
pdat <- cbind.data.frame(pred = results$preds,
truth = results$target,
country = X_cv$iso3c,
region = X_cv$region,
w = results$weights)
write_csv(pdat, "output-data/calibration_plot_gradient_booster.csv")
ggplot(pdat,
aes(x=pred, y=truth,
col=region))+
geom_point()+
geom_abline(aes(slope = 1, intercept = 0))+
geom_smooth(aes(group = "1"), method = 'lm')+theme_minimal()+facet_wrap(.~region)
ggplot(pdat,
aes(x=pred, y=truth,
col=region))+
geom_point()+
geom_abline(aes(slope = 1, intercept = 0))+
geom_smooth(mapping = aes(weight = weights, group = "1"), method = 'lm')+theme_minimal()
}
|
mean_token_length <- function(topic_model, top_n_tokens = 10){
UseMethod("mean_token_length")
}
mean_token_length.TopicModel <- function(topic_model, top_n_tokens = 10){
top_terms <- terms(topic_model, top_n_tokens)
nchar_mat <- apply(top_terms, 2, nchar)
unname(colMeans(nchar_mat))
}
|
wfs_api <- function(base_url = "http://geo.stat.fi/geoserver/wfs", queries) {
if (!grepl("^http", base_url)) stop("Invalid base URL")
ua <- httr::user_agent("https://github.com/rOpenGov/geofi")
url <- httr::modify_url(base_url, query = queries)
message("Requesting response from: ", url)
resp <- httpcache::GET(url, ua)
content <- xml2::read_xml(resp$content)
if (httr::http_error(resp)) {
status_code <- httr::status_code(resp)
exception_texts <- ""
if (status_code == 400) {
exception_texts <- xml2::xml_text(xml2::xml_find_all(content, "//ExceptionText"))
exception_texts <- exception_texts[!grepl("^(URI)", exception_texts)]
exception_texts <- c(exception_texts, paste("URL: ", url))
}
stop(
sprintf(
"WFS API %s request failed [%s]\n %s",
paste(url),
httr::http_status(status_code)$message,
paste0(exception_texts, collapse = "\n ")
),
call. = FALSE
)
}
api_obj <- structure(
list(
url = url,
response = resp
),
class = "wfs_api"
)
api_obj$content <- content
api_obj$content <- content
return(api_obj)
}
|
reshape_grouplevel <- function(x, indices = "all", ...) {
UseMethod("reshape_grouplevel")
}
reshape_grouplevel.estimate_grouplevel <- function(x, indices = "all", ...) {
if (any(indices == "all")) {
indices <- names(x)[!names(x) %in% c("Group", "Level", "Parameter", "CI")]
}
if ("Coefficient" %in% indices) {
indices <- c(indices, "Median", "Mean", "MAP")
}
if ("SE" %in% indices) {
indices <- c(indices, "SD")
}
indices <- names(x)[names(x) %in% unique(indices)]
data <- attributes(x)$data
groups <- unique(x$Group)
for (group in groups) {
data_group <- x[x$Group == group, ]
if (nrow(data_group) == 0) next
data_group[[group]] <- data_group$Level
newvars <- paste0(group, "_", indices)
names(data_group)[names(data_group) %in% indices] <- newvars
data_group$Parameter <- ifelse(data_group$Parameter == "(Intercept)",
"Intercept",
data_group$Parameter
)
data_wide <- datawizard::data_to_wide(
data_group[c(group, newvars, "Parameter")],
rows_from = group,
values_from = newvars,
colnames_from = "Parameter",
sep = "_"
)
if (grepl(":", group)) {
groups <- as.data.frame(t(sapply(strsplit(data_wide[[group]], ":"), function(x) as.data.frame(t(x)))))
names(groups) <- unlist(strsplit(group, ":"))
data_wide <- cbind(groups, data_wide)
data_wide[group] <- NULL
group <- names(groups)
}
data[["__sort_id"]] <- 1:nrow(data)
data <- merge(data, data_wide, by = group, sort = FALSE)
data <- data[order(data[["__sort_id"]]), ]
data[["__sort_id"]] <- NULL
}
row.names(data) <- NULL
class(data) <- c("reshape_grouplevel", class(data))
data
}
summary.reshape_grouplevel <- function(object, ...) {
x <- object[!duplicated(object), ]
row.names(x) <- NULL
x
}
|
xts2ts <- function(series, freq=NULL) {
if (is.null(freq)) {freq = freq_xts(series)}
newTS <- series
if (freq==365) {
newTS[format(zoo::index(series), "%m-%d")=="02-29"] <- NA}
time <- sum(as.numeric(format(zoo::index(series), "%Y")==format(xts::first(zoo::index(series)), "%Y")))
if (.is.leapyear(format(xts::first(zoo::index(series)), "%Y")) &&
as.Date(xts::first(zoo::index(series))) < as.Date(paste0(format(xts::first(zoo::index(series)), "%Y"), "-02-29"))) {time <- time-1}
newstart <- c(as.numeric(format(xts::first(zoo::index(series)), "%Y")), freq-time+1)
newseries <- as.numeric(newTS[!is.na(newTS)])
outTS <- stats::ts(newseries, start=newstart, frequency=freq)
outTS
}
|
blus <- function(mainlm, omit = c("first", "last", "random"), keepNA = TRUE,
exhaust = NA, seed = 1234) {
processmainlm(m = mainlm, needy = FALSE)
n <- nrow(X)
if (!is.na(seed)) set.seed(seed)
omitfunc <- do_omit(omit, n, p, seed)
Xmats <- do_Xmats(X, n, p, omitfunc$omit_ind)
singular_matrix <- FALSE
if (is.singular.mat(Xmats$X_ord_sq) ||
is.singular.mat(Xmats$X0)) {
singular_matrix <- TRUE
message("Passed `omit` argument resulted in singular matrix; BLUS residuals
could not be computed. Randomly chosen combinations of indices to
omit will be attempted according to `exhaust` argument passed.")
ncombn <- choose(n, p)
if ((is.na(exhaust) || is.null(exhaust))) {
dosample <- (ncombn > 1e4)
numsample <- 1e4
} else if (exhaust <= 0) {
stop("`exhaust` is not positive; no attempts will be made to find subset to omit")
} else {
dosample <- (ncombn > exhaust)
numsample <- min(ncombn, exhaust)
}
if (dosample) {
subsetstotry <- unique(t(replicate(numsample, sort(sample(x = n, size = p)))))
rowstodo <- 1:nrow(subsetstotry)
} else {
subsetstotry <- t(utils::combn(n, p))
maxrow <- min(exhaust, nrow(subsetstotry), na.rm = TRUE)
rowstodo <- sample(1:nrow(subsetstotry), maxrow, replace = FALSE)
}
for (r in rowstodo) {
omitfunc <- do_omit(subsetstotry[rowstodo[r], , drop = FALSE], n, p)
Xmats <- do_Xmats(X, n, p, omitfunc$omit_ind)
if (!is.singular.mat(Xmats$X_ord_sq) &&
!is.singular.mat(Xmats$X0)) {
singular_matrix <- FALSE
message(paste0("Success! Subset of indices found that does not yield singular
matrix: ", paste(omitfunc$omit_ind, collapse = ",")))
break
}
}
}
if (singular_matrix) stop("No subset of indices to omit was found that
avoided a singular matrix.")
keep_ind <- setdiff(1:n, omitfunc$omit_ind)
G <- Xmats$X0 %*% solve(Xmats$X_ord_sq) %*% t(Xmats$X0)
Geig <- eigen(G, symmetric = TRUE)
lambda <- sqrt(Geig$values)
q <- as.data.frame(Geig$vectors)
Z <- Reduce(`+`, mapply(`*`, lambda / (1 + lambda),
lapply(q, function(x) tcrossprod(x)),
SIMPLIFY = FALSE))
e0 <- e[omitfunc$omit_ind]
e1 <- e[keep_ind]
e_tilde <- c(e1 - Xmats$X1 %*% solve(Xmats$X0) %*% Z %*% e0)
if (keepNA) {
rval <- rep(NA_real_, n)
rval[keep_ind] <- e_tilde
} else {
rval <- e_tilde
}
rval
}
|
`dsminmeanmax` <-
function(intervalnumber,min,mean,max){
p=seq(0,1,1/(intervalnumber-1));
lo=max-(max-mean)/p;lo[lo<min]=min;
hi=(p*min-mean)/(p-1);hi[hi>max]=max;hi[hi==-Inf]=max
erg=dsstruct(cbind(lo,hi,1/intervalnumber))
}
|
l_cget <- function(target, state) {
UseMethod("l_cget", target)
}
l_cget.loon <- function(target, state) {
obj_eval <- .loonobject(target, as.character)
if(substr(state,1,1) != "-") {
dash_state <- paste("-", state, sep='')
} else {
dash_state <- state
state <- substring(state, 2)
}
type <- obj_eval('info', 'stateType', state)
if (type %in% c("double", "positive_double", "integer",
"positive_integer", "tempcoords", "in_unit_interval")) {
environment(obj_eval)$convert <- function(x) {as.numeric(as.character(x))}
} else if (type == "boolean") {
environment(obj_eval)$convert <- function(x) {as.logical(as.character(x))}
} else if (type == "data") {
environment(obj_eval)$convert <- function(result) {
vars <- tcl('dict', 'keys', result)
l <- sapply(as.character(tcl('dict','keys',result)),
FUN=function(var){
as.character(tcl('dict','get', result, var))
}, simplify=FALSE, USE.NAMES=TRUE)
l[['stringsAsFactors']] <- FALSE
do.call(data.frame, l)
}
} else if (type == "nested_double") {
environment(obj_eval)$convert <- function(result) {
dim <- as.numeric(tcl('llength', result))
out <- vector(mode='list', length=dim)
for (i in 1:dim) {
out[[i]] <- as.numeric(tcl('lindex', result, i-1))
}
out
}
} else if (state %in% c("n","p")) {
environment(obj_eval)$convert <- function(x) {as.numeric(as.character(x))}
} else if (type == "nested_double") {
environment(obj_eval)$convert <- l_nestedTclList2Rlist
} else {
dim <- obj_eval('info', 'stateDimension', state)
if (dim == "1") {
environment(obj_eval)$convert <- function(x) {
paste(rawToChar(as.raw(x)), collapse=' ')
}
}
}
obj_eval('cget', dash_state)
}
l_cget.character <- function(target, state) {
widget <- try(l_create_handle(target), silent = TRUE)
if ("try-error" %in% class(widget)) {
stop(paste0(state, " is not accessible from", target, "via l_cget"))
}
else {
l_cget(widget, state)
}
}
|
generate.lm <- function(baseline, X=NULL, N=1000, type="none", beta=NULL, xvars=3, mu=0, sd=1, censor=.1){
T <- max(baseline$time)
if(type=="none"){
if(is.null(X)) X <- cbind(matrix(rnorm(N*xvars, mean=mu, sd=sd), N, xvars))
if(!is.null(X)) X <- as.matrix(X)
if(is.null(beta)) beta <- as.matrix(rnorm(ncol(X), mean=0, sd=.1))
if(!is.null(beta)) beta <- as.matrix(beta)
XB <- X%*%beta
survival <- t(sapply(XB, FUN=function(x){baseline$survivor^exp(x)}, simplify=TRUE))
survival <- cbind(1, survival)
y <- apply(survival, 1, FUN=function(x){
z <- diff(x < runif(1))
r <- ifelse(all(z==0), T, which.max(z))
return(r)
})
data <- data.frame(X)
data$y <- y
tvc <- FALSE
} else if(type=="tvc"){
if(!is.null(X)) warning("User-supplied X matrices are not implemented when type='tvc'. Generating random X data")
if(is.null(beta)) beta <- as.matrix(rnorm(xvars, mean=0, sd=.1))
if(!is.null(beta)) beta <- as.matrix(beta)
Xmat1 <- expand.grid(N = 1:N, T=1:T)
Xmat1$X1 <- rnorm(N*T)
Xmat2 <- data.frame(matrix(rnorm(xvars*N), N, xvars))
Xmat2$N <- 1:N
Xmat2 <- Xmat2[,-1]
X <- as.matrix(merge(Xmat1, Xmat2, by="N")[,-c(1,2)])
XB <- matrix(X%*%beta, N, T, byrow=TRUE)
survival <- t(apply(XB, 1, FUN=function(x){baseline$survivor^exp(x)}))
survival <- cbind(1, survival)
lifetimes <- apply(survival, 1, FUN=function(x){
z <- diff(x < runif(1))
r <- ifelse(all(z==0), T, which.max(z))
return(r)
})
cen <- quantile(lifetimes, 1-censor)
m <- (2*cen + 1)/T
data <- PermAlgo::permalgorithm(N, T, X,
XmatNames = colnames(X),
eventRandom = lifetimes,
censorRandom = round(runif(N, 1, m*T),0),
betas = beta,
groupByD = FALSE)
data <- dplyr::rename(data, id=Id, failed=Event, start=Start, end=Stop)
data <- dplyr::select(data, -Fup)
rownames(data) <- NULL
tvc <- TRUE
} else if(type=="tvbeta"){
X <- cbind(matrix(rnorm(N*xvars, mean=mu, sd=sd), N, xvars))
if(is.null(beta)) beta <- as.matrix(rnorm(ncol(X), mean=0, sd=.1))
if(!is.null(beta) & ncol(beta) == 1){
beta.mat1 <- data.frame(time = 1:T, one=1)
beta.mat2 <- data.frame(t(beta), one=1)
beta.mat <- merge(beta.mat1, beta.mat2, by="one")
beta.mat <- dplyr::select(beta.mat, -one)
colnames(beta.mat) <- gsub(pattern="X", replacement="beta", colnames(beta.mat))
beta.mat <- dplyr::mutate(beta.mat, beta1 = beta1*log(time))
beta.mat2 <- dplyr::select(beta.mat, -time)
XB <- apply(as.matrix(beta.mat2), 1, FUN=function(b){
as.matrix(X)%*%b
})
}
if(!is.null(beta) & ncol(beta) > 1){
XB <- apply(as.matrix(beta), 1, FUN=function(b){
as.matrix(X)%*%b
})
beta.mat <- cbind(time=1:nrow(beta), beta)
}
survival <- t(apply(XB, 1, FUN=function(x){baseline$survivor^exp(x)}))
survival <- cbind(1, survival)
lifetimes <- apply(survival, 1, FUN=function(x){
z <- diff(x < runif(1))
r <- ifelse(all(z==0), T, which.max(z))
return(r)
})
data <- data.frame(X)
data <- dplyr::mutate(data, y = lifetimes)
beta <- beta.mat
tvc <- FALSE
} else {stop("type must be one of 'none', 'tvc', or 'tvbeta'")}
return(list(data=data, beta=beta, XB=XB, exp.XB = exp(XB), survmat=survival, tvc=tvc))
}
|
wth.param <- function(dly, llim = 0, method = "poisson", year.col = "YEAR", month.col = "MONTH", day.col = "DAY", prcp.col = "PRCP.VALUE", tmin.col = "TMIN.VALUE", tmax.col = "TMAX.VALUE") {
results <- NA
yearlist <- table(dly[[year.col]])
yearlist <- as.numeric(names(yearlist)[yearlist >= 365])
nyears <- length(yearlist)
dly <- dly[ dly[[year.col]] %in% yearlist, ]
dly <- dly[!(dly[[month.col]] == 2 & dly[[day.col]] == 29), ]
dly <- dly[seq(min(which(dly$MONTH == 1 & dly$DAY == 1)), max(which(dly$MONTH == 12 & dly$DAY == 31))), ]
tmin <- dly[[tmin.col]]
tmax <- dly[[tmax.col]]
tave <- (tmin + tmax)/2
A <- mean(tave, na.rm = TRUE)
B <- diff(range(tave, na.rm = TRUE))/4
C <- which.min(apply(matrix(tave, nrow = 365, byrow = FALSE), 1, mean, na.rm = TRUE))
if(method == "poisson") {
dly <- dly[!(dly[[month.col]] == 2 & dly[[day.col]] == 29), ]
dly <- dly[seq(min(which(dly[[month.col]] == 1 & dly[[day.col]] == 1)), max(which(dly[[month.col]] == 12 & dly[[day.col]] == 31))), ]
prcp <- dly[[prcp.col]]
prun <- rle(ifelse(prcp > llim, 1, 0))
lambda <- 1/mean(prun$lengths[prun$values == 0], na.rm = TRUE)
d <- mean(prcp[prcp > 0], na.rm = TRUE)
results <- list(lambda = lambda, depth = d)
}
if(method == "markov") {
mindex <- dly[[month.col]]
mindex <- factor(mindex, levels=1:12)
mnames <- paste0("m", sprintf("%02d", 1:12))
dayT.max <- split(dly[[tmax.col]], mindex)
dayT.min <- split(dly[[tmin.col]], mindex)
dayT.mean <- split((dly[[tmax.col]] + dly[[tmin.col]]) / 2, mindex)
dayP.sum <- split(dly[[prcp.col]], mindex)
monthP.sum <- sapply(dayP.sum, sum, na.rm=TRUE) / nyears
prcpmean <- sapply(dayP.sum, function(x)mean(x[x > 0], na.rm=TRUE))
monthP.max <- sapply(dayP.sum, max, na.rm=TRUE)
monthT.mmin <- sapply(dayT.min, mean, na.rm=TRUE)
monthT.mmax <- sapply(dayT.max, mean, na.rm=TRUE)
tsdmin <- sapply(dayT.min, sd, na.rm=TRUE)
tsdmax <- sapply(dayT.max, sd, na.rm=TRUE)
prsd <- sapply(dayP.sum, function(x)sd(x[x > 0], na.rm=TRUE))
prskew <- sapply(dayP.sum, function(x)e1071::skewness(x[x > 0], na.rm=TRUE))
prskew[is.nan(prskew)] <- 0
dayP.wetv <- ifelse(dly[[prcp.col]] > llim, 1, 0)
dayP.wetv[is.na(dayP.wetv)] <- 0
dayP.wet <- split(dayP.wetv, mindex)
prdays <- sapply(dayP.wet, sum) / sapply(dayP.wet, length)
Ptoday <- c(dayP.wetv, 0)
Pyest <- c(0, dayP.wetv)
Pyt <- paste0(Pyest, Ptoday)
Pyt <- Pyt[-length(Pyt)]
Pyt <- split(Pyt, mindex)
prww <- sapply(Pyt, function(x)sum(x == "11")) / sapply(Pyt, function(x)sum(x == "11" | x == "10"))
prdw <- sapply(Pyt, function(x)sum(x == "01")) / sapply(Pyt, function(x)sum(x == "01" | x == "00"))
results <- data.frame(
tmin = monthT.mmin,
tminsd = tsdmin,
tmax = monthT.mmax,
tmaxsd = tsdmax,
prcp = monthP.sum,
prcpmean = prcpmean,
prcpmax = monthP.max,
prcpsd = prsd,
prcpskew = prskew,
prcpwet = prdays,
prcpww = prww,
prcpdw = prdw)
}
list(params = results, temperature = list(A = A, B = B, C = C), llim = llim, start = min(yearlist), end = max(yearlist))
}
|
opt.TPO <- function (x, k.max = ncol (x), n.lambda = 30, lambda.max, ...)
{
store.opt = TRUE
ret <- .sPCAgrid.opt.ind (x = x, k.max = k.max, n.lambda = n.lambda, lambda.max = lambda.max, store.PCs = store.opt, f.eval = .TPO, ...)
class (ret) <- c (class (ret), "opt.TPO")
return (ret)
}
opt.BIC <- function (x, k.max = ncol (x), n.lambda = 30, lambda.max, ...)
{
store.opt = TRUE
ret <- .sPCAgrid.opt.tot (x = x, k.max = k.max, n.lambda = n.lambda, lambda.max = lambda.max, store.PCs = store.opt, f.eval = .BIC.RSS, ...)
class (ret) <- c (class (ret), "opt.BIC")
return (ret)
}
.flexapply <- function (X, f, NAME, args)
{
args[[NAME]] <- X
do.call (f, args)
}
.sPCAgrid.ml <- function (..., lambda, f.pca = .sPCAgrid.ini, f.apply = lapply)
{
args <- list (...)
args$store.call <- FALSE
f.apply (X = lambda, FUN = .flexapply, f = f.pca, NAME = "lambda", args = args)
}
.sPCAgrid.opt.tot <- function (x, n.lambda = 101, k.max = 2, lambda, lambda.ini, lambda.max, trace = 0, store.PCs = TRUE, f.apply = lapply, f.eval = .TPO, ...)
{
pc.ini <- NULL
f.pca <- .sPCAgrid.ini
if (!missing (lambda.ini) && !is.null (lambda.ini))
{
k.ini <- length (lambda.ini)
pc.ini <- f.pca (x = x, lambda = lambda.ini, k = k.ini, cut.pc = FALSE, ...)
if (!missing (pc.ini) && !is.null (pc.ini))
warning ("argumens \x22pc.ini\x22 AND \x22lambda.ini\x22 were specified. Ignoring \x22pc.ini\x22.")
}
else if (!missing (pc.ini) && !is.null (pc.ini))
{
k.ini = pc.ini$k
lambda.ini <- rep (NA, k.ini)
}
else
{
pc.ini <- NULL
lambda.ini <- NULL
k.ini <- 0
}
p <- ncol (x)
if (k.ini == p)
stop ("all components have already been computed")
if (k.ini + k.max > p)
{
warning (paste ("reducing k.max to", p - k.ini))
k.max <- p - k.ini
}
if (missing (lambda))
{
if (missing (lambda.max) || is.na (lambda.max))
max.fs <- .FSgetLambda (x = x, k = k.max, pc.ini = pc.ini, f.pca = f.pca, scores = FALSE, trace = trace, ...)
else
max.fs <- lambda.max[1]
lambda <- seq (0, max.fs, len = n.lambda)
}
PCs <- .sPCAgrid.ml (x = x, pc.ini = pc.ini, k = k.max, scores = FALSE, cut.pc = TRUE, trace = trace, f.pca = f.pca, lambda = lambda, f.apply = f.apply, ...)
opt <- .SPCAgrid.opt (x, PCs, f.eval, store.PCs, k = k.ini + 1:k.max, singlePC = FALSE, ...)
if (!store.PCs)
return (opt)
ret <- list (pc = list (), pc.noord = list (),
x = x, k.ini = k.ini, opt = opt)
for (i in 1:length (opt$pc))
{
pc <- .cut.pc (opt$pc[[i]], k.ini + k.max)
ret$pc.noord[[i]] <- pc
ret$pc[[i]] <- .orderPCs (pc, k.max, k.ini, TRUE)
}
class (ret) <- "sPCAgrid.opt.tot"
return (ret)
}
.sPCAgrid.opt.eval <- function (x, f.eval = .BIC.RSS, k = 1, ...)
{
if (all (class (x) != "sPCAgrid.opt.tot"))
stop ("x must be of type \"sPCAgrid.opt.tot\"")
.SPCAgrid.opt (x$x, x$opt$PCs, f.eval, storePCs = FALSE, k = 1:k, ...)
}
.SPCAgrid.opt <- function (x, PCs, k, f.eval, store.PCs, f.apply = sapply, singlePC = TRUE, ...)
{
ret <- list ()
if (store.PCs)
ret$PCs <- PCs
if (missing (f.eval) || !is.function (f.eval))
{
if (!store.PCs)
stop ("either store.PCs must be TRUE, or f.eval must be a valid model evaluation function")
return (ret)
}
ret$pc <- ret$k <- list ()
ret$mode <- .GetFunctionName (f.eval, ...)
for (i in 1:length (k))
{
if (singlePC)
K <- k[i]
else
K <- k[1:i]
obj.pc.0 <- f.eval (x = x, pc = PCs[[1]], k = K, ...)
obj.pc.1 <- f.eval (x = x, pc = PCs[[length (PCs)]], k = K, obj.pc.0 = obj.pc.0, ...)
obj <- f.apply (X = PCs, FUN = .flexapply, f = f.eval, NAME = "pc", args = list (x = x, k = K, obj.pc.0 = obj.pc.0, obj.pc.1 = obj.pc.1, ...))
ret$obj <- cbind (ret$obj, obj)
idx.best <- which.min (obj)
ret$idx.best <- cbind (ret$idx.best, idx.best)
ret$pc[[i]] <- PCs[[idx.best]]
ret$k[[i]] <- K
}
if (!store.PCs)
return (ret$pc)
return (ret)
}
.sPCAgrid.opt.ind <- function (x, n.lambda = 101, k.max = ncol (x), lambda.ini, lambda.max, trace = 0, store.PCs = TRUE, f.eval = .TPO, ...)
{
pc.ini <- NULL
f.pca <- .sPCAgrid.ini
if (!missing (lambda.ini) && !is.null (lambda.ini))
{
k.ini <- length (lambda.ini)
pc.ini <- f.pca (x = x, lambda = lambda.ini, k = k.ini, cut.pc = FALSE, ...)
if (!missing (pc.ini))
stop ("either store.PCs must be TRUE, or f.eval must be a valid model evaluation function")
}
else if (!missing (pc.ini) && !is.null (pc.ini))
{
k.ini = pc.ini$k
lambda.ini <- rep (NA, k.ini)
}
else
{
pc.ini <- NULL
lambda.ini <- NULL
k.ini <- 0
}
if (!missing (lambda.max))
lambda.max <- rep (lambda.max, len = k.max)
p <- ncol (x)
if (k.ini == p)
stop ("all components have already been computed")
if (k.ini + k.max > p)
{
warning (paste ("reducing k.max to", p - k.ini))
k.max <- p - k.ini
}
if (store.PCs)
opt <- list ()
for (i in 1:k.max)
{
if (missing (lambda.max) || is.na (lambda.max[i]))
max.fs <- .FSgetLambda (x = x, k = 1, pc.ini = pc.ini, f.pca = f.pca, scores = FALSE, trace = trace, ...)
else
max.fs <- lambda.max[i]
lambda <- seq (0, max.fs, len = n.lambda)
cur.pcs <- .sPCAgrid.ml (x = x, pc.ini = pc.ini, k = 1, scores = FALSE, cut.pc = FALSE, trace = trace, lambda = lambda, f.pca = f.pca, ...)
cur.opt <- .SPCAgrid.opt (x, cur.pcs, f.eval, store.PCs, k = k.ini + i, ...)
if (store.PCs)
opt[[i]] <- cur.opt
pc.ini <- cur.opt$pc[[1]]
}
pc.ini <- .cut.pc (pc.ini, k.ini + k.max)
pc <- .orderPCs (pc.ini, k.max, k.ini, TRUE)
if (!store.PCs)
return (pc)
ret <- list (pc = pc, pc.noord = pc.ini, x = x, k.ini = k.ini, opt = opt)
class (ret) <- "sPCAgrid.opt.ind"
return (ret)
}
.GFSL.calc.sPCA <- function (k.check, lambda = 1, f.pca = sPCAgrid, zero.tol = 1e-10, trace = 0, check.all = TRUE, ...)
{
if (trace >= 5)
.flush.cat ("checking lambda: ", lambda, "\r\n", sep = "")
if (check.all)
return (f.pca (lambda = lambda, k = k.check, ...)$loadings[, 1:k.check])
return (f.pca (lambda = lambda, ...)$loadings[, k.check, drop = FALSE])
}
.GFSL.is.sparse <- function (k.check, zero.tol = 1e-10, ...)
{
load <- .GFSL.calc.sPCA (k.check = k.check, zero.tol = zero.tol, ...)
return ((sum (abs (load)> zero.tol) ) == ncol (load))
}
.GFSL.is.same <- function (sparse.load, zero.tol = 1e-10, ...)
{
load <- .GFSL.calc.sPCA (zero.tol = zero.tol, ...)
return (sum (abs (sparse.load - load)) <= zero.tol)
}
.GFSL.find.max <- function (lambda = 1, ...)
{
for (i in 1:16)
{
if (.GFSL.is.sparse (lambda = lambda, ...))
return (lambda)
lambda = lambda * 2
}
return (NULL)
}
.GFSL.find.range <- function (lbL , ubL, niter = 6, f.sparse = .GFSL.is.sparse, ...)
{
for (i in 1:niter)
{
mbL <- mean (c(ubL, lbL))
if (f.sparse (lambda = mbL, ...))
ubL <- mbL
else
lbL <- mbL
}
return (ubL)
}
.getFullSparseLambda.all <- function (uBound, ...)
{
if (missing (uBound))
uBound <- .GFSL.find.max (lambda = 1, check.all = TRUE, ...)
if (is.null (uBound))
stop ("cannot find full sparse model")
lBound <- ifelse (uBound > 1, uBound / 2, 0)
.GFSL.find.range (lbL = lBound, ubL = uBound, f.sparse = .GFSL.is.sparse, ...)
}
.getFullSparseLambda.indiv <- function (niter = 15, ...)
{
uBound <- .GFSL.find.max (lambda = 1, check.all = FALSE, ...)
if (!is.null (uBound))
{
lBound <- ifelse (uBound > 1, uBound / 2, 0)
return (.GFSL.find.range (lbL = lBound, ubL = uBound, f.sparse = .GFSL.is.sparse, check.all = FALSE, ...))
}
lambda.max <- 1e6
load <- .GFSL.calc.sPCA (lambda = lambda.max, check.all = FALSE, ...)
.GFSL.find.range (lbL = 0, ubL = lambda.max, niter = 25, f.sparse = .GFSL.is.same, sparse.load = load, check.all = FALSE, ...)
}
.FSgetLambda <- function (...)
{
kc <- .FSgetK.check (...)
if (.FSpossible (...))
.FSgetLambdaFS (..., k.check = kc)
else
.FSgetLambdaC (..., k.check = kc)
}
.FSgetK.check <- function (pc.ini = NULL, k.ini, k, ...)
{
if (is.null (pc.ini))
return (1:k)
if (missing (k.ini))
k.ini <- pc.ini$k
return (k.ini + 1 : k)
}
.FSpossible <- function (pc.ini = NULL, k.ini, k, zero.tol = 1e-16, ...)
{
if (is.null (pc.ini))
return (TRUE)
if (missing (k.ini))
k.ini <- pc.ini$k
l0 <- abs (pc.ini$loadings[, 1:k.ini, drop = FALSE]) > zero.tol
return (any (rowSums (l0) == 0))
}
.FSgetLambdaFS <-function (iter = 15, ...)
{
.FSiter (..., f.test = .FSisFullSparse)
}
.FSgetLambdaC <- function (zero.tol, ...)
{
pc0 <- .FScalc (..., lambda = 2^20)[[1]]
.FSiter (..., pc0 = pc0, f.test = .FScompL)
}
.FSiter <- function (niter = 8, testL = testE^-2, testU = testE^5, testE = 16, ...)
{
L <- testL
U <- testU
for (i in 1:2)
{
lambda.test <- testE^seq (log (L) / log (testE), log (U) / log (testE), len = niter)
sparse <- .FStest (..., lambda = lambda.test)
if (sparse[1])
return (lambda.test[1])
if (!sparse[niter])
return (lambda.test[niter])
idx <- which (sparse)[1]
L <- lambda.test[idx-1]
U <- lambda.test[idx]
}
return (lambda.test[idx])
}
.FStest <- function (..., f.test)
{
PCs <- .FScalc (...)
sapply (PCs, f.test, ...)
}
.FScompL <- function (pc, pc0, k.check, zero.tol = 1e-16, ...)
{
sum (abs (.loadSgnU (pc$load [, k.check, drop = FALSE]) - .loadSgnU (pc0$load [, k.check, drop = FALSE])) > sqrt (zero.tol)) == 0
}
.FScalc <- function (...) { .sPCAgrid.ml (...) }
.FSisFullSparse <- function (pc, k.check, zero.tol = 1e-16, ...)
{
n.k <- length(k.check)
sum (abs (pc$load [, k.check]) > sqrt (zero.tol)) == n.k
}
.loadSgnU <- function (x)
{
idx.max <- apply (abs (x), 2, which.max)
sgn <- sign (x[cbind (idx.max, 1:ncol (x))])
if (length (sgn) == 1)
return (x * sgn)
return (x %*% diag (sgn))
}
|
Hox02 <- matrix(
c(-0.264, 0.086, 3,
-0.230, 0.106, 1,
0.166, 0.055, 2,
0.173, 0.084, 4,
0.225, 0.071, 3,
0.291, 0.078, 6,
0.309, 0.051, 7,
0.435, 0.093, 9,
0.476, 0.149, 3,
0.617, 0.095, 6,
0.651, 0.110, 6,
0.718, 0.054, 7,
0.740, 0.081, 9,
0.745, 0.084, 5,
0.758, 0.087, 6,
0.922, 0.103, 5,
0.938, 0.113, 5,
0.962, 0.083, 7,
1.522, 0.100, 9,
1.844, 0.141, 9),
ncol=3, byrow=TRUE)
Hox02 <- cbind(1:nrow(Hox02), Hox02)
dimnames(Hox02) <- list(NULL, c("study", "yi","vi","weeks"))
Hox02 <- data.frame(Hox02)
|
context("bs4CardLabel")
test_that("is shiny tag?", {
golem::expect_shinytag(bs4CardLabel(text = 1, status = "warning"))
})
test_that("Long text warning", {
expect_warning(bs4CardLabel(text = "jssjjsjssjsjsj", status = "danger"))
})
test_that("basis", {
expect_error(bs4CardLabel(text = 1))
})
test_that("status", {
labelCl <- bs4CardLabel(text = 1, status = "danger")$attribs$class
expect_match(labelCl, "badge bg-danger")
})
test_that("Is wrapper tag span?", {
wrapperType <- bs4CardLabel(text = 1, status = "primary")$name
expect_match(wrapperType, "span")
})
test_that("no tooltip", {
labelTagChildren <- bs4CardLabel(text = 1, status = "primary")$children
expect_length(labelTagChildren, 1)
})
test_that("tooltip", {
labelTagProps <- bs4CardLabel(text = 1, tooltip = "prout", status = "primary")$attribs
expect_length(labelTagProps, 3)
})
|
ReconstructedCountSet <- R6Class("ReconstructedCountSet",
inherit = ReconstructedFeatureSet,
public = list(
KR = NULL,
initialize = function(fs=NULL, ro=NULL) {
if (!is.null(fs)) {
super$initialize(fs, ro)
if (!is.null(fs$data) & (length(fs$data) > 0)) {
for (name in names(fs$data)) {
self$data[[name]] <- cbind(self$data[[name]], C=fs$data[[name]][,"C"])
}
}
}
},
getKR = function() {
if (is.null(self$KR)) {
yhat <- function(r, mu, kappa) {
kr.yhat(r, mu[,1:2], mu[,3], kappa)
}
compute.conc <- function(mu) {
kr.compute.concentration(mu[,1:2], mu[,3])
}
Gss <- list()
for (id in self$getIDs()) {
Gss[[id]] <- self$getFeature(id)
}
for (n in names(Gss)) {
if (sum(Gss[[n]][,"C"]) <= 2) {
Gss[[n]] <- NULL
}
}
return(compute.kernel.estimate(Gss, self$ro$phi0, yhat, compute.conc))
}
return(self$KR)
}
)
)
projection.ReconstructedCountSet <-
function(r,
phi0,
transform=identity.transform,
ids=r$getIDs(),
axisdir=cbind(phi=90, lambda=0),
projection=azimuthal.equalarea,
proj.centre=cbind(phi=0, lambda=0),
markup=NULL,
max.proj.dim=NULL,
...)
{
for (id in ids) {
if (!is.null(r$getFeature(id))) {
if (nrow(r$getFeature(id)) > 0) {
rc <- projection(rotate.axis(transform(r$getFeature(id)[,c("phi", "lambda")],
phi0=r$phi0),
axisdir*pi/180),
proj.centre=pi/180*proj.centre)
text(rc[,"x"], rc[,"y"], r$getFeature(id)[,"C"],
col=r$cols[[id]],
...)
}
}
}
}
|
"pois.daly" <-
function(x, pt = 1, conf.level = 0.95){
xc <- cbind(x,conf.level,pt)
pt2 <- xc[,3]
results <- matrix(NA,nrow(xc),6)
cipois <-
function(x, conf.level = 0.95){
if(x!=0){
LL <- qgamma((1 - conf.level)/2, x)
UL <- qgamma((1 + conf.level)/2, x + 1)
} else {
if(x==0){
LL <- 0
UL <- -log(1 - conf.level)
}
}
data.frame(x = x, lower = LL, upper = UL)
}
for(i in 1:nrow(xc)){
alp <- 1-xc[i,2]
daly <- cipois(x = xc[i, 1], conf.level = xc[i, 2])
LCL <- daly$lower/pt2[i]
UCL <- daly$upper/pt2[i]
results[i,] <- c(xc[i,1],pt2[i],xc[i,1]/pt2[i],LCL,UCL,xc[i,2])
}
coln <- c("x","pt","rate","lower","upper","conf.level")
colnames(results) <- coln
data.frame(results)
}
|
library(statpsych)
test_that("ci.prop1 returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "LL", "UL"
)
res <- ci.prop1(.05, 12, 100)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(2, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.pairs.prop1 returns valid matrix", {
colnames_expected <- c(
"", "", "Estimate", "SE", "LL", "UL"
)
f <- c(125, 82, 92)
res <- ci.pairs.prop1(.05, f)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(3, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.prop2 returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "LL", "UL"
)
res <- ci.prop2(.05, 35, 21, 150, 150)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.ratio.prop2 returns valid matrix", {
colnames_expected <- c(
"Estimate", "LL", "UL"
)
res <- ci.ratio.prop2(.05, 35, 21, 150, 150)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.lc.prop.bs returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "z", "p", "LL", "UL"
)
f <- c(26, 24, 38)
n <- c(60, 60, 60)
c <- c(-.5, -.5, 1)
res <- ci.lc.prop.bs(.05, f, n, c)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.pairs.prop.bs returns valid matrix", {
colnames_expected <- c(
"", "", "Estimate", "SE", "z", "p", "LL", "UL"
)
f <- c(111, 161, 132)
n <- c(200, 200, 200)
res <- ci.pairs.prop.bs(.05, f, n)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(3, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.slope.prop.bs returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "z", "p", "LL", "UL"
)
f <- c(14, 27, 38)
n <- c(100, 100, 100)
x <- c(10, 20, 40)
res <- ci.slope.prop.bs(.05, f, n, x)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.prop.ps returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "LL", "UL"
)
res <- ci.prop.ps(.05, 12, 26, 4, 6)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.ratio.prop.ps returns valid matrix", {
colnames_expected <- c(
"Estimate", "LL", "UL"
)
res <- ci.ratio.prop.ps(.05, 12, 26, 4, 6)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.condslope.log returns valid matrix", {
colnames_expected <- c(
"Estimate", "exp(Estimate)", "z", "p", "LL", "UL"
)
res <- ci.condslope.log(.05, .132, .154, .031, .021, .015, 5.2, 10.6)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(2, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.oddsratio returns valid matrix", {
colnames_expected <- c(
"Estimate", "LL", "UL"
)
res <- ci.oddsratio(.05, 229, 28, 96, 24)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.yule returns valid matrix", {
colnames_expected <- c(
"Estimate", "LL", "UL"
)
res <- ci.yule(.05, 229, 28, 96, 24)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.phi returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "LL", "UL"
)
res <- ci.phi(.05, 229, 28, 96, 24)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.biphi returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "LL", "UL"
)
res <- ci.biphi(.05, 46, 15, 100, 100)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.tetra returns valid matrix", {
colnames_expected <- c(
"Estimate", "LL", "UL"
)
res <- ci.tetra(.05, 46, 15, 54, 85)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.kappa returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "LL", "UL"
)
res <- ci.kappa(.05, 31, 12, 4, 58)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(2, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.agree returns valid matrix", {
colnames_expected <- c(
"Estimate", "SE", "LL", "UL"
)
res <- ci.agree(.05, 100, 80, 4)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("ci.popsize returns valid matrix", {
colnames_expected <- c(
"Estimate", "LL", "UL"
)
res <- ci.popsize(.05, 794, 710, 741)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("test.prop1 returns valid matrix", {
colnames_expected <- c(
"Estimate", "z", "p"
)
res <- test.prop1(9, 20, .2)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("test.prop2 returns valid matrix", {
colnames_expected <- c(
"Estimate", "z", "p"
)
res <- test.prop2(11, 26, 50, 50)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("test.prop.bs returns valid matrix", {
colnames_expected <- c(
"Chi-square", "df", "p"
)
f <- c(35, 30, 15)
n <- c(50, 50, 50)
res <- test.prop.bs (f, n)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("test.prop.ps returns valid matrix", {
colnames_expected <- c(
"Estimate", "z", "p"
)
res <- test.prop.ps(156, 96, 68, 80)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
test_that("size.ci.prop1 returns valid numeric", {
res <- size.ci.prop1(.05, .4, .2)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 93)
})
test_that("size.ci.prop2 returns valid numeric", {
res <- size.ci.prop2(.05, .4, .2, .15)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 274)
})
test_that("size.ci.ratio.prop2 returns valid numeric", {
res <- size.ci.ratio.prop2(.05, .2, .1, 2)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 416)
})
test_that("size.ci.lc.prop.bs returns valid numeric", {
p <- c(.25, .30, .50, .50)
v <- c(.5, .5, -.5, -.5)
res <- size.ci.lc.prop.bs(.05, p, .2, v)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 87)
})
test_that("size.ci.prop.ps returns valid numeric", {
p <- c(.25, .30, .50, .50)
v <- c(.5, .5, -.5, -.5)
res <- size.ci.prop.ps(.05, .2, .3, .8, .1)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 118)
})
test_that("size.ci.ratio.prop.ps returns valid numeric", {
res <- size.ci.ratio.prop.ps(.05, .4, .2, .7, 2)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 67)
})
test_that("size.ci.agree returns valid numeric", {
res <- size.ci.agree(.05, .8, .2)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 139)
})
test_that("size.test.prop1 returns valid numeric", {
res <- size.test.prop1(.05, .9, .5, .2)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 66)
})
test_that("size.test.prop2 returns valid numeric", {
res <- size.test.prop2(.05, .8, .2, .4, .2)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 79)
})
test_that("size.test.lc.prop.bs returns valid numeric", {
p <- c(.25, .30, .50, .50)
v <- c(.5, .5, -.5, -.5)
res <- size.test.lc.prop.bs(.05, .9, p, .15, v)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 105)
})
test_that("size.equiv.prop2 returns valid numeric", {
res <- size.equiv.prop2(.1, .8, .30, .35, .15)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 288)
})
test_that("size.supinf.prop2 returns valid numeric", {
res <- size.supinf.prop2(.05, .9, .35, .20, .05)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 408)
})
test_that("size.test.prop.ps returns valid numeric", {
res <- size.test.prop.ps(.05, .80, .4, .3, .5, .1)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 177)
})
test_that("size.equiv.prop.ps returns valid numeric", {
res <- size.equiv.prop.ps(.1, .8, .30, .35, .40, .15)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 173)
})
test_that("size.supinf.prop.ps returns valid numeric", {
res <- size.supinf.prop.ps(.05, .9, .35, .20, .45, .05)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(res[[1,1]], 227)
})
test_that("iqv returns valid matrix", {
colnames_expected <- c(
"Simpson", "Berger", "Shannon"
)
f <- c(10, 46, 15, 3)
res <- iqv(f)
testthat::expect_equal(class(res), c("matrix", "array"))
testthat::expect_equal(dim(res), c(1, length(colnames_expected)))
testthat::expect_equal(colnames(res), colnames_expected)
})
|
utils::globalVariables(c("rawdata", "stratum"))
roundR <- function(roundin, level = 2, smooth = FALSE,
textout = TRUE, drop0 = FALSE, .german = FALSE, .bigmark = FALSE) {
if (.german) {
textout <- TRUE
}
decimalmark <- ifelse(.german, ",", ".")
bigmark <- ifelse(.german, ".", ",")
if (!.bigmark) {
bigmark <- ""
}
if (!is.matrix(roundin)) {
roundin <- matrix(roundin)
}
roundin <- as.numeric(roundin)
roundout <- roundin
roundlevel <- 0
roundlevel <- max(
0,
level - floor(
log10(
max(abs(roundin), na.rm = TRUE)
) + 1
)
)
roundout[which(!is.na(roundout))] <-
round(roundin[which(!is.na(roundin))], roundlevel)
if (smooth & max(abs(roundout), na.rm = TRUE) != 0) {
roundout[which(!is.na(roundout))] <-
round(
roundin[which(!is.na(roundin))] /
10^ceiling(log10(max(abs(roundin), na.rm = TRUE)) - level)
) *
10^ceiling(log10(max(abs(roundin), na.rm = TRUE)) - level)
}
if (textout) {
roundout[which(!is.na(roundout))] <-
formatC(roundout[which(!is.na(roundout))],
format = "f",
digits = roundlevel, drop0trailing = drop0,
big.mark = bigmark,
decimal.mark = decimalmark
)
}
return(roundout)
}
markSign <- function(SignIn, plabel = c("n.s.", "+", "*", "**", "***")) {
SignIn <- as.numeric(SignIn)
SignOut <- cut(SignIn,
breaks = c(-Inf, .001, .01, .05, .1, 1),
labels = rev(plabel)
)
return(SignOut)
}
formatP <- function(pIn, ndigits = 3, textout = TRUE, pretext = FALSE,
mark = FALSE, german_num = FALSE) {
decimal.mark <- ifelse(german_num, ",", ".")
pIn_is_matrix <- is.matrix(pIn)
if(pIn_is_matrix){
pIn <- apply(pIn,c(1,2),as.numeric)
} else{
pIn <- as.numeric(pIn)
}
formatp <- NA_character_
if (length(na.omit(pIn))>0) {
if (!pIn_is_matrix) {
pIn <- matrix(pIn)
}
formatp <- apply(
X = pIn, MARGIN = c(1, 2), max,
10**(-ndigits), na.rm = FALSE
) %>%
apply(MARGIN = c(1, 2), round, ndigits) %>%
apply(
MARGIN = c(1, 2),
formatC, format = "f",
digits = ndigits, drop0trailing = FALSE,
decimal.mark = decimal.mark
)
if (pretext) {
for (row_i in 1:nrow(pIn)) {
for (col_i in 1:ncol(pIn)) {
formatp[row_i, col_i] <- paste(
ifelse(pIn[row_i, col_i] < 10**(-ndigits),
"<", "="
),
formatp[row_i, col_i]
)
}
}
}
if (mark) {
formatp <- matrix(
paste(
formatp,
apply(gsub("[\\<\\=]", "", formatp), c(1, 2), markSign)
),
ncol = ncol(pIn)
)
}
if (textout == FALSE & pretext == FALSE) {
formatp <- apply(formatp, MARGIN = c(1, 2), as.numeric)
}
if(!pIn_is_matrix){
formatp <- as.vector(formatp)
}
}
return(formatp)
}
FindVars <- function(varnames, allnames = NULL,
exact = FALSE, exclude = NA, casesensitive = TRUE,
fixed = FALSE) {
if (is.null(allnames)) {
allnames <- colnames(get("rawdata"))
}
if (fixed) {
exact <- FALSE
}
allnames_tmp <- allnames
if (!casesensitive) {
varnames <- tolower(varnames)
allnames_tmp <- tolower(allnames)
exclude <- tolower(exclude)
}
vars <- numeric()
evars <- numeric()
if (exact) {
for (i in 1:length(varnames)) {
vars <- c(vars, grep(paste0("^", varnames[i], "$"), allnames_tmp))
}
vars <- unique(vars)
} else {
for (i in 1:length(varnames)) {
vars <- c(vars, grep(varnames[i], allnames_tmp,
fixed = fixed
))
}
vars <- sort(unique(vars))
if (any(!is.na(exclude))) {
for (i in 1:length(exclude))
{
evars <- c(evars, grep(exclude[i], allnames_tmp))
}
evars <- unique(na.omit(match(
sort(unique(evars)), vars
)))
if (length(evars) > 0) {
vars <- vars[-evars]
}
}
vars <- unique(vars)
}
return(list(
index = vars,
names = allnames[vars],
bticked = bt(allnames[vars]),
symbols = rlang::syms(allnames[vars]),
count = length(vars)
))
}
print_kable <- function(t, nrows = 30, caption = "",
ncols = 100, ...) {
for (block_i in 1:ceiling(nrow(t) / nrows)) {
for (col_i in 1:ceiling((ncol(t) - 1) / ncols)) {
if (block_i + col_i > 2) {
cat("\\newpage\n\n")
}
print(
knitr::kable(
t[
(1 + (block_i - 1) * nrows):
min(nrow(t), block_i * nrows),
c(1, (2 + (col_i - 1) * ncols):min((1 + col_i * ncols), ncol(t)))
],
row.names = FALSE,
caption = paste0(
ifelse(block_i + col_i > 2, "continued: ", ""),
caption,
" \n \n "
)
)
)
cat(" \n \n")
}
}
}
pdf_kable <- function(.input, width1 = 6,
twidth = 14,
tposition = "left",
innercaption = NULL,
caption = "",
foot = NULL,
escape = TRUE) {
ncols <- ncol(.input)
out <- knitr::kable(.input,
format = "latex", booktabs = TRUE,
linesep = "",
escape = escape, caption = caption,
align = c("l", rep("c", ncols - 1))
) %>%
kableExtra::kable_styling(
position = tposition,
latex_options = c(
"striped",
"hold_position"
)
) %>%
kableExtra::column_spec(-1,
width = paste0((twidth - width1) / (ncols - 1), "cm"),
) %>%
kableExtra::column_spec(1, bold = TRUE, width = paste0(width1, "cm")) %>%
kableExtra::row_spec(0, bold = TRUE)
if (!is.null(innercaption)) {
caption1 <- c(caption = ncols)
names(caption1) <- caption
out <- out %>%
kableExtra::add_header_above(caption1, bold = TRUE)
}
if (!is.null(foot)) {
out <- out %>%
kableExtra::footnote(general = foot)
}
return(out)
}
cn <- function(data = rawdata) {
colnames(data)
}
bt <- function(x, remove = FALSE) {
if (remove) {
return(gsub("`", "", x))
} else {
return(paste0("`", x, "`"))
}
}
tab.search <- function(searchdata = rawdata, pattern,
find.all = T, names.only = FALSE) {
if (!is.character(pattern)) {
pattern <- as.character(pattern)
}
positions <- purrr::map(searchdata, str_which, pattern = pattern) %>% purrr::compact()
if (!find.all) {
positions <- purrr::map(positions, nth, n = 1)
}
if (names.only) {
positions <- names(positions)
}
return(positions)
}
|
toString.XMLNode <-
function(x, ...)
{
.tempXMLOutput = ""
con <- textConnection(".tempXMLOutput", "w", local = TRUE)
sink(con)
print(x)
sink()
close(con)
paste(.tempXMLOutput, collapse="\n")
}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.