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neg2ELratio_nocrossings <- function(alpha, fit, fit1, fit2, fit_time_restrict_boot, nn, lowerbindx_boot, upperbindx_boot, sum_DWknGImuw_1_big, sum_DWknGImuw_2_big){
test_nocross = 0
HW_CBdistr = apply(abs(sum_DWknGImuw_2_big[,lowerbindx_boot:upperbindx_boot]-sum_DWknGImuw_1_big[,lowerbindx_boot:upperbindx_boot]),1,max)
HW_CBcrit = as.vector(quantile(HW_CBdistr,1-alpha))
S1_hat = (((c(1,fit1$surv)[cumsum(c(0,fit$time) %in% c(0,fit1$time))])[-1])[fit_time_restrict_boot])[lowerbindx_boot:upperbindx_boot]
S2_hat = (((c(1,fit2$surv)[cumsum(c(0,fit$time) %in% c(0,fit2$time))])[-1])[fit_time_restrict_boot])[lowerbindx_boot:upperbindx_boot]
diff = log(S1_hat)-log(S2_hat)
HW_CB_ubs = diff+HW_CBcrit/sqrt(sum(nn))
HW_CB_lbs = diff-HW_CBcrit/sqrt(sum(nn))
eq_S_all = sum(HW_CB_lbs <= 0) == (upperbindx_boot - lowerbindx_boot + 1) & sum(HW_CB_ubs >= 0) == (upperbindx_boot - lowerbindx_boot + 1)
g_S_all = sum(HW_CB_ubs >= 0) == (upperbindx_boot - lowerbindx_boot + 1) & sum(HW_CB_lbs <= 0) != (upperbindx_boot - lowerbindx_boot + 1)
if (g_S_all | eq_S_all) {
test_nocross = 1
}
return(list(test_nocross = test_nocross))
} |
require(geophys)
opar=par(no.readonly=TRUE)
s1 = matrix(c(5,2,2,3) , byrow=TRUE, ncol=2, nrow=2)
Stensor = s1
theta = 0*pi/180
rot1 = cbind( c( cos(theta), -sin(theta)), c(sin(theta), cos(theta)))
DoMohrFig1(s1, rot1)
s1 = matrix(c(15,-2,-2,3) , byrow=TRUE, ncol=2, nrow=2)
Stensor = s1
DoMohr(Stensor)
Stensor = matrix(c(
15, 0, 0,
0, 10, 0,
0, 0, 5), ncol=3)
M = DoMohr(Stensor)
par(ask=FALSE)
Stensor = matrix(c(
15, 0, 0,
0, 10, 0,
0, 0, 5), ncol=3)
stress(Stensor=Stensor)
P1 = c(0.2, 1, 1, 0)
P2 = c(1, 0.1, 1, 0)
P3 = c(1, 1, 0.4, 0)
S = stressSETUP(P1, P2, P3, xscale=30 )
stress(PPs = S$PPs, Rview =S$Rview, xscale = S$xscale, Stensor=Stensor )
S = stressSETUP( )
Nvec = NORMvec(S$PPs, S$xscale, S$Rview, S$aglyph , add = FALSE)
Stensor = matrix(c(
15, 0, 0,
0, 8, 0,
0, 0, 5), ncol=3)
Mstress = Maxstress(Nvec, Stensor)
DoMohr(Stensor)
axis(1)
axis(2)
points(Mstress$sigNORMmax , Mstress$tauSHEARmax, pch=21, col='blue' , bg='gold' )
u=par('usr')
segments(0, Mstress$tauSHEARmax, Mstress$sigNORMmax ,
Mstress$tauSHEARmax, lty=2, col='green' , lwd=3 )
text(mean(c(0, Mstress$tauSHEARmax)), Mstress$tauSHEARmax,
"MaxShear in Plane", pos=3)
segments(Mstress$sigNORMmax , u[3] , Mstress$sigNORMmax ,
Mstress$tauSHEARmax, lty=2, col='purple' , lwd=3 )
text(Mstress$sigNORMmax , u[3], "MaxNormal stress", adj=c(0,-1) )
GG = randpoles(30, 40, 10, opt="norm", N=20)
graphics.off()
par(ask=TRUE)
dev.new(width=10, height=6 )
par(mfrow=c(1,2))
RFOC::net()
RFOC::qpoint(30, 40, col = "red", UP=FALSE )
RFOC::qpoint(GG$az, GG$dip, col = "blue", UP=FALSE )
rplane = RFOC::lowplane(30-90, 40, UP=TRUE, col='red')
for(i in 1:length(GG$az))
{
rplane = RFOC::lowplane(GG$az[i]-90, GG$dip[i], UP=TRUE, col='blue')
}
g = RSEIS::TOCART(GG$az, GG$dip)
AA = DoMohr(Stensor)
for(i in 1:length(g$az))
{
KVEC = c(g$x[i], g$y[i],g$z[i])
Mstress = Maxstress(KVEC, Stensor)
points(Mstress$sigNORMmax , Mstress$tauSHEARmax, pch=21, col='blue' , bg='gold' )
}
par(opar) |
gensvm.refit <- function(fit, x, y, p=NULL, lambda=NULL, kappa=NULL,
epsilon=NULL, weights=NULL, kernel=NULL, gamma=NULL,
coef=NULL, degree=NULL, kernel.eigen.cutoff=NULL,
max.iter=NULL, verbose=NULL, random.seed=NULL)
{
p <- if(is.null(p)) fit$p else p
lambda <- if(is.null(lambda)) fit$lambda else lambda
kappa <- if(is.null(kappa)) fit$kappa else kappa
epsilon <- if(is.null(epsilon)) fit$epsilon else epsilon
weights <- if(is.null(weights)) fit$weights else weights
kernel <- if(is.null(kernel)) fit$kernel else kernel
gamma <- if(is.null(gamma)) fit$gamma else gamma
coef <- if(is.null(coef)) fit$coef else coef
degree <- if(is.null(degree)) fit$degree else degree
kernel.eigen.cutoff <- (if(is.null(kernel.eigen.cutoff))
fit$kernel.eigen.cutoff else kernel.eigen.cutoff)
max.iter <- if(is.null(max.iter)) fit$max.iter else max.iter
verbose <- if(is.null(verbose)) fit$verbose else verbose
random.seed <- if(is.null(random.seed)) fit$random.seed else random.seed
errfunc <- getOption('error')
options(error=function() {})
newfit <- gensvm(x, y, p=p, lambda=lambda, kappa=kappa, epsilon=epsilon,
weights=weights, kernel=kernel, gamma=gamma, coef=coef,
degree=degree, kernel.eigen.cutoff=kernel.eigen.cutoff,
verbose=verbose, max.iter=max.iter, seed.V=coef(fit))
options(error=errfunc)
return(newfit)
} |
write.csv <- function (...) {
UseMethod("write.csv")
}
write.csv.default <- function (...) {
utils::write.csv(...)
}
write.csv.AlphaPart <- function (x, file, traitsAsDir=FALSE, csv2=TRUE, row.names=FALSE, ...) {
if(length(file) > 1) stop("'file' argument must be of length one")
if(!("AlphaPart" %in% class(x))) stop("'x' must be of a 'AlphaPart' class")
fileOrig <- sub(pattern=".csv$", replacement="", x=file)
ret <- c()
for(i in 1:(length(x)-1)) {
if(traitsAsDir) {
dir.create(path=file.path(dirname(fileOrig), x$info$lT[i]), recursive=TRUE, showWarnings=FALSE)
file <- file.path(dirname(fileOrig), x$info$lT[i], basename(fileOrig))
}
fileA <- paste(file, "_", x$info$lT[i], ".csv", sep="")
ret <- c(ret, fileA)
cat(fileA, "\n")
if(csv2) {
write.csv2(x=x[[i]], file=fileA, row.names=row.names, ...)
} else {
write.csv(x=x[[i]], file=fileA, row.names=row.names, ...)
}
}
invisible(ret)
}
write.csv.summaryAlphaPart <- function (x, file, traitsAsDir=FALSE, csv2=TRUE, row.names=FALSE, ...) {
if(length(file) > 1) stop("'file' argument must be of length one")
if(!("summaryAlphaPart" %in% class(x))) stop("'x' must be of a 'summaryAlphaPart' class")
fileOrig <- sub(pattern=".csv$", replacement="", x=file)
ret <- c()
for(i in 1:(length(x)-1)) {
if(traitsAsDir) {
dir.create(path=file.path(dirname(fileOrig), x$info$lT[i]), recursive=TRUE, showWarnings=FALSE)
file <- file.path(dirname(fileOrig), x$info$lT[i], basename(fileOrig))
}
fileA <- paste(file, x$info$lT[i], ".csv", sep="_")
ret <- c(ret, fileA)
cat(fileA, "\n")
if(csv2) {
write.csv2(x=x[[i]], file=fileA, row.names=row.names, ...)
} else {
write.csv(x=x[[i]], file=fileA, row.names=row.names, ...)
}
}
invisible(ret)
} |
acontext("path key")
path.list <- list()
N <- 100
x <- 1:N
point <- data.frame(
showSelected.i=1:2)
set.seed(1)
for(group.i in 1:2){
for(showSelected.i in point$showSelected.i){
path.list[[paste(group.i, showSelected.i)]] <-
data.frame(group.i, showSelected.i, x, y=rnorm(N, group.i))
}
}
path <- do.call(rbind, path.list)
viz <- list(
point=ggplot()+
geom_point(aes(showSelected.i, showSelected.i,
id=paste0("point", showSelected.i),
clickSelects=showSelected.i),
size=10,
data=point),
transition=ggplot()+
ggtitle("should have animated transition")+
geom_path(aes(x, y, group=group.i, color=group.i,
key=group.i,
showSelected=showSelected.i),
data=path),
noTransition=ggplot()+
ggtitle("should NOT have animated transition")+
geom_path(aes(x, y, group=group.i, color=group.i,
key=paste(group.i, showSelected.i),
showSelected=showSelected.i),
data=path),
first=list(showSelected.i="1"),
duration=list(showSelected.i=3000))
info <- animint2HTML(viz)
getD <- function(html=getHTML()){
node.list <- getNodeSet(html, '//g[@class="PANEL1"]//path')
node.mat <- sapply(node.list, xmlAttrs)
node.mat["d",]
}
test_that("transitions only for equivalent keys", {
d.before <- getD()
clickID("point2")
Sys.sleep(1)
d.during <- getD()
Sys.sleep(3)
d.after <- getD()
expect_identical(d.before == d.during, c(FALSE, FALSE, FALSE, FALSE))
expect_identical(d.during == d.after, c(FALSE, FALSE, TRUE, TRUE))
}) |
varNhat <- function(data, model){
grps <- attr(data, "groups")
grps$.rows <- NULL
grps <- apply(grps, 1, function(x) any(is.na(x)))
vardat_str <- attr(data, "groups")[!grps, , drop=FALSE]
ind <- vardat_str$.rows
area <- rep(NA, length(ind))
covered_area <- rep(NA, length(ind))
for(i in 1:length(ind)){
idata <- data[ind[[i]], ]
area[i] <- idata$Area[1]
covered_area[i] <- sum(idata$Covered_area[!duplicated(idata$Sample.Label)])
}
dhtd <- function(par, data, model, area, covered_area, ind){
res <- rep(NA, length(ind))
model$par <- par
data$p <- predict(model, newdata=as.data.frame(data), integrate=TRUE,
compute=TRUE)$fitted
data$Nc <- (data$size/data$p)/data$rate
for(i in 1:length(ind)){
res[i] <- (area[i]/covered_area[i]) * sum(data$Nc[ind[[i]]], na.rm=TRUE)
}
res
}
vcov <- solvecov(model$hessian)$inv
data <- data[!is.na(data$object), ]
dm <- DeltaMethod(model$par, dhtd, vcov, sqrt(.Machine$double.eps),
model=model, data=data, area=area,
covered_area=covered_area, ind=ind)
attr(dm, "vardat_str") <- vardat_str
ret <- list(Nhat=dm)
attr(ret, "vardat_str") <- vardat_str
return(ret)
} |
geom_polypath <- function (mapping = NULL, data = NULL, stat = "identity", position = "identity",
na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, rule = "winding", ...) {
ggplot2::layer(data = data, mapping = mapping, stat = stat, geom = GeomPolypath,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm , rule = rule, ...))
}
GeomPolypath <- ggproto(
"GeomPolypath",
GeomPolygon,
extra_params = c("na.rm", "rule"),
draw_panel = function(data, scales, coordinates, rule) {
n <- nrow(data)
if (n == 1)
return(zeroGrob())
munched <- coord_munch(coordinates, data, scales)
munched <- munched[order(munched$group), ]
object_munch <- function(xmunch) {
first_idx <- !duplicated(xmunch$group)
first_rows <- xmunch[first_idx, ]
grid::pathGrob(xmunch$x, xmunch$y, default.units = "native",
id = xmunch$group, rule = rule,
gp = grid::gpar(col = first_rows$colour,
fill = alpha(first_rows$fill, first_rows$alpha),
lwd = first_rows$size * .pt,
lty = first_rows$linetype))
}
ggplot2:::ggname(
"geom_holygon",
do.call(grid::grobTree, lapply(split(munched, munched$fill), object_munch))
)
}
) |
test_that("get_translation", {
testthat::expect_error(get_translation(local_path = ".", language = "german"), NA)
pdfs <- list.files(path = ".", pattern = "*.pdf", full.names = TRUE)
purrr::map(pdfs, fs::file_delete)
}) |
logLik.loglm <- function(object, ..., zero=1E-10) {
fr <- if(!is.null(object$frequencies)) unclass(object$frequencies) else {
unclass(update(object, keep.frequencies = TRUE)$frequencies)
}
df <- prod(dim(fr)) - object$df
if (any(fr==0)) {
fr <- as.vector(fr)
fr[fr==0] <- zero
}
structure(sum((log(fr) - 1) * fr - lgamma(fr + 1)) - object$deviance/2,
df = df, class = "logLik")
} |
summary.msq.itemfit <- function( object, file=NULL, ... )
{
tam_osink( file=file)
sdisplay <- tam_summary_display()
cat(sdisplay)
tam_print_package_rsession(pack="TAM")
tam_print_computation_time(object=object)
cat("MSQ item fit statitics (Function 'msq.itemfit')\n")
tam_print_call(object$CALL)
sdisplay2 <- tam_summary_display("*", 52)
cat(sdisplay2)
cat("\nSummary outfit and infit statistic\n")
obji <- object$summary_itemfit
tam_round_data_frame_print(obji=obji, from=2, digits=3, rownames_null=TRUE)
cat("\n")
cat(sdisplay2)
cat("\nOutfit and infit statistic\n")
obji <- object$itemfit
ind <- grep( "fitgroup", colnames(obji) )
tam_round_data_frame_print(obji=obji, from=ind+1, digits=3, rownames_null=FALSE)
tam_csink(file=file)
} |
`NoOp` <-
function(x) x |
lwq_multi <- function(answering_department, peer_id,
start_date, end_date, extra_args, verbose) {
if (is.null(peer_id) == TRUE) {
mp_id_list <- NA
} else {
mp_id_list <- as.list(peer_id)
}
if (is.null(answering_department) == TRUE) {
dep_list <- NA
} else {
dep_list <- as.list(answering_department)
}
search_grid <- expand.grid(dep_list, mp_id_list, stringsAsFactors = FALSE)
names(search_grid)[names(search_grid) == "Var1"] <- "department"
names(search_grid)[names(search_grid) == "Var2"] <- "member"
dat <- vector("list", nrow(search_grid))
seq_list <- seq(from = 1, to = nrow(search_grid), by = 1)
for (i in seq_along(seq_list)) {
dat[[i]] <- hansard::lords_written_questions(
answering_department = search_grid$department[[i]],
peer_id = search_grid$member[[i]],
end_date = end_date,
start_date = start_date,
extra_args = extra_args,
verbose = verbose,
tidy = FALSE
)
}
dat <- dat[sapply(dat, function(d) is.null(d) == FALSE)]
df <- dplyr::bind_rows(dat)
names(df)[names(df) == "_about"] <- "about"
df
}
lwq_tidy <- function(df, tidy_style) {
if (nrow(df) > 0) {
df$dateTabled._value <- as.POSIXct(df$dateTabled._value)
df$AnswerDate._value <- as.POSIXct(df$AnswerDate._value)
df$dateTabled._datatype <- "POSIXct"
df$AnswerDate._value <- "POSIXct"
df$AnsweringBody <- unlist(df$AnsweringBody)
df$tablingMemberPrinted <- unlist(df$tablingMemberPrinted)
df$tablingMember._about <- gsub(
"http://data.parliament.uk/members/", "",
df$tablingMember._about
)
}
df <- hansard_tidy(df, tidy_style)
df
}
lords_division_tidy <- function(df, division_id, summary, tidy_style) {
if (nrow(df) > 0) {
if (is.null(division_id) == TRUE) {
df$date._datatype <- "POSIXct"
df$date._value <- as.POSIXct(df$date._value)
} else {
if (summary == FALSE) {
df <- ldsum_tidy(df, tidy_style)
}
}
}
df <- hansard_tidy(df, tidy_style)
df
}
ldsum_tidy <- function(df, tidy_style) {
if (nrow(df) > 0) {
df$date._value <- as.POSIXct(df$date._value)
df$date._datatype <- "POSIXct"
df$vote.type <- gsub(
"http://data.parliament.uk/schema/parl
df$vote.type
)
df$vote.type <- gsub(
"([[:lower:]])([[:upper:]])", "$1_$2",
df$vote.type
)
df$vote.member <- unlist(df$vote.member)
df$vote.member <- gsub(
"http://data.parliament.uk/resources/members/api/lords/id/", "",
df$vote.member
)
names(df) <- snakecase::to_any_case(names(df), case = tidy_style)
}
df
}
lord_vote_record_tidy <- function(df, tidy_style) {
if (nrow(df) > 0) {
df$vote <- as.factor(df$vote)
df$date._datatype <- as.factor(df$date._datatype)
df$date._value <- as.POSIXct(df$date._value)
df$date._datatype <- "POSIXct"
}
df <- hansard_tidy(df, tidy_style)
df
}
lords_amendments_tidy <- function(df, tidy_style) {
if (nrow(df) > 0) {
df$bill.date._value <- as.POSIXct(df$bill.date._value)
df$bill.date._datatype <- "POSIXct"
}
df <- hansard_tidy(df, tidy_style)
df
}
lords_attendance_tidy <- function(df, tidy_style) {
if (nrow(df) > 0) {
df$date._value <- as.POSIXct(df$date._value)
df$date._datatype <- "POSIXct"
}
df <- hansard_tidy(df, tidy_style)
df$about <- gsub("http://data.parliament.uk/resources/", "", df$about)
df
}
lords_interests_tidy <- function(df, tidy_style) {
if (nrow(df) > 0) {
if ("amendedDate" %in% colnames(df)) {
seq_list <- seq(from = 1, to = nrow(df), by = 1)
for (i in seq_along(seq_list)) {
if (is.null(df$amendedDate[[i]]) == FALSE) {
df$amendedDate[[i]] <- df$amendedDate[[i]][["_value"]]
}
}
df$amendedDate[df$amendedDate == "NULL"] <- NA
df$amendedDate <- do.call("c", df$amendedDate)
df$amendedDate <- as.POSIXct(df$amendedDate)
}
df$date._value <- as.POSIXct(df$date._value)
df$date._datatype <- "POSIXct"
df$registeredLate._value <- as.logical(df$registeredLate._value)
df$registeredLate._datatype <- "Logical"
names(df)[names(df) == "X_about"] <- "registeredInterestNumber"
names(df)[names(df) == "_about"] <- "registeredInterestNumber"
df$registeredInterestNumber <- gsub(
".*registeredinterest/", "",
df$registeredInterestNumber
)
}
df <- hansard_tidy(df, tidy_style)
df
}
lords_interests_tidy2 <- function(df, tidy_style) {
if (nrow(df) > 0) {
seq_list <- seq(from = 1, to = nrow(df), by = 1)
for (i in seq_along(seq_list)) {
df[i, ]$hasRegisteredInterest[[1]] <- as.data.frame(
df[i, ]$hasRegisteredInterest
)
}
seq_list <- seq(from = 1, to = nrow(df), by = 1)
for (i in seq_along(seq_list)) {
if ("amendedDate" %in% colnames(df[i, ]$hasRegisteredInterest[[1]])) {
seq_list2 <- seq(
from = 1,
to = nrow(df[i, ]$hasRegisteredInterest[[1]]),
by = 1
)
for (x in seq_along(seq_list2)) {
if (is.null(
df[i, ]$hasRegisteredInterest[[1]]$amendedDate[[x]]
) == FALSE) {
}
}
df[i, ]$hasRegisteredInterest[[1]]$amendedDate[
df[i, ]$hasRegisteredInterest[[1]]$amendedDate == "NULL"
] <- NA
df[i, ]$hasRegisteredInterest[[1]]$amendedDate <- do.call(
"c", df[i, ]$hasRegisteredInterest[[1]]$amendedDate
)
df[i, ]$hasRegisteredInterest[[1]]$amendedDate <- as.POSIXct(
df[i, ]$hasRegisteredInterest[[1]]$amendedDate,
na.omit = TRUE
)
}
df[i, ]$hasRegisteredInterest[[1]]$date._value <- as.POSIXct(
df[i, ]$hasRegisteredInterest[[1]]$date._value,
na.omit = TRUE
)
df[i, ]$hasRegisteredInterest[[1]]$date._datatype <- "POSIXct"
df[i, ]$hasRegisteredInterest[[1]]$registeredLate._value <- as.logical(
df[i, ]$hasRegisteredInterest[[1]]$registeredLate._value
)
df[i, ]$hasRegisteredInterest[[1]]$registeredLate._datatype <- "Logical"
names(df[i, ]$hasRegisteredInterest[[1]])[
names(df[i, ]$hasRegisteredInterest[[1]]) == "X_about"
] <-
"registeredInterestNumber"
names(df[i, ]$hasRegisteredInterest[[1]])[names(
df[i, ]$hasRegisteredInterest[[1]]
) == "_about"] <-
"registeredInterestNumber"
df[i, ]$hasRegisteredInterest[[1]]$registeredInterestNumber <-
gsub(
".*registeredinterest/", "",
df[i, ]$hasRegisteredInterest[[1]]$registeredInterestNumber
)
df[i, ]$hasRegisteredInterest[[1]] <- hansard_tidy(
df[i, ]$hasRegisteredInterest[[1]], tidy_style
)
}
}
df <- hansard_tidy(df, tidy_style)
df
} |
gradtrmf <- function(model_switch,aX,groupsize,ni,xt,x,a,bpop,d,sigma,docc,poped.db,gradxt=FALSE){
m=size(ni,1)
if (gradxt == FALSE) {
gdmf=matrix(1,m,size(a,2))
} else {
gdmf=matrix(1,m,size(xt,2))
}
iParallelN = (poped.db$settings$parallel$bParallelSG==1) + 1
if((iParallelN == 2)){
designsin = cell(1,0)
it=1
}
for(p in 1:iParallelN){
if((p==2)){
stop("Parallel execution not yet implemented in PopED for R")
designout = designsin
}
if((iParallelN==1)){
returnArgs <- mftot(model_switch,groupsize,ni,xt,x,a,bpop,d,sigma,docc,poped.db)
mft <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
} else {
if((p==1)){
designsin = update_designinlist(designsin,groupsize,ni,xt,x,a,-1,0)
} else {
mft = designout[[it]]$FIM
it = it+1
}
}
if((iParallelN==1 || p==2)){
if(all(size(poped.db$settings$prior_fim) == size(mft))){
mft = mft + poped.db$settings$prior_fim
}
imft=inv(mft)
if((is.infinite(imft[1,1]))){
imft = zeros(size(mft))
}
}
for(i in 1:m){
if((groupsize[i]==0)){
gdmf[i,1:ni[i]]=zeros(1,ni(i))
} else {
a0 = a
xt0 = xt
nCtl = ifelse(gradxt==FALSE, size(poped.db$design$a,2), ni[i])
for(ct1 in 1:nCtl){
if((aX[i,ct1]!=0)){
if (gradxt==FALSE) {
a=a0
a[i,ct1]=a[i,ct1]+poped.db$settings$hgd
} else {
xt=xt0
xt[i,ct1]=xt[i,ct1]+poped.db$settings$hgd
}
if((!isempty(x))){
x_i = t(x[i,,drop=F])
} else {
x_i = zeros(0,1)
}
if((!isempty(a))){
a_i = t(a[i,,drop=F])
} else {
a_i = zeros(0,1)
}
if((iParallelN ==1)){
returnArgs <- mf_all(t(model_switch[i,1:ni[i,drop=F],drop=F]),t(xt[i,1:ni[i,drop=F],drop=F]),x_i,a_i,bpop,d,sigma,docc,poped.db)
mf_tmp <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
} else {
if((p==1)){
designsin = update_designinlist(designsin,1,ni,xt,x,a,-1,i)
} else {
mf_tmp = designout[[it]]$FIM
it = it+1
}
}
if((iParallelN ==1 || p==2)){
mf_plus = groupsize[i]*mf_tmp
if((size(poped.db$settings$prior_fim)==size(mf_plus))){
mf_plus = mf_plus + poped.db$settings$prior_fim
}
imf_plus=inv(mf_plus)
ir=(imf_plus-imft)/poped.db$settings$hgd
if((trace_matrix(ir)!=0)){
gdmf[i,ct1]=-trace_matrix(ir)
} else {
gdmf[i,ct1]=1e-12
}
}
}
}
}
}
}
return(list( gdmf= gdmf,poped.db=poped.db))
}
|
.parmaspec = function(scenario = NULL, probability = NULL, S = NULL, Q = NULL,
qB = NULL, benchmark = NULL, benchmarkS = NULL, forecast = NULL, target = NULL,
targetType = c("inequality", "equality"),
risk = c("MAD", "MiniMax", "CVaR", "CDaR", "EV", "LPM", "LPMUPM"),
riskType = c("minrisk", "optimal", "maxreward"), riskB = NULL,
options = list(alpha = 0.05, threshold = 999, moment = 1,
lmoment = 1, umoment = 1, lthreshold = -0.01, uthreshold = 0.01),
LB = NULL, UB = NULL, budget = 1, leverage = NULL,
ineqfun = NULL, ineqgrad = NULL, eqfun = NULL, eqgrad = NULL,
uservars = list(), ineq.mat = NULL, ineq.LB = NULL,
ineq.UB = NULL, eq.mat = NULL, eqB = NULL, max.pos = NULL,
asset.names = NULL, ...){
hasminlp = 0
hasqcqp = 0
hasmiqp = 0
indx = rep(0, 8)
names(indx) = c("datatype", "benchmark", "targettype", "risk", "risktype", "leverage", "aux1", "aux2")
type = rep(0, 8)
if(is.null(scenario) && is.null(S)) stop("\nparma: You cannot have both the scenario AND covariance matrix (S) NULL!")
if(!is.null(scenario)){
if(tolower(riskType)=="maxreward") stop("\nparma: maxreward type only supported for covariance matrix (S) at present.")
S = NULL
scenario = as.matrix(scenario)
indx[1] = 1
m = NCOL(scenario)
n = NROW(scenario)
indx[8] = m
if(is.null(asset.names)) asset.names = colnames(scenario)
if(is.null(forecast)){
if(!is.null(benchmark)){
warning("\nparma: no forecast provided but benchmark is included...using means from scenario - benchmark instead")
forecast = as.numeric( colMeans(scenario) - mean(as.vector(benchmark)) )
} else{
forecast = as.numeric( colMeans(scenario) )
warning("\nparma: no forecast provided...using means from scenario instead.")
}
} else{
forecast = as.numeric(forecast)[1:m]
}
if(!is.null(benchmark)){
if(length(benchmark)<n){
benchmark = rep(benchmark[1], n)
} else{
nb = length(as.numeric(benchmark))
if(nb!=n) stop("\nparma: benchmark length not equal to scenario length.")
benchmark = as.numeric(benchmark[1:n])
}
indx[2] = 1
} else{
benchmark = rep(0, n)
}
if(is.null(target)){
target = 0
if(tolower(riskType[1]) == "minrisk") warning("\nparma: no target provided...setting target reward to zero.")
} else{
target = as.numeric(target)[1]
}
if(is.null(probability)){
probability = rep(1/n, n)
} else{
probability = probability[1:n]
if(sum(probability)!=1) warning("\nProbability does not sum to 1!")
}
xtmp = match(tolower(riskType[1]), c("minrisk", "optimal"))
if(is.na(xtmp)) stop("\nparma: riskType not recognized") else riskType = c("minrisk", "optimal")[xtmp]
indx[5] = xtmp
tmp = match(tolower(targetType[1]), c("inequality", "equality"))
if(is.na(tmp)) stop("\nparma: targetType not recognized") else targetType = c("inequality", "equality")[tmp]
indx[3] = tmp
tmp = match(tolower(risk[1]), tolower(c("MAD", "MiniMax", "CVaR", "CDaR", "EV", "LPM", "LPMUPM")))
if(is.na(tmp)) stop("\nparma: risk not recognized") else risk = c("MAD", "MiniMax", "CVaR", "CDaR", "EV", "LPM", "LPMUPM")[tmp]
indx[4] = tmp
if(!is.null(leverage)) indx[6] = as.numeric( leverage )
if(!is.null(ineqfun) && (!is.null(ineq.mat) | !is.null(eq.mat))){
stop("\nparma: you cannot mix ineqfun with linear custom constraints")
}
if(!is.null(eqfun) && (!is.null(ineq.mat) | !is.null(eq.mat))){
stop("\nparma: you cannot mix eqfun with linear custom constraints")
}
if(!is.null(leverage) && (!is.null(ineq.mat) | !is.null(eq.mat))){
stop("\nparma: you cannot mix leverage (NLP) with linear custom constraints")
}
if(!is.null(ineqfun)){
chk = .checkconsfun(ineqfun, name = "ineqfun")
}
if(!is.null(ineqgrad)){
chk = .checkconsfun(ineqgrad, name = "ineqgrad")
}
if(!is.null(eqfun)){
chk = .checkconsfun(eqfun, name = "eqfun")
}
if(!is.null(eqgrad)){
chk = .checkconsfun(eqgrad, name = "eqgrad")
}
midx = widx = vidx = NA
if(tmp == 1){
widx = 1:m
if(riskType == "optimal") midx=m+1
if(!is.null(max.pos)){
if(riskType == "minrisk"){
type[c(2, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[2] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[8] = 0
}
if(hasminlp && type[2]>0) type[7] = 1
} else{
if(!is.null(ineq.mat) | !is.null(eq.mat))
stop("\nparma: you cannot have ineq.mat or eq.mat (LP constraints) with cardinality constraints in optimal risk case (MINLP/GNLP problem)!")
if(hasminlp) type[c(7,8)] = 1 else type[8] = 1
}
} else{
type[c(1, 6, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[1] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[c(6,8)] = 0
}
}
}
if(tmp == 2){
if(riskType=="optimal"){
vidx = 1
widx = 2:(m+1)
midx = m+2
} else{
vidx = 1
widx = 2:(m+1)
midx = 0
}
if(!is.null(max.pos)){
if(riskType == "minrisk"){
type[c(2, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[2] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[8] = 0
}
if(hasminlp && type[2]>0) type[7] = 1
} else{
if(!is.null(ineq.mat) | !is.null(eq.mat))
stop("\nparma: you cannot have ineq.mat or eq.mat (LP constraints) with cardinality constraints in optimal risk case (MINLP/GNLP problem)!")
if(hasminlp) type[c(7,8)] = 1 else type[8] = 1
}
} else{
type[c(1, 6, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[1] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[c(6,8)] = 0
}
}
}
if(tmp == 3){
widx = 2:(m+1)
vidx = 1
if(riskType == "optimal") midx=m+2
if(!is.null(max.pos)){
if(riskType == "minrisk"){
type[c(2, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[2] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[8] = 0
}
if(hasminlp && type[2]>0) type[7] = 1
} else{
if(!is.null(ineq.mat) | !is.null(eq.mat))
stop("\nparma: you cannot have ineq.mat or eq.mat (LP constraints) with cardinality constraints in optimal risk case (MINLP/GNLP problem)!")
if(hasminlp) type[c(7,8)] = 1 else type[8] = 1
}
} else{
type[c(1, 6, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[1] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[c(6,8)] = 0
}
}
}
if(tmp == 4){
widx = 2:(m+1)
vidx = 1
if(riskType == "optimal") midx=m+2
if(!is.null(max.pos)){
if(riskType == "minrisk") type[2] = 1 else stop("\nparma: CDaR with cardinality constraints and optimal risk type not supported.")
} else{
type[1] = 1
}
if(!is.null(leverage)) stop("\nparma: CDaR with leverage requires NLP formulation (not supported)")
if(!is.null(ineqfun)) stop("\nparma: CDaR with custom NLP (ineqfun) constraints requires NLP formulation (not supported)")
if(!is.null(eqfun)) stop("\nparma: CDaR with custom NLP (eqfun) constraints requires NLP formulation (not supported)")
}
if(tmp == 5){
widx = 1:m
if(!is.null(max.pos)){
if(hasminlp) type[c(7,8)] = 1 else type[8] = 1
} else{
type[c(6, 8)] = 1
}
if(riskType == "optimal") midx = m+1
if(!is.null(ineq.mat) | !is.null(eq.mat)){
stop("\nparma: EV scenario problem is NLP. Does not accept linear constraints (use QP with S matrix instead).")
}
}
if(tmp == 6){
benchmark = 0
widx = 1:m
if(riskType == "optimal") midx=m+1
if(options$moment == 1){
if(!is.null(max.pos)){
if(riskType == "minrisk"){
type[c(2, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[2] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[8] = 0
}
if(hasminlp && type[2]>0) type[7] = 1
} else{
if(!is.null(ineq.mat) | !is.null(eq.mat))
stop("\nparma: you cannot have ineq.mat or eq.mat (LP constraints) with cardinality constraints in optimal risk case (MINLP/GNLP problem)!")
if(hasminlp) type[c(7,8)] = 1 else type[8] = 1
}
} else{
type[c(1, 6, 8)] = 1
if(!is.null(leverage) | !is.null(ineqfun) | !is.null(eqfun)){
type[1] = 0
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
type[c(6,8)] = 0
}
}
} else{
if(!is.null(max.pos)){
if(hasminlp) type[c(7,8)]<-1 else type[8]<-1
} else{
type[c(6, 8)] = 1
}
if(!is.null(ineq.mat) | !is.null(eq.mat)){
stop("\nparma: LPM scenario problem with moment!=1 is NLP. Does not accept linear constraints.")
}
}
}
if(tmp == 7){
widx = 1:m
midx = m+1
type[8]<-1
if(!is.null(ineq.mat) | !is.null(eq.mat)){
stop("\nparma: LPMUPM is GNLP. Does not accept linear constraints.")
}
riskType = "optimal"
indx[5] = 2
}
if(is.null(LB)){
LB = rep(0, m)
warning("\nparma: no LB provided...setting Lower Bounds to zero.")
}
if(is.null(UB)){
UB = rep(1, m)
warning("\nparma: no UB provided...setting Upper Bounds to 1.")
}
if(any(UB<LB)) stop("\nparma: UB must be greater than LB.")
if(is.null(leverage)){
if(is.null(budget)){
budget = 1
warning("\nparma: no budget (or leverage) provided...setting budget constraint to 1.")
}
} else{
indx[6] = 1
}
if(!is.null(ineq.mat)){
ineq.mat = as.matrix(ineq.mat)
nb = dim(ineq.mat)[2]
if(nb!=m) stop("\nparma: ineq.mat columns not equal to number of assets")
nb = dim(ineq.mat)[1]
if(nb!=length(ineq.LB)) stop("\nparma: ineq.mat rows not equal to length of ineq.LB")
if(nb!=length(ineq.UB)) stop("\nparma: ineq.mat rows not equal to length of ineq.UB")
}
if(!is.null(eq.mat)){
ineq.mat = as.matrix(eq.mat)
nb = dim(eq.mat)[2]
if(nb!=m) stop("\nparma: eq.mat columns not equal to number of assets")
nb = dim(eq.mat)[1]
if(nb!=length(eqB)) stop("\nparma: eq.mat rows not equal to length of eqB")
}
if(!is.null(ineqfun)){
if(is.null(ineqgrad)){
type = rep(0,8)
type[8] = 1
}
}
if(!is.null(eqfun)){
if(is.null(eqgrad)){
type = rep(0,8)
type[8] = 1
}
}
} else{
m = NCOL(S)
benchmark = ineqfun = eqfun = ineqgrad = eqgrad = NULL
midx = 1
vidx = 0
widx = 2:(m+1)
if( !is.null(max.pos) ){
if(hasmiqp){
type[4] = 1
} else{
stop("\nparma: max.pos NOT NULL but MIQP not available!")
}
} else{
if(!is.null(Q)){
type[5] = 1
if(riskType == "optimal") stop("\nparma: QCQP not yet implemented for optimal risk problem")
} else{
type[3] = 1
type[5] = 1
}
}
if(!is.null(leverage)){
if(leverage<=0) stop("\nparma: leverage must be strictly positive!")
type[3] = 0
type[5] = 1
indx[6] = as.numeric( leverage )
}
if(tolower(riskType)=="maxreward"){
type[3] = 0
type[5] = 1
}
if(riskType == "optimal") midx=1
if(tolower(risk[1])!="ev") stop("\nparma: only EV risk type allowed with covariance matrix (S)")
indx[1] = 2
indx[8] = m
if(is.null(asset.names)) asset.names = colnames(S)
if(!is.null(benchmarkS)){
benchmarkS = matrix(benchmarkS, nrow = 1)
indx[2] = 1
nb = NCOL(benchmarkS)
if((nb-1)!=m) stop("\nparma: benchmarkS length must be equal to ncol S + 1.")
} else{
benchmarkS = matrix(0, nrow = 1, ncol = m+1)
}
if(is.null(forecast)){
forecast = rep(0, m)
warning("\nparma: no forecast provided...setting to zero.")
} else{
forecast = as.numeric(forecast)[1:m]
}
if(is.null(target)){
target = 0
if(tolower(riskType[1]) == "minrisk"){
warning("\nparma: no target provided...setting target reward to zero.")
indx[5] = 1
} else if(tolower(riskType[1])=="optimal"){
indx[5] = 2
} else{
indx[5] = 3
if(is.null(riskB)) stop("\nparma: maxreward option chosen but riskB not provided!")
riskB = as.numeric(riskB[1])
}
} else{
target = as.numeric(target)[1]
if(tolower(riskType[1]) == "minrisk"){
indx[5] = 1
} else if(tolower(riskType[1])=="optimal"){
indx[5] = 2
} else{
indx[5] = 3
if(is.null(riskB)) stop("\nparma: maxreward option chosen but riskB not provided!")
riskB = as.numeric(riskB[1])
target = NULL
warning("\nparma: maxreward chosen AND target given...setting target to NULL.")
}
}
tmp = match(tolower(targetType[1]), c("inequality", "equality"))
if(is.na(tmp)) stop("\nparma: targetType not recognized") else targetType = c("inequality", "equality")[tmp]
indx[3] = tmp
if(is.null(LB)){
LB = rep(0, m)
warning("\nparma: no LB provided...setting Lower Bounds to zero.")
}
if(is.null(UB)){
UB = rep(1, m)
warning("\nparma: no UB provided...setting Upper Bounds to 1.")
}
if(any(UB<LB)) stop("\nparma: UB must be greater than LB.")
if(is.null(leverage)){
if(is.null(budget)){
budget = 1
warning("\nparma: no budget (or leverage) provided...setting budget constraint to 1.")
}
}
if(!is.null(ineq.mat)){
ineq.mat = as.matrix(ineq.mat)
nb = dim(ineq.mat)[2]
if(nb!=m) stop("\nparma: ineq.mat columns not equal to number of assets")
nb = dim(ineq.mat)[1]
if(nb!=length(ineq.LB)) stop("\nparma: ineq.mat rows not equal to length of ineq.LB")
if(nb!=length(ineq.UB)) stop("\nparma: ineq.mat rows not equal to length of ineq.UB")
}
if(!is.null(eq.mat)){
eq.mat = as.matrix(eq.mat)
nb = dim(eq.mat)[2]
if(nb!=m) stop("\nparma: eq.mat columns not equal to number of assets")
nb = dim(eq.mat)[1]
if(nb!=length(eqB)) stop("\nparma: eq.mat rows not equal to length of eqB")
}
}
model = list(indx = indx, risk = risk, riskType = riskType, targetType = targetType,
options = options, type = type, widx = widx, midx = midx, vidx = vidx)
modeldata = list(scenario = scenario, probability = probability, S = S, Q = Q,
qB = qB, benchmark = benchmark, benchmarkS = benchmarkS, forecast = forecast,
target = target, riskB = riskB, asset.names = asset.names, uservars = uservars)
constraints = list(LB = LB, UB = UB, budget = budget, leverage = leverage,
ineqfun = ineqfun, ineqgrad = ineqgrad, eqfun = eqfun, eqgrad = eqgrad,
ineq.mat = ineq.mat, ineq.LB = ineq.LB, ineq.UB = ineq.UB,
eq.mat = eq.mat, eqB = eqB, uservars = uservars, max.pos = max.pos)
ans = new("parmaSpec",
model = model,
modeldata = modeldata,
constraints = constraints)
return(ans)
}
.spec2minNLP = function(spec){
optvars = list()
optvars$widx = spec@model$widx
optvars$midx = spec@model$midx
optvars$vidx = spec@model$vidx
optvars$index = spec@model$indx
if(spec@model$indx[2]==0){
optvars$Data = switch(tolower(spec@model$risk),
"mad" = scale(spec@modeldata$scenario, scale = FALSE),
"ev" = scale(spec@modeldata$scenario, scale = FALSE),
"minimax" = spec@modeldata$scenario,
"cvar" = spec@modeldata$scenario,
"cdar" = spec@modeldata$scenario,
"lpm" = spec@modeldata$scenario,
spec@modeldata$scenario)
} else{
optvars$Data = spec@modeldata$scenario
}
optvars$benchmark = spec@modeldata$benchmark
optvars$mu = spec@modeldata$forecast
optvars$mutarget = spec@modeldata$target
risk = spec@model$risk
mn = dim(optvars$Data)
N = mn[1]
m = mn[2]
optvars$wm = m
optvars$N = N
if(tolower(risk) == "lpm"){
if(spec@model$options$threshold == 999){
optvars$Data = scale(optvars$Data, scale = FALSE)
optvars$threshold = 0
} else{
optvars$threshold = spec@model$options$threshold
}
} else{
optvars$threshold = 0
}
optvars$moment = spec@model$options$moment
if(tolower(risk) == "cvar"){
optvars$fm = m+1
x0 = as.numeric(c(quantile(optvars$Data %*% rep(1/m, m), spec@model$options$alpha), rep(1/m, m)))
optvars$LB = c(-10, spec@constraints$LB)
optvars$UB = c( 0, spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else if(tolower(risk) == "minimax"){
optvars$fm = m+1
x0 = as.numeric(c(-min(optvars$Data %*% rep(1/m, m)), rep(1/m, m)))
optvars$LB = c( 0, spec@constraints$LB)
optvars$UB = c( 1, spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else{
optvars$fm = m
x0 = rep(1/m, m)
optvars$LB = spec@constraints$LB
optvars$UB = spec@constraints$UB
optvars$alpha = 0.05
}
optvars$budget = spec@constraints$budget
optvars$leverage = spec@constraints$leverage
optvars$ineqfun = spec@constraints$ineqfun
optvars$ineqgrad = spec@constraints$ineqgrad
optvars$eqfun = spec@constraints$eqfun
optvars$eqgrad = spec@constraints$eqgrad
optvars$x0 = x0
return(optvars)
}
.spec2optNLP = function(spec, pcontrol = list(ubounds = 1e4,
mbounds = 1e5, penalty = 1e4, startp = 150)){
optvars = list()
optvars$widx = spec@model$widx
optvars$midx = spec@model$midx
optvars$vidx = spec@model$vidx
optvars$index = spec@model$indx
if(spec@model$indx[2]==0){
optvars$Data = switch(tolower(spec@model$risk),
"mad" = scale(spec@modeldata$scenario, scale = FALSE),
"ev" = scale(spec@modeldata$scenario, scale = FALSE),
"minimax" = spec@modeldata$scenario,
"cvar" = spec@modeldata$scenario,
"cdar" = spec@modeldata$scenario,
"lpm" = spec@modeldata$scenario,
spec@modeldata$scenario)
} else{
optvars$Data = spec@modeldata$scenario
}
optvars$benchmark = spec@modeldata$benchmark
optvars$mu = spec@modeldata$forecast
optvars$mutarget = spec@modeldata$target
risk = spec@model$risk
mn = dim(optvars$Data)
N = mn[1]
m = mn[2]
optvars$wm = m
optvars$N = N
if(tolower(risk) == "lpm"){
if(spec@model$options$threshold == 999){
optvars$Data = scale(optvars$Data, scale = FALSE)
optvars$threshold = 0
} else{
optvars$threshold = spec@model$options$threshold
}
} else{
optvars$threshold = 0
}
if(tolower(risk) == "lpmupm"){
if(spec@model$options$lthreshold == 999 || spec@model$options$uthreshold == 999){
optvars$Data = scale(optvars$Data, scale = FALSE)
optvars$lthreshold = 0
optvars$uthreshold = 0
} else{
optvars$lthreshold = spec@model$options$lthreshold
optvars$uthreshold = spec@model$options$uthreshold
if(optvars$lthreshold>optvars$uthreshold) stop("\nparma: lower threshold higher than upper threshold in LPMUPM!")
}
optvars$lmoment = spec@model$options$lmoment
optvars$umoment = spec@model$options$umoment
} else{
optvars$lthreshold = 0
optvars$uthreshold = 0
optvars$umoment = 1
optvars$lmoment = 1
}
optvars$moment = spec@model$options$moment
if(tolower(risk) == "cvar"){
optvars$fm = m+2
x0 = as.numeric(c(5*quantile(optvars$Data %*% rep(1/m, m), spec@model$options$alpha), 5*rep(1/m, m), 5))
optvars$fLB = c( -pcontrol$ubounds, rep(-pcontrol$ubounds, m), 1e-8)
optvars$fUB = c( pcontrol$ubounds, rep( pcontrol$ubounds, m), pcontrol$mbounds)
optvars$LB = c(spec@constraints$LB)
optvars$UB = c(spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else if(tolower(risk) == "minimax"){
optvars$fm = m+2
x0 = as.numeric(c(-min(optvars$Data %*% rep(1/m, m)), rep(1/m, m), 1))
optvars$fLB = c(-pcontrol$ubounds, rep(-pcontrol$ubounds, m), 1e-8)
optvars$fUB = c( pcontrol$ubounds, rep( pcontrol$ubounds, m), pcontrol$mbounds)
optvars$LB = c(spec@constraints$LB)
optvars$UB = c(spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else{
optvars$fm = m+1
x0 = c(rep(1/m, m), 2)
optvars$fLB = c(rep(-pcontrol$ubounds, m), 1e-8)
optvars$fUB = c(rep( pcontrol$ubounds, m), pcontrol$mbounds)
optvars$LB = spec@constraints$LB
optvars$UB = spec@constraints$UB
optvars$alpha = 0.05
}
optvars$budget = spec@constraints$budget
optvars$leverage = spec@constraints$leverage
optvars$ineqfun = spec@constraints$ineqfun
optvars$ineqgrad = spec@constraints$ineqgrad
optvars$eqfun = spec@constraints$eqfun
optvars$eqgrad = spec@constraints$eqgrad
optvars$x0 = x0
return(optvars)
}
.spec2minGNLP = function(spec, pcontrol = list(ubounds = 1e4,
mbounds = 1e5, penalty = 1e4, startp = 150)){
optvars = list()
optvars$widx = spec@model$widx
optvars$midx = spec@model$midx
optvars$vidx = spec@model$vidx
optvars$index = spec@model$indx
if(spec@model$indx[2]==0){
optvars$Data = switch(tolower(spec@model$risk),
"mad" = scale(spec@modeldata$scenario, scale = FALSE),
"ev" = scale(spec@modeldata$scenario, scale = FALSE),
"minimax" = spec@modeldata$scenario,
"cvar" = spec@modeldata$scenario,
"cdar" = spec@modeldata$scenario,
"lpm" = spec@modeldata$scenario,
spec@modeldata$scenario)
} else{
optvars$Data = spec@modeldata$scenario
}
optvars$benchmark = spec@modeldata$benchmark
optvars$mu = spec@modeldata$forecast
optvars$mutarget = spec@modeldata$target
optvars$penalty = pcontrol$penalty
risk = spec@model$risk
mn = dim(optvars$Data)
N = mn[1]
m = mn[2]
optvars$wm = m
optvars$N = N
if(tolower(risk) == "lpm"){
if(spec@model$options$threshold == 999){
optvars$Data = scale(optvars$Data, scale = FALSE)
optvars$threshold = 0
} else{
optvars$threshold = spec@model$options$threshold
}
} else{
optvars$threshold = 0
}
optvars$moment = spec@model$options$moment
if(tolower(risk) == "cvar"){
optvars$fm = m+1
x0 = as.numeric(c(quantile(optvars$Data %*% rep(1/m, m), spec@model$options$alpha), rep(1/m, m)))
optvars$LB = c(-10, spec@constraints$LB)
optvars$UB = c( 0, spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else if(tolower(risk) == "minimax"){
optvars$fm = m+1
x0 = as.numeric(c(-min(optvars$Data %*% rep(1/m, m)), rep(1/m, m)))
optvars$LB = c(0, spec@constraints$LB)
optvars$UB = c(1, spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else{
optvars$fm = m
x0 = rep(1/m, m)
optvars$LB = spec@constraints$LB
optvars$UB = spec@constraints$UB
optvars$alpha = 0.05
}
optvars$budget = spec@constraints$budget
optvars$leverage = spec@constraints$leverage
optvars$ineqfun = spec@constraints$ineqfun
optvars$ineqgrad = spec@constraints$ineqgrad
optvars$eqfun = spec@constraints$eqfun
optvars$eqgrad = spec@constraints$eqgrad
optvars$x0 = x0
return(optvars)
}
.spec2optGNLP = function(spec, pcontrol = list(ubounds = 1e4,
mbounds = 1e5, penalty = 1e4, startp = 150)){
optvars = list()
optvars$widx = spec@model$widx
optvars$midx = spec@model$midx
optvars$vidx = spec@model$vidx
optvars$index = spec@model$indx
if(spec@model$indx[2]==0){
optvars$Data = switch(tolower(spec@model$risk),
"mad" = scale(spec@modeldata$scenario, scale = FALSE),
"ev" = scale(spec@modeldata$scenario, scale = FALSE),
"minimax" = spec@modeldata$scenario,
"cvar" = spec@modeldata$scenario,
"cdar" = spec@modeldata$scenario,
"lpm" = spec@modeldata$scenario,
spec@modeldata$scenario)
} else{
optvars$Data = spec@modeldata$scenario
}
optvars$benchmark = spec@modeldata$benchmark
optvars$mu = spec@modeldata$forecast
optvars$mutarget = spec@modeldata$target
penalty = pcontrol$penalty
risk = spec@model$risk
mn = dim(optvars$Data)
N = mn[1]
m = mn[2]
optvars$wm = m
optvars$N = N
if(tolower(risk) == "lpm"){
if(spec@model$options$threshold == 999){
optvars$Data = scale(optvars$Data, scale = FALSE)
optvars$threshold = 0
} else{
optvars$threshold = spec@model$options$threshold
}
} else{
optvars$threshold = 0
}
if(tolower(risk) == "lpmupm"){
if(spec@model$options$lthreshold == 999 || spec@model$options$uthreshold == 999){
optvars$Data = scale(optvars$Data, scale = FALSE)
optvars$lthreshold = 0
optvars$uthreshold = 0
} else{
optvars$lthreshold = spec@model$options$lthreshold
optvars$uthreshold = spec@model$options$uthreshold
if(optvars$lthreshold>optvars$uthreshold) stop("\nparma: lower threshold higher than upper threshold in LPMUPM!")
}
optvars$lmoment = spec@model$options$lmoment
optvars$umoment = spec@model$options$umoment
} else{
optvars$lthreshold = 0
optvars$uthreshold = 0
optvars$umoment = 1
optvars$lmoment = 1
}
optvars$moment = spec@model$options$moment
if(tolower(risk) == "cvar"){
optvars$fm = m+2
x0 = as.numeric(c(5*quantile(optvars$Data %*% rep(1/m, m), spec@model$options$alpha), 5*rep(1/m, m), 5))
optvars$fLB = c( -pcontrol$ubounds, rep(-pcontrol$ubounds, m), 1e-8)
optvars$fUB = c( pcontrol$ubounds, rep( pcontrol$ubounds, m), pcontrol$mbounds)
optvars$LB = c(spec@constraints$LB)
optvars$UB = c(spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else if(tolower(risk) == "minimax"){
optvars$fm = m+2
x0 = as.numeric(c(-min(optvars$Data %*% rep(1/m, m)), rep(1/m, m), 1))
optvars$fLB = c(-pcontrol$ubounds, rep(-pcontrol$ubounds, m), 1e-8)
optvars$fUB = c( pcontrol$ubounds, rep( pcontrol$ubounds, m), pcontrol$mbounds)
optvars$LB = c(spec@constraints$LB)
optvars$UB = c(spec@constraints$UB)
optvars$alpha = spec@model$options$alpha
} else{
optvars$fm = m+1
x0 = c(rep(1/m, m), 2)
optvars$fLB = c(rep(-pcontrol$ubounds, m), 1e-8)
optvars$fUB = c(rep( pcontrol$ubounds, m), pcontrol$mbounds)
optvars$LB = spec@constraints$LB
optvars$UB = spec@constraints$UB
optvars$alpha = 0.05
}
optvars$budget = spec@constraints$budget
optvars$leverage = spec@constraints$leverage
optvars$ineqfun = spec@constraints$ineqfun
optvars$ineqgrad = spec@constraints$ineqgrad
optvars$eqfun = spec@constraints$eqfun
optvars$eqgrad = spec@constraints$eqgrad
optvars$x0 = x0
return(optvars)
}
.spec2QP = function(spec){
optvars = list()
optvars$index = spec@model$indx
optvars$S = spec@modeldata$S
optvars$benchmarkS = spec@modeldata$benchmarkS
optvars$mu = spec@modeldata$forecast
optvars$mutarget = spec@modeldata$target
optvars$budget = spec@constraints$budget
optvars$ineq.mat = spec@constraints$ineq.mat
optvars$eq.mat = spec@constraints$eq.mat
optvars$LB = spec@constraints$LB
optvars$UB = spec@constraints$UB
optvars$ineq.LB = spec@constraints$ineq.LB
optvars$ineq.UB = spec@constraints$ineq.UB
optvars$eqB = spec@constraints$eqB
return(optvars)
}
.spec2SOCP = function(spec){
optvars = list()
optvars$index = spec@model$indx
optvars$S = spec@modeldata$S
optvars$Q = spec@modeldata$Q
optvars$qB = spec@modeldata$qB
optvars$riskB = spec@modeldata$riskB
optvars$benchmarkS = spec@modeldata$benchmarkS
optvars$mu = spec@modeldata$forecast
optvars$mutarget = spec@modeldata$target
optvars$budget = spec@constraints$budget
optvars$leverage = spec@constraints$leverage
optvars$ineq.mat = spec@constraints$ineq.mat
optvars$eq.mat = spec@constraints$eq.mat
optvars$LB = spec@constraints$LB
optvars$UB = spec@constraints$UB
optvars$ineq.LB = spec@constraints$ineq.LB
optvars$ineq.UB = spec@constraints$ineq.UB
optvars$eqB = spec@constraints$eqB
return(optvars)
}
.spec2LP = function(spec){
optvars = list()
optvars$index = spec@model$indx
if(spec@model$indx[2]==0){
optvars$Data = switch(tolower(spec@model$risk),
"mad" = scale(spec@modeldata$scenario, scale = FALSE),
"ev" = scale(spec@modeldata$scenario, scale = FALSE),
"minimax" = spec@modeldata$scenario,
"cvar" = spec@modeldata$scenario,
"cdar" = spec@modeldata$scenario,
"lpm" = spec@modeldata$scenario,
spec@modeldata$scenario)
} else{
optvars$Data = spec@modeldata$scenario
}
optvars$probability = spec@modeldata$probability
optvars$benchmark = spec@modeldata$benchmark
optvars$mu = spec@modeldata$forecast
optvars$mutarget = spec@modeldata$target
risk = spec@model$risk
mn = dim(optvars$Data)
N = mn[1]
m = mn[2]
optvars$wm = m
optvars$N = N
if(tolower(risk) == "lpm"){
if(spec@model$options$threshold == 999){
optvars$Data = scale(optvars$Data, scale = FALSE)
optvars$threshold = 0
} else{
optvars$threshold = spec@model$options$threshold
}
} else{
optvars$threshold = 0
}
optvars$moment = 1
optvars$alpha = spec@model$options$alpha
optvars$LB = spec@constraints$LB
optvars$UB = spec@constraints$UB
optvars$budget = spec@constraints$budget
optvars$ineq.mat = spec@constraints$ineq.mat
optvars$ineq.LB = spec@constraints$ineq.LB
optvars$ineq.UB = spec@constraints$ineq.UB
optvars$eq.mat = spec@constraints$eq.mat
optvars$eqB = spec@constraints$eqB
optvars$max.pos = spec@constraints$max.pos
return(optvars)
}
.parmasolve = function(spec, type = NULL, solver = NULL, solver.control = list(),
x0 = NULL, w0 = NULL, parma.control = list(ubounds = 1e4,
mbounds = 1e5, penalty = 1e4, eqSlack = 1e-5), ...){
tic = Sys.time()
if(is.null(parma.control)) parma.control = list()
mm = match(names(parma.control), c("ubounds", "mbounds", "penalty","eqSlack"))
if(any(is.na(mm))){
idx = which(is.na(mm))
enx = NULL
for(i in 1:length(idx)) enx = c(enx, parma.control[idx[i]])
warning(paste(c("unidentified option(s) in parma.control:\n", enx), sep="", collapse=" "), call. = FALSE, domain = NULL)
}
if(is.null(parma.control$ubounds)) parma.control$ubounds = 1e4 else parma.control$ubounds = parma.control$ubounds[1]
if(is.null(parma.control$mbounds)) parma.control$mbounds = 1e5 else parma.control$mbounds = parma.control$mbounds[1]
if(is.null(parma.control$penalty)) parma.control$penalty = 1e4 else parma.control$penalty = parma.control$penalty[1]
if(is.null(parma.control$eqSlack)) parma.control$eqSlack = 1e-5 else parma.control$eqSlack = parma.control$eqSlack[1]
available.problems = toupper(c("LP", "MILP", "QP", "MIQP", "SOCP", "NLP", "MINLP", "GNLP")[which(spec@model$type==1)])
if(!is.null(type[1])){
type = toupper(type[1])
tmp = match.arg(type, available.problems)
type = tmp
} else{
type = available.problems[1]
}
if(spec@model$indx[5]==1){
optvars = switch(tolower(type),
"lp" = .spec2LP(spec),
"milp" = .spec2LP(spec),
"nlp" = .spec2minNLP(spec),
"qp" = .spec2QP(spec),
"socp" = .spec2SOCP(spec),
"gnlp" = .spec2minGNLP(spec, parma.control))
} else{
optvars = switch(tolower(type),
"lp" = .spec2LP(spec),
"milp" = .spec2LP(spec),
"nlp" = .spec2optNLP(spec, parma.control),
"qp" = .spec2QP(spec),
"socp" = .spec2SOCP(spec),
"gnlp" = .spec2optGNLP(spec, parma.control))
}
uservars = spec@modeldata$uservars
if(!is.null(x0)){
if(length(optvars$x0)!=length(x0)) stop("\nparma: wrong length for x0!")
optvars$x0 = x0
}
if(!is.null(w0)){
if(length(optvars$widx)!=length(w0)) stop("\nparma: wrong length for w0!")
optvars$x0[optvars$widx] = w0
}
if(type=="LP" && is.null(solver)) solver="GLPK"
sol = switch(toupper(type),
"LP" = lpport(optvars, solver, ...),
"MILP" = milpport(optvars, solver, ...),
"NLP" = nlpport(optvars, uservars, control = solver.control, ...),
"QP" = qpport(optvars, ...),
"SOCP" = socpport(optvars, control = solver.control, eqSlack = parma.control$eqSlack, ...),
"GNLP" = gnlpport(optvars, uservars, solver = solver, control = solver.control, ...))
if(type!="QP" & type!="SOCP"){
arbitrage = .arbcheck(sol$weights, spec@modeldata$scenario, spec@model$options, spec@model$risk)
} else{
arbitrage = c(0, 0)
}
spec@model$asset.names = spec@modeldata$asset.names
spec@model$type = type
sol$solver = solver
sol$arbitrage = arbitrage
toc = Sys.time() - tic
spec@model$elapsed = toc
ret = new("parmaPort",
solution = sol,
model = spec@model)
return(ret)
}
.parmafrontier = function(spec, n.points = 100, miny = NULL, maxy = NULL,
type = NULL, solver = NULL, solver.control = list(),
parma.control = list(ubounds = 10000, mbounds = 1e+05, penalty = 10000),
cluster = NULL)
{
if(!is.null(spec@modeldata$S))
{
ans = m.parmafrontier(spec, n.points = n.points, type = type,
miny = miny, maxy = maxy, cluster = cluster)
} else {
ans = s.parmafrontier(spec, n.points = n.points, miny = miny,
maxy = maxy, type = type, solver = solver,
solver.control = solver.control,
parma.control = parma.control, cluster = cluster)
}
return(ans)
}
s.parmafrontier = function(spec, n.points = 100, miny = NULL,
maxy = NULL, type = NULL, solver = NULL, solver.control = list(),
parma.control = list(ubounds = 10000, mbounds = 1e+05, penalty = 10000),
cluster = NULL)
{
targettype = parmaget(spec, "targetType")
risktype = parmaget(spec, "riskType")
if(risktype!="minrisk") stop("\nspec riskType must be minrisk...fix and resubmit.")
if(targettype!="equality") stop("\nspec targetType must be equality...fix and resubmit.")
m = NCOL(spec@modeldata$scenario)
if(is.null(spec@modeldata$forecast)){
f = abs(apply(spec@modeldata$scenario, 2, "mean"))
minb = min(f)
maxb = max(f)
} else{
minb = min(abs(spec@modeldata$forecast))
maxb = max(abs(spec@modeldata$forecast))
}
if(is.null(miny)){
xspec = spec
parmaset(xspec)<-list(target=0)
parmaset(xspec)<-list(targetType="inequality")
solx = try(parmasolve(xspec, type = type, solver = solver,
solver.control = solver.control,
parma.control = parma.control), silent = TRUE)
if(!inherits(solx, "try-error")) minb = parmareward(solx)
} else{
minb = miny
}
if(!is.null(maxy)){
maxb = maxy
}
fs = seq(minb, maxb, length.out = n.points)
fmat = matrix(NA, ncol = m+3, nrow = n.points)
if(!is.null(cluster)){
clusterEvalQ(cluster, require(parma))
clusterExport(cluster, c("fs", "spec", "type", "solver",
"solver.control","parma.control"), envir = environment())
sol = parLapply(cluster, 1:n.points, fun = function(i){
xspec = spec
parmaset(xspec)<-list(target=fs[i])
tmp = parmasolve(xspec, type = type, solver = solver,
solver.control = solver.control,
parma.control = parma.control)
return(tmp)
})
for(i in 1:n.points){
fmat[i,1:m] = weights(sol[[i]])
fmat[i,m+1] = parmarisk(sol[[i]])
fmat[i,m+2] = parmareward(sol[[i]])
fmat[i,m+3] = parmastatus(sol[[i]])
}
} else{
for(i in 1:n.points){
xspec = spec
parmaset(xspec)<-list(target=fs[i])
tmp = parmasolve(xspec, type = type, solver = solver,
solver.control = solver.control,
parma.control = parma.control)
fmat[i,1:m] = weights(tmp)
fmat[i,m+1] = parmarisk(tmp)
fmat[i,m+2] = parmareward(tmp)
fmat[i,m+3] = parmastatus(tmp)
}
}
colnames(fmat) = c(spec@modeldata$asset.names, spec@model$risk, "reward", "status")
return(fmat)
}
m.parmafrontier = function(spec, n.points = 100, type = "QP",
solver.control = list(abs.tol = 1e-8, rel.tol = 1e-8, Nu=2, max.iter=5250,
BigM.K = 4, BigM.iter = 15), miny = NULL, maxy = NULL, cluster = NULL)
{
targettype = parmaget(spec, "targetType")
risktype = parmaget(spec, "riskType")
if(risktype!="minrisk") stop("\nspec riskType must be minrisk...fix and resubmit.")
if(targettype!="equality") stop("\nspec targetType must be equality...fix and resubmit.")
m = NCOL(spec@modeldata$S)
if(is.null(spec@modeldata$forecast)){
stop("\nparma: cannot have a NULL forecast vector in QP formulation.")
} else{
minb = min(abs(spec@modeldata$forecast))
maxb = max(abs(spec@modeldata$forecast))
}
if(is.null(miny)){
xspec = spec
parmaset(xspec)<-list(target=0)
parmaset(xspec)<-list(targetType="inequality")
solx = try(parmasolve(xspec), silent = TRUE)
if(!inherits(solx, "try-error")) minb = parmareward(solx)
} else{
minb = miny
}
if(!is.null(maxy)){
maxb = maxy
}
fs = seq(minb, maxb, length.out = n.points)
fmat = matrix(NA, ncol = m+2, nrow = n.points)
if(!is.null(cluster)){
clusterEvalQ(cluster, require(parma))
clusterExport(cluster, c("fs", "spec","type","solver.control"), envir = environment())
sol = parLapply(cluster, 1:n.points, fun = function(i){
xspec = spec
parmaset(xspec)<-list(target=fs[i])
tmp = parmasolve(xspec, type = type, solver.control = solver.control)
return(tmp)
})
for(i in 1:n.points){
fmat[i,1:m] = weights(sol[[i]])
fmat[i,m+1] = parmarisk(sol[[i]])
fmat[i,m+2] = parmareward(sol[[i]])
}
} else{
for(i in 1:n.points){
xspec = spec
parmaset(xspec)<-list(target=fs[i])
tmp = parmasolve(xspec, type = type, solver.control = solver.control)
fmat[i,1:m] = weights(tmp)
fmat[i,m+1] = parmarisk(tmp)
fmat[i,m+2] = parmareward(tmp)
}
}
colnames(fmat) = c(spec@modeldata$asset.names, spec@model$risk, "reward")
return(fmat)
}
.checkconsfun = function(fun, name = "ineqfun"){
if(!is.list(fun)) stop(paste("\n",name," must be a list of functions",sep=""))
n = length(fun)
for(i in 1:n){
if(!is.function(fun[[i]])) stop(paste("\n",name," list constains non functions at position: ",i,sep=""))
}
return(0)
} |
context('extent calculations')
test_that('extent calculations',{
data(leroy)
expect_equal(
extent(leroy),
extent(move:::.extcalc(leroy,0))
)
expect_equal(
extent(leroy)*2,
extent(move:::.extcalc(leroy,0.5))
)
expect_equal(
extent(leroy)*1:2,
extent(move:::.extcalc(leroy,c(0,0.5)))
)
})
test_that('extent calculations between bgb and dbbmm',{
data<-move(x=rep(2:3,9)/5, y=rep(2:3,9)/5, Sys.time()+1:18)
expect_message(x<-brownian.bridge.dyn(data, location.error=.1, ext=7, dimSize=50, window.size=11, margin=5),'Computa')
expect_warning(xd<-dynBGB(data, locErr=.1, ext=7, dimSize=50, windowSize=11, margin=5),'Brownian motion assumed, because no direction could be calculated')
expect_equal(
raster(xd),
raster(x)
)
expect_message(x<-brownian.bridge.dyn(data, location.error=.1, ext=7, raster=.020, window.size=11, margin=5),'Computa')
expect_warning(xd<-dynBGB(data, locErr=.1, ext=7, raster=.020, windowSize=11, margin=5),'Brownian motion assumed, because no direction could be calculated')
expect_equal(
raster(xd)
,
raster(x)
)
}) |
codeNames = c("Data","Technical.Constraints","Performance.Parameters","Client.and.Consultant.Requests","Design.Reasoning","Collaboration")
accum = ena.accumulate.data(
units = RS.data[,c("Condition","UserName")],
conversation = RS.data[,c("Condition","GroupName")],
metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post","C.Change")],
codes = RS.data[,codeNames],
model = "EndPoint",
window.size.back = 4
);
set = ena.make.set(
enadata = accum,
rotation.by = ena.rotate.by.mean,
rotation.params = list(FirstGame=accum$meta.data$Condition=="FirstGame", SecondGame=accum$meta.data$Condition=="SecondGame")
);
first.game = set$meta.data$Condition == "FirstGame"
first.game.points = set$points.rotated[first.game,]
second.game = set$meta.data$Condition == "SecondGame"
second.game.points = set$points.rotated[second.game,]
ena.conversations(set = set,
units = c("FirstGame.steven z"), units.by=c("Condition","UserName"),
conversation.by = c("Condition","GroupName"),
codes=codeNames,
window = 4
)
first.game.lineweights = set$line.weights[first.game,]
first.game.mean = colMeans(first.game.lineweights)
second.game.lineweights = set$line.weights[second.game,]
second.game.mean = colMeans(second.game.lineweights)
subtracted.network = first.game.mean - second.game.mean
plot1 = rENA::ena.plot(set)
plot1 = rENA::ena.plot.network(plot1, network = subtracted.network)
plot2 = rENA::ena.plot(set)
plot2 = rENA::ena.plot.group(plot2, second.game.points, labels = "SecondGame", colors = "blue", confidence.interval = "box")
plot2 = rENA::ena.plot.group(plot2, first.game.points, labels = "FirstGame", colors = "red", confidence.interval = "box")
plot3 = rENA::ena.plot(set)
plot3 = rENA::ena.plot.network(plot3, network = subtracted.network)
plot3 = rENA::ena.plot.group(plot3, first.game.points, labels = "FirstGame", colors = "red", confidence.interval = "box")
plot3 = rENA::ena.plot.group(plot3, second.game.points, labels = "SecondGame", colors = "blue", confidence.interval = "box")
dim.by.activity = cbind(
set$points.rotated[,1],
set$enadata$trajectories$step$ActivityNumber*.8/14-.4
)
accum = ena.accumulate.data(
units = RS.data[,c("UserName","Condition")],
conversation = RS.data[,c("GroupName","ActivityNumber")],
metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post","C.Change")],
codes = RS.data[,codeNames],
window.size.back = 4,
model = "A"
);
set = ena.make.set(accum);
plot = ena.plot(set)
plot = ena.plot.network(plot, network = subtracted.network, legend.name="Network", legend.include.edges = T)
dim.by.activity = cbind(
set$points.rotated[,1],
set$enadata$trajectories$step$ActivityNumber*.8/14-.4
) |
expected <- eval(parse(text="structure(list(price.index = c(4.70997, 4.70217, 4.68944, 4.68558, 4.64019, 4.62553, 4.61991, 4.61654, 4.61407, 4.60766, 4.60227, 4.5896, 4.57592, 4.58661, 4.57997, 4.57176, 4.56104, 4.54906, 4.53957, 4.51018, 4.50352, 4.4936, 4.46505, 4.44924, 4.43966, 4.42025, 4.4106, 4.41151, 4.3981, 4.38513, 4.3732, 4.3277, 4.32023, 4.30909, 4.30909, 4.30552, 4.29627, 4.27839, 4.27789)), .Names = \"price.index\")"));
test(id=0, code={
argv <- eval(parse(text="list(structure(list(y = structure(c(8.79236, 8.79137, 8.81486, 8.81301, 8.90751, 8.93673, 8.96161, 8.96044, 9.00868, 9.03049, 9.06906, 9.05871, 9.10698, 9.12685, 9.17096, 9.18665, 9.23823, 9.26487, 9.28436, 9.31378, 9.35025, 9.35835, 9.39767, 9.4215, 9.44223, 9.48721, 9.52374, 9.5398, 9.58123, 9.60048, 9.64496, 9.6439, 9.69405, 9.69958, 9.68683, 9.71774, 9.74924, 9.77536, 9.79424), .Tsp = c(1962.25, 1971.75, 4), class = \"ts\"), lag.quarterly.revenue = c(8.79636, 8.79236, 8.79137, 8.81486, 8.81301, 8.90751, 8.93673, 8.96161, 8.96044, 9.00868, 9.03049, 9.06906, 9.05871, 9.10698, 9.12685, 9.17096, 9.18665, 9.23823, 9.26487, 9.28436, 9.31378, 9.35025, 9.35835, 9.39767, 9.4215, 9.44223, 9.48721, 9.52374, 9.5398, 9.58123, 9.60048, 9.64496, 9.6439, 9.69405, 9.69958, 9.68683, 9.71774, 9.74924, 9.77536), price.index = c(4.70997, 4.70217, 4.68944, 4.68558, 4.64019, 4.62553, 4.61991, 4.61654, 4.61407, 4.60766, 4.60227, 4.5896, 4.57592, 4.58661, 4.57997, 4.57176, 4.56104, 4.54906, 4.53957, 4.51018, 4.50352, 4.4936, 4.46505, 4.44924, 4.43966, 4.42025, 4.4106, 4.41151, 4.3981, 4.38513, 4.3732, 4.3277, 4.32023, 4.30909, 4.30909, 4.30552, 4.29627, 4.27839, 4.27789), income.level = c(5.8211, 5.82558, 5.83112, 5.84046, 5.85036, 5.86464, 5.87769, 5.89763, 5.92574, 5.94232, 5.95365, 5.9612, 5.97805, 6.00377, 6.02829, 6.03475, 6.03906, 6.05046, 6.05563, 6.06093, 6.07103, 6.08018, 6.08858, 6.10199, 6.11207, 6.11596, 6.12129, 6.122, 6.13119, 6.14705, 6.15336, 6.15627, 6.16274, 6.17369, 6.16135, 6.18231, 6.18768, 6.19377, 6.2003), market.potential = c(12.9699, 12.9733, 12.9774, 12.9806, 12.9831, 12.9854, 12.99, 12.9943, 12.9992, 13.0033, 13.0099, 13.0159, 13.0212, 13.0265, 13.0351, 13.0429, 13.0497, 13.0551, 13.0634, 13.0693, 13.0737, 13.077, 13.0849, 13.0918, 13.095, 13.0984, 13.1089, 13.1169, 13.1222, 13.1266, 13.1356, 13.1415, 13.1444, 13.1459, 13.152, 13.1593, 13.1579, 13.1625, 13.1664)), .Names = c(\"y\", \"lag.quarterly.revenue\", \"price.index\", \"income.level\", \"market.potential\"), row.names = c(\"1962.25\", \"1962.5\", \"1962.75\", \"1963\", \"1963.25\", \"1963.5\", \"1963.75\", \"1964\", \"1964.25\", \"1964.5\", \"1964.75\", \"1965\", \"1965.25\", \"1965.5\", \"1965.75\", \"1966\", \"1966.25\", \"1966.5\", \"1966.75\", \"1967\", \"1967.25\", \"1967.5\", \"1967.75\", \"1968\", \"1968.25\", \"1968.5\", \"1968.75\", \"1969\", \"1969.25\", \"1969.5\", \"1969.75\", \"1970\", \"1970.25\", \"1970.5\", \"1970.75\", \"1971\", \"1971.25\", \"1971.5\", \"1971.75\"), class = \"data.frame\"), 3L)"));
do.call(`.subset`, argv);
}, o=expected); |
structure(list(url = "/browse-edgar?action=getcurrent&CIK=&type=&company=&datea=&dateb=&owner=include&start=0&count=40&output=atom",
status_code = 200L, headers = structure(list(`content-encoding` = "gzip",
`content-type` = "application/atom+xml", server = "Apache",
`x-content-type-options` = "nosniff", `x-frame-options` = "SAMEORIGIN",
`x-xss-protection` = "1; mode=block", `content-length` = "1705",
`cache-control` = "no-cache", date = "Sun, 18 Apr 2021 16:54:19 GMT",
vary = "Accept-Encoding", `strict-transport-security` = "max-age=31536000 ; includeSubDomains ; preload",
location = character(0)), class = c("insensitive", "list"
)), all_headers = list(list(status = 200L, version = "HTTP/2",
headers = structure(list(`content-encoding` = "gzip",
`content-type` = "application/atom+xml", server = "Apache",
`x-content-type-options` = "nosniff", `x-frame-options` = "SAMEORIGIN",
`x-xss-protection` = "1; mode=block", `content-length` = "1705",
`cache-control` = "no-cache", date = "Sun, 18 Apr 2021 16:54:19 GMT",
vary = "Accept-Encoding", `strict-transport-security` = "max-age=31536000 ; includeSubDomains ; preload"), class = c("insensitive",
"list")))), cookies = structure(list(domain = logical(0),
flag = logical(0), path = logical(0), secure = logical(0),
expiration = structure(numeric(0), class = c("POSIXct",
"POSIXt")), name = logical(0), value = logical(0)), row.names = integer(0), class = "data.frame"),
content = as.raw(c(0x3c, 0x3f, 0x78, 0x6d, 0x6c, 0x20, 0x76,
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0x65, 0x64, 0x3e, 0x0a)), date = structure(1618764859, class = c("POSIXct",
"POSIXt"), tzone = "GMT"), times = c(redirect = 0, namelookup = 2.4e-05,
connect = 2.5e-05, pretransfer = 7.9e-05, starttransfer = 0.093213,
total = 0.093385)), class = "response") |
report.bayesfactor_models <- function(x,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
out <- .report.bayesfactor_models(
x,
interpretation = interpretation,
exact = exact,
protect_ratio = protect_ratio,
...
)
as.report(
text = as.report_text(out$text_full, summary = out$text_short),
table = as.report_table(out$table_full, summary = out$table_short),
rules = out$rules,
denominator = out$denominator,
BF_method = out$BF_method
)
}
report_table.bayesfactor_models <- function(x,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
out <-
.report.bayesfactor_models(
x,
interpretation = interpretation,
exact = exact,
protect_ratio = protect_ratio,
...
)
as.report_table(out$table_full,
summary = out$table_short,
rules = out$rules,
denominator = out$denominator,
BF_method = out$BF_method
)
}
report_text.bayesfactor_models <- function(x,
table = NULL,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
out <- .report.bayesfactor_models(
x,
interpretation = interpretation,
exact = exact,
protect_ratio = protect_ratio,
...
)
as.report_text(out$text_full,
summary = out$text_short,
rules = out$rules,
denominator = out$denominator,
BF_method = out$BF_method
)
}
.report.bayesfactor_models <- function(model,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
model$Model[model$Model == "1"] <- "(Intercept only)"
denominator <- attr(model, "denominator")
BF_method <- attr(model, "BF_method")
max_den <- which.max(exp(model$log_BF))
min_den <- which.min(exp(model$log_BF))
model_ind <- rep("", nrow(model))
model_ind[max_den] <- " (the most supported model)"
model_ind[min_den] <- " (the least supported model)"
summ_inds <- c(max_den, min_den)
summ_inds <- summ_inds[summ_inds != denominator]
bf_text <- paste0(
"Compared to the ", model$Model[denominator], " model", model_ind[denominator], ", ",
"we found ",
paste0(
effectsize::interpret_bf(exp(model$log_BF)[summ_inds],
rules = interpretation, include_value = TRUE,
exact = exact, protect_ratio = protect_ratio
),
" the ", model$Model[summ_inds], " model", model_ind[summ_inds],
collapse = "; "
),
"."
)
if (grepl("BIC", BF_method)) {
bf_explain <- paste0(
"Bayes factors were computed using the BIC approximation, ",
"by which BF10 = exp((BIC0 - BIC1)/2). "
)
} else if (grepl("JZS", BF_method)) {
bf_explain <- paste0(
"Bayes factors were computed with the `BayesFactor` package, ",
"using JZS priors. "
)
} else if (grepl("bridgesampling", BF_method)) {
bf_explain <- paste0(
"Bayes factors were computed by comparing marginal likelihoods, ",
"using the `bridgesampling` package. "
)
}
bf_text_full <- paste0(bf_explain, paste0(
"Compared to the ", model$Model[denominator], " model", model_ind[denominator], ", ",
"we found ",
paste0(
effectsize::interpret_bf(exp(model$log_BF)[-denominator],
rules = interpretation, include_value = TRUE,
exact = exact, protect_ratio = protect_ratio
),
" the ", model$Model[-denominator], " model", model_ind[-denominator],
collapse = "; "
),
"."
))
model$Model <- paste0(" [", seq_len(nrow(model)), "] ", model$Model)
bf_table <- as.data.frame(model)
bf_table$BF <- insight::format_bf(exp(model$log_BF),
name = NULL,
exact = exact,
protect_ratio = protect_ratio
)
colnames(bf_table) <- c("Model", "Bayes factor")
footer <- list(
paste0("\nBayes Factor Type: ", BF_method),
paste0("\nAgainst denominator: Model ", denominator)
)
attr(bf_table, "table_footer") <- footer
bf_table_full <- bf_table
bf_table_full$BF2 <- insight::format_bf(exp(model$log_BF) / exp(model$log_BF)[max_den],
name = NULL,
exact = exact,
protect_ratio = protect_ratio
)
colnames(bf_table_full) <- c(
"Model",
paste0("BF against model ", denominator, ""),
paste0("BF against model ", max_den, " (best model)")
)
out <- list(
table_full = bf_table_full,
table_short = bf_table,
text_full = bf_text_full,
text_short = bf_text,
rules = interpretation,
denominator = denominator,
BF_method = BF_method
)
}
report.bayesfactor_inclusion <- function(x,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
out <- .report.bayesfactor_inclusion(
x,
interpretation = interpretation,
exact = exact,
protect_ratio = protect_ratio,
...
)
as.report(
text = as.report_text(out$text_full, summary = out$text_short),
table = as.report_table(out$table_full, summary = out$table_short),
interpretation = out$interpretation,
priorOdds = out$priorOdds,
matched = out$matched
)
}
report_table.bayesfactor_inclusion <- function(x,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
out <- .report.bayesfactor_inclusion(
x,
interpretation = interpretation,
exact = exact,
protect_ratio = protect_ratio,
...
)
as.report_table(out$table_full,
summary = out$table_short,
interpretation = out$interpretation,
priorOdds = out$priorOdds,
matched = out$matched
)
}
report_text.bayesfactor_inclusion <- function(x,
table = NULL,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
out <- .report.bayesfactor_inclusion(
x,
interpretation = interpretation,
exact = exact,
protect_ratio = protect_ratio,
...
)
as.report_text(out$text_full,
summary = out$text_short,
interpretation = out$interpretation,
priorOdds = out$priorOdds,
matched = out$matched
)
}
.report.bayesfactor_inclusion <- function(model,
interpretation = "jeffreys1961",
exact = TRUE,
protect_ratio = TRUE,
...) {
matched <- attr(model, "matched")
priorOdds <- attr(model, "priorOdds")
bf_results <- data.frame(Term = rownames(model), stringsAsFactors = FALSE)
bf_results$evidence <- effectsize::interpret_bf(exp(model$log_BF),
rules = interpretation, include_value = TRUE,
exact = exact, protect_ratio = protect_ratio
)
bf_results$postprob <- paste0(round(model$p_posterior * 100, ...), "%")
bf_text <- paste0(
"Bayesian model averaging (BMA) was used to obtain the average evidence ",
"for each predictor. We found ",
paste0(
paste0(bf_results$evidence, " including ", bf_results$Term),
collapse = "; "
), "."
)
bf_explain <- paste0(
"Bayesian model averaging (BMA) was used to obtain the average evidence ",
"for each predictor. Since each model has a prior probability",
if (is.null(priorOdds)) {
NULL
} else {
paste0(
" (here we used subjective prior odds of ",
paste0(priorOdds, collapse = ", "), ")"
)
},
", it is possible to sum the prior probability of all models that include ",
"a predictor of interest (the prior inclusion probability), and of all ",
"models that do not include that predictor (the prior exclusion probability). ",
"After the data are observed, we can similarly consider the sums of the ",
"posterior models' probabilities to obtain the posterior inclusion ",
"probability and the posterior exclusion probability. The change from ",
"prior to posterior inclusion odds is the Inclusion Bayes factor. ",
if (!matched) {
NULL
} else {
paste0(
"For each predictor, averaging was done only across models that ",
"did not include any interactions with that predictor; ",
"additionally, for each interaction predictor, averaging was done ",
"only across models that contained the main effect from which the ",
"interaction predictor was comprised. This was done to prevent ",
"Inclusion Bayes factors from being contaminated with non-relevant ",
"evidence (see Mathot, 2017). "
)
}
)
bf_text_full <- paste0(
bf_explain,
paste0(
"We found ",
paste0(
paste0(
bf_results$evidence, " including ", bf_results$Term,
", with models including ", bf_results$Term,
" having an overall posterior probability of ", bf_results$postprob
),
collapse = "; "
), "."
)
)
bf_table <- as.data.frame(model)
colnames(bf_table) <- c("Pr(prior)", "Pr(posterior)", "Inclusion BF")
bf_table <- cbind(Terms = rownames(bf_table), bf_table)
rownames(bf_table) <- NULL
bf_table$`Inclusion BF` <- insight::format_bf(
bf_table$`Inclusion BF`,
name = NULL,
exact = exact,
protect_ratio = protect_ratio
)
bf_table[, 2:3] <- insight::format_value(bf_table[, 2:3], ...)
footer <- list(
sprintf("\nAcross %s", ifelse(matched, "matched models only.", "all models.")),
ifelse(is.null(priorOdds),
"\nAssuming unifor prior odds.",
paste0("\nWith custom prior odds of [", paste0(priorOdds, collapse = ", "), "].")
)
)
attr(bf_table, "table_footer") <- footer
bf_table_full <- bf_table
out <- list(
table_full = bf_table_full,
table_short = bf_table,
text_full = bf_text_full,
text_short = bf_text,
interpretation = interpretation,
priorOdds = priorOdds,
matched = matched, ...
)
} |
run_nm <- function(m,
ignore.stdout = TRUE, ignore.stderr = TRUE,
quiet = getOption("quiet_run"), intern = getOption("intern"),
force = FALSE,
cache_ignore_cmd = FALSE, cache_ignore_ctl = FALSE, cache_ignore_data = FALSE) {
UseMethod("run_nm")
}
run_nm.nm_generic <- function(m,
ignore.stdout = TRUE, ignore.stderr = TRUE,
quiet = getOption("quiet_run"), intern = getOption("intern"),
force = FALSE,
cache_ignore_cmd = FALSE, cache_ignore_ctl = FALSE, cache_ignore_data = FALSE) {
if (is.na(m)) {
return(m)
}
ctl <- ctl_contents(m)
if (length(ctl) == 1) {
if (is.na(ctl)) {
warning("no ctl_contents defined. Use ?based_on")
return(m)
}
}
m %>% write_ctl()
if (!force) {
if (length(run_cache_paths(m)) > 0) {
run_cache_disk <- readRDS(run_cache_paths(m))
current_checksums <- run_checksums(m)
if (cache_ignore_cmd) {
keep <- !names(current_checksums) %in% "cmd"
run_cache_disk$checksums <- run_cache_disk$checksums[keep]
current_checksums <- current_checksums[keep]
}
if (cache_ignore_ctl) {
keep <- !names(current_checksums) %in% "ctl"
run_cache_disk$checksums <- run_cache_disk$checksums[keep]
current_checksums <- current_checksums[keep]
}
if (cache_ignore_data) {
keep <- !names(current_checksums) %in% "data"
run_cache_disk$checksums <- run_cache_disk$checksums[keep]
current_checksums <- current_checksums[keep]
}
names(current_checksums) <- NULL
names(run_cache_disk$checksums) <- NULL
matched <- identical(run_cache_disk$checksums, current_checksums)
if (matched) {
if (!is_finished(m)) {
warning("run was previously executed but has is_finished() status = FALSE")
}
message("rebuilding run from cache... use run_nm(force = TRUE) to override")
m <- m %>% executed(TRUE)
m <- m %>% job_info(run_cache_disk$job_info)
m <- m %>% save_run_cache()
return(invisible(m))
}
}
}
behaviour <- overwrite_behaviour()
if ("skip" %in% behaviour) {
message("skipping step as it would require overwriting \n change behaviour with overwrite_behaviour()")
return(invisible(m))
}
if (.sso_env$run_count == 0 & !force) {
tryCatch(psn_check(fail_if_false = TRUE), error = function(e){
usethis::ui_stop("This message will only appear on first attempt to run NONMEM in the active R session.
Failed: {usethis::ui_code('psn_check()')}.
This is either due to a missing or incorrectly configured PsN installation or {usethis::ui_code('system_nm()')} not being configured for your system.
Ensure PsN (and NONMEM) are installed and available from the command line with {usethis::ui_code('system_nm()')} and {usethis::ui_code('system_nm_intern()')}.
The command {usethis::ui_code('system_nm_intern(\"psn --version\")')} should return the PsN version number.
See {usethis::ui_path('https://uupharmacometrics.github.io/PsN/')} for instructions to install or re-install PsN.
If PsN is installed, you may need to configure {usethis::ui_code('system_nm()')}. See {usethis::ui_code('?system_nm')} for config help.
If you want to overide this message for future R session, use {usethis::ui_code('force = TRUE')}")
})
}
.sso_env$run_count <- .sso_env$run_count + 1
wipe_run(m)
kill_job(m)
message(paste0("Running: ", type(m), ":", ctl_path(m)))
stdout0 <- system_nm(
cmd = cmd(m), dir = run_in(m), wait = FALSE,
ignore.stdout = FALSE, ignore.stderr = FALSE,
intern = intern
)
if (intern) {
cat(stdout0, sep = "\n")
job_info <- getOption("get_job_info")(stdout0)
if (is.null(job_info)) job_info <- NA
} else {
job_info <- NA
}
m <- m %>% executed(TRUE)
m <- m %>% job_info(job_info)
m <- m %>% save_run_cache()
invisible(m)
}
run_nm.nm_list <- Vectorize_nm_list(run_nm.nm_generic, SIMPLIFY = FALSE, invisible = TRUE)
run_nm_batch <- function(m, threads = 10, ...) {
runs_remaining <- seq_along(m)
while (length(runs_remaining) > 0) {
n_to_take <- min(threads, length(runs_remaining))
runs_to_run <- runs_remaining[seq_len(n_to_take)]
m_sub <- m[runs_to_run]
run_nm(m_sub, ...)
runs_remaining <- setdiff(runs_remaining, runs_to_run)
if (length(runs_remaining) > 0) wait_finish(m_sub)
}
m
}
wipe_run <- function(r) {
UseMethod("wipe_run")
}
wipe_run.nm_generic <- function(r) {
psn_exported_files <- psn_exported_files(r)
lst_path <- file.path(run_in(r), lst_path(r))
run_dir_to_delete <- file.path(run_in(r), run_dir(r))
if (!file.exists(run_dir_to_delete)) {
run_dir_to_delete <- c()
} else {
if (run_dir_to_delete %in% c(".", ".\\")) {
run_dir_to_delete <- c()
} else {
if (normalizePath(run_dir_to_delete) == normalizePath(run_in(r))) run_dir_to_delete <- c()
}
}
ctl_out_files <- c(lst_path, psn_exported_files, run_dir_to_delete)
existing_ctl_out_files <- ctl_out_files[file.exists(ctl_out_files)]
behaviour <- overwrite_behaviour()
if ("stop" %in% behaviour & length(existing_ctl_out_files) > 0) {
stop(
"no overwriting allowed, stopping due to following files/directories:\n ",
paste(paste(existing_ctl_out_files, collapse = "\n "))
)
}
prompt_overwrite(rev(existing_ctl_out_files))
unlink(ctl_out_files, recursive = TRUE, force = TRUE)
invisible()
}
wipe_run.nm_list <- Vectorize_nm_list(wipe_run.nm_generic, SIMPLIFY = FALSE, invisible = TRUE)
psn_exported_files <- function(r, minimal = FALSE) {
UseMethod("psn_exported_files")
}
psn_exported_files.nm_generic <- function(r, minimal = FALSE) {
if (minimal) {
output_files <- paste0(
tools::file_path_sans_ext(ctl_path(r)),
c(".ext")
)
} else {
output_files <- paste0(
tools::file_path_sans_ext(ctl_path(r)),
c(".phi", ".ext", ".cov", ".coi", ".cor", ".lst")
)
}
if (minimal) {
ctl_out_files <- c(output_files)
} else {
exported_table_paths <- file.path(run_in(r), ctl_table_files(ctl_contents(r)))
ctl_out_files <- c(output_files, exported_table_paths)
}
ctl_out_files
}
psn_exported_files.nm_list <- Vectorize_nm_list(psn_exported_files.nm_generic, SIMPLIFY = FALSE)
ctl_table_files <- function(ctl) {
UseMethod("ctl_table_files")
}
ctl_table_files.default <- function(ctl) {
ctl <- ctl_character(ctl)
s0 <- rem_comment(ctl)
s <- grep("FILE\\s*=\\s*(\\S+)", s0, value = TRUE)
table_files <- gsub(".*FILE\\s*=\\s*(\\S+)\\s*.*$", "\\1", s)
table_files
}
ls_tempfiles <- function(object = ".", output_loc = c("run_dir", "base"),
run_files = NA_character_, include_slurm_files = TRUE,
ctl_extension = "mod",
include_psn_exports = FALSE) {
UseMethod("ls_tempfiles")
}
ls_tempfiles.default <- function(object = ".", output_loc = c("run_dir", "base"),
run_files = NA_character_, include_slurm_files = TRUE,
ctl_extension = "mod",
include_psn_exports = FALSE) {
output_loc <- match.arg(output_loc)
if (length(run_files) == 0) return(character())
if (identical(run_files, NA_character_)) {
all_run_dirs <- list_dirs(
object,
pattern = "NM_run[0-9]+",
recursive = TRUE, full.names = TRUE, maxdepth = Inf
)
if (length(all_run_dirs) == 0) {
return(character())
} else {
all_run_files <- dir(all_run_dirs, full.names = TRUE)
}
all_outside_run_dirs <- file.path(all_run_dirs, "..")
all_outside_run_files <- dir(all_outside_run_dirs, full.names = TRUE)
all_run_files <- c(all_run_files, all_outside_run_files)
} else {
all_run_files <- run_files
}
temp_files <- c()
non_temp_files <- c()
all_psn.mod <- all_run_files[basename(all_run_files) == "psn.mod"]
non_temp_files <- c(non_temp_files, all_psn.mod)
all_run_dir_table_files <-
lapply(all_psn.mod, function(psn.mod) {
file.path(dirname(psn.mod), ctl_table_files(psn.mod))
})
all_run_dir_table_files <- unlist(all_run_dir_table_files)
non_temp_files <- c(non_temp_files, all_run_dir_table_files)
all_base_tables <- all_run_dir_table_files
all_base_table_dir <- dirname(all_base_tables)
all_base_table_dir <- file.path(all_base_table_dir, "..", "..")
all_base_table_dir <- normalizePath(all_base_table_dir)
if (output_loc == "run_dir") {
all_base_tables <- file.path(all_base_table_dir, basename(all_run_dir_table_files))
all_base_tables <- all_base_tables[file.exists(all_base_tables)]
if (include_psn_exports) temp_files <- c(temp_files, all_base_tables)
all_base_mod_files <- dir(unique(all_base_table_dir),
pattern = paste0("\\.", ctl_extension, "$"),
full.names = TRUE
)
all_base_stubs <- tools::file_path_sans_ext(all_base_mod_files)
all_base_psn_files <- lapply(all_base_stubs, function(base_mod_stub) {
base_dir <- dirname(base_mod_stub)
stub <- basename(base_mod_stub)
dir(base_dir, pattern = paste0("^", stub, "\\..*"), full.names = TRUE)
})
all_base_psn_files <- unlist(all_base_psn_files)
all_base_psn_files <- all_base_psn_files[
!tools::file_ext(all_base_psn_files) %in% c("mod", "lst")
]
if (include_psn_exports) temp_files <- c(temp_files, all_base_psn_files)
}
temp_files <- c(temp_files, all_run_files[grepl("temp_dir", all_run_files)])
temp_files <- c(temp_files, all_run_files[tools::file_ext(basename(all_run_files)) %in% c("o", "f90", "Rmd", "csv")])
temp_files <- c(
temp_files,
all_run_files[basename(all_run_files) %in%
c(
"INTER",
"fort.2002",
"model_NMrun_translation.txt",
"modelfit.log",
"raw_results_structure",
"version_and_option_info.txt"
)]
)
temp_files <- setdiff(temp_files, non_temp_files)
temp_dirs <- temp_files[file.info(temp_files)$isdir]
temp_dir_files <- dir(temp_dirs, full.names = TRUE, recursive = TRUE)
temp_files <- c(
temp_files[!temp_files %in% temp_dirs],
temp_dir_files
)
if (length(temp_files) == 0) {
return(character())
}
relative_path(temp_files, getwd())
}
ls_tempfiles.nm_list <- function(object = ".", output_loc = c("run_dir", "base"),
run_files = NA_character_, include_slurm_files = TRUE,
ctl_extension = "mod",
include_psn_exports = FALSE) {
output_loc <- match.arg(output_loc)
if (output_loc %in% "run_dir") {
all_run_dirs <- list_dirs(
run_dir_path(object),
pattern = "NM_run[0-9]+",
full.names = TRUE
)
} else {
all_run_dirs <- list_dirs(
run_in(object),
full.names = TRUE
)
}
all_run_files <- dir(all_run_dirs, full.names = TRUE)
ls_tempfiles(
run_files = all_run_files, output_loc = output_loc,
ctl_extension = tools::file_ext(ctl_name(object)),
include_psn_exports = include_psn_exports
)
}
clean_run <- function(m, output_loc = c("run_dir", "base"), include_slurm_files = TRUE) {
.Deprecated("clean_tempfiles")
UseMethod("clean_run")
}
clean_run.nm_list <- function(m, output_loc = c("run_dir", "base"), include_slurm_files = TRUE) {
ls_tempfiles(m, output_loc = output_loc) %>%
unlink(force = TRUE)
}
clean_tempfiles <- function(object = ".", output_loc = c("run_dir", "base"), include_slurm_files = TRUE) {
UseMethod("clean_run")
}
clean_tempfiles.nm_list <- function(object = ".", output_loc = c("run_dir", "base"), include_slurm_files = TRUE) {
ls_tempfiles(object, output_loc = output_loc) %>%
unlink(force = TRUE)
}
write_ctl <- function(m, force = FALSE) {
UseMethod("write_ctl")
}
write_ctl.nm_generic <- function(m, force = FALSE) {
ctl_name <- ctl_path(m)
ctl_ob <- ctl_contents(m) %>% ctl_character()
dir_name <- run_in(m)
if (!file.exists(dir_name)) {
dir.create(dir_name, showWarnings = FALSE, recursive = TRUE)
}
if (file.exists(ctl_name) & !force) {
behaviour <- overwrite_behaviour()
old_contents <- readLines(ctl_name)
new_contents <- ctl_ob
attributes(new_contents) <- NULL
overwrite_required <- !identical(new_contents, old_contents)
if (overwrite_required) {
if ("stop" %in% behaviour) {
stop("stopping because overwrite required: change behaviour in overwrite_behaviour()")
}
if ("ask" %in% behaviour) {
prompt_overwrite(ctl_name, new_path_contents = ctl_ob)
}
if ("skip" %in% behaviour) {
return(invisible(m))
}
}
}
writeLines(ctl_ob, ctl_name)
invisible(m)
}
write_ctl.nm_list <- Vectorize_nm_list(write_ctl.nm_generic, SIMPLIFY = FALSE, invisible = TRUE)
data_name <- function(x) UseMethod("data_name")
data_name.default <- function(x) {
unlist(lapply(x, function(x) {
if (!file.exists(x)) x <- file.path(nm_dir("models"), x)
if (!file.exists(x)) stop("can't find control stream")
x <- normalizePath(x)
ctl <- readLines(x, warn = FALSE)
data.row <- grep("^ *\\$DATA", ctl)
if (length(data.row) < 1) stop("can't identify data row")
if (length(data.row) > 1) {
warning("multiple data rows found. Using first")
data.row <- data.row[1]
}
ctl <- paste(ctl[data.row:length(ctl)], collapse = " ")
data_name <- gsub("^ *\\$DATA\\s*([^ ]+).*$", "\\1", ctl)
data_name
}))
} |
if (!require("fritools", character.only = TRUE))
install.packages("fritools", repos = "https://cloud.r-project.org/")
r <- fritools::is_running_on_gitlab_com(verbose = TRUE)
warning(attr(r, "message"))
print(r)
if (!isTRUE(r)) {
stop("fritools: Do not recognize gitlab.com")
} else {
message("Node is ", Sys.info()[["nodename"]],
", I guess this is gitlab.com.")
} |
"kfascv" <-
function(xt,cov,k=np,mdx=nrow(xt),f=.dFvGet()$fff,sg,ip) {
if (missing(xt)) messagena("xt")
np <- ncol(xt)
ncov <- length(cov)
if (missing(cov)) cov <- single(ncov)
se <- single(np)
if (missing(sg)) sg <- single(np)
if (missing(ip)) ip <- integer(np)
f.res <- .Fortran("kfascvz",
xt=to.single(xt),
cov=to.single(cov),
k=to.integer(k),
np=to.integer(np),
mdx=to.integer(mdx),
ncov=to.integer(ncov),
f=to.single(f),
se=to.single(se),
sg=to.single(sg),
ip=to.integer(ip))
list(cov=f.res$cov)
} |
getIdCols <- function(x = NULL) {
if (!is(x, "antaresDataTable") || is.null(x))
stop("x has to be an 'antaresDataTable' object")
.idCols(x)
} |
library(RoughSets)
data(RoughSetData)
decision.table <- RoughSetData$hiring.dt
res.1 <- FS.quickreduct.RST(decision.table)
print(res.1)
new.decTable <- SF.applyDecTable(decision.table, res.1)
print(new.decTable) |
{
library(vroom)
data <- vroom(file, col_types = c(pickup_datetime = "c"))
vroom:::vroom_materialize(data, replace = TRUE)
}
{
con <- gzfile(tempfile(fileext = ".gz"), "wb")
write.table(data, con, sep = "\t", quote = FALSE, row.names = FALSE)
close(con)
} |
plot.smacofPerm <- function(x, alpha = 0.05, main, xlab, ylab, ...)
{
if (missing(main)) main <- "ECDF SMACOF Permutation Test" else main <- main
if (missing(xlab)) xlab <- "Stress Values" else xlab <- xlab
if (missing(ylab)) ylab <- "Probability" else ylab <- ylab
Ecdf(x$stressvec, main = main, xlab = xlab, ylab = ylab, subtitles = FALSE, ...)
abline(v = x$stress.obs, col = "gray", lty = 1)
text(x$stress.obs, y = 1, labels = paste("Stress: ",round(x$stress.obs, 3), sep = ""), col = "gray", pos = 4, cex = 0.7)
abline(h = x$pval, col = "gray", lty = 1)
if (x$pval == 0) {
pp <- "<0.001"
text(max(x$stressvec)-0.01*max(x$stressvec), y = x$pval, labels = paste("p-value: ", pp, sep = ""), col = "gray", pos = 3, cex = 0.7)
} else {
pp <- x$pval
text(max(x$stressvec)-0.01*max(x$stressvec), y = x$pval, labels = paste("p-value: ", round(pp, 3), sep = ""), col = "gray", pos = 3, cex = 0.7)
}
abline(h = alpha, col = "gray", lty = 2)
}
|
plot.group_metric <- function(x, ...) {
data <- x$group_metric_data
performance_data <- x$performance_data
fairness_metric <- x$fairness_metric
performance_metric <- x$performance_metric
n <- length(unique(data$model))
model <- group <- score <- label <- NULL
plot1 <- ggplot(data, aes(x = group, y = score, fill = model)) +
geom_bar(
stat = "identity",
position = "dodge"
) +
DALEX::theme_drwhy() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "none"
) +
ylab(fairness_metric) +
xlab("subgroups") +
scale_fill_manual(values = colors_fairmodels(n)) +
ggtitle("Group metric plot")
plot2 <- ggplot(performance_data, aes(x = model, y = score, fill = model)) +
geom_bar(
stat = "identity",
width = 0.4
) +
geom_text(aes(label = round(score, 3)),
vjust = -1,
color = "black",
size = 3,
fontface = "bold"
) +
DALEX::theme_drwhy() +
theme(
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 0.3)
) +
scale_fill_manual(values = colors_fairmodels(n)) +
scale_y_continuous(limits = c(0, 1)) +
xlab("Models") +
ylab(performance_metric)
plot1 + plot2
} |
"latent<-" <- function(x,...,value) UseMethod("latent<-")
"latent<-.lvm" <- function(x, clear=FALSE,..., value) {
if (inherits(value,"formula")) {
return(latent(x,all.vars(value),clear=clear,...))
}
latent(x, var=value, clear=clear,...)
}
`latent` <-
function(x,...) UseMethod("latent")
`latent.lvm` <- function(x,var,clear=FALSE,messages=lava.options()$messages,...) {
if (missing(var)) {
latentidx <- unlist(x$latent)
if (length(latentidx)>0)
return(names(latentidx))
else
return(NULL)
}
if (inherits(var,"formula")) var <- all.vars(var)
if (clear) {
x$noderender$shape[var] <- "rectangle"
x$latent[var] <- NULL
} else {
if (!all(var%in%vars(x))) {
addvar(x,messages=messages,reindex=FALSE,) <- setdiff(var,vars(x))
}
x$noderender$shape[var] <- "ellipse"
x$latent[var] <- TRUE
ord <- intersect(var,ordinal(x))
if (length(ord)>0) ordinal(x,K=NULL) <- ord
}
xorg <- exogenous(x)
exoset <- setdiff(xorg,var)
if (length(exoset)<length(xorg)) {
exogenous(x) <- exoset
}
index(x) <- reindex(x)
return(x)
}
`latent.lvmfit` <-
function(x,clear=FALSE,...) {
latent(Model(x),...)
}
latent.list <- function(x,...) {
latlist <- c()
for (i in seq_along(x)) {
latlist <- c(latlist, latent(x[[i]]))
}
latlist <- unique(latlist)
return(latlist)
}
`latent.multigroup` <-
function(x,...) {
latent(Model(x))
} |
kmunionclosure <- function(x) {
if (!inherits(x, "matrix")) {
stop(sprintf("%s must be of class %s.", dQuote("x"), dQuote("matrix")))
}
if (any(x != 1*as.logical(x))) {
stop(sprintf("%s must be a binary matrix.", dQuote("x")))
}
noi <- dim(x)[2]
nob <- dim(x)[1]
f <- matrix(rep(0L, noi), nrow = 1, ncol = noi)
lapply(as.list(1:nob), function(i) {
f2 <- t(apply(f, 1, function(s) {1L*(s | x[i,])}))
f <<- unique(rbind(f, f2))
})
storage.mode(f) <- "integer"
f
} |
library(plyr)
suppressPackageStartupMessages(library(dplyr))
library(ggplot2)
library(readr)
gap_dat <- read_tsv("05_gap-merged-with-china-1952.tsv") %>%
mutate(country = factor(country),
continent = factor(continent))
gap_dat %>% str
gap_dat$continent %>% summary()
tmp <- gap_dat %>%
filter(continent == "FSU") %>%
droplevels()
tmp$country %>% levels()
tmp <- gap_dat %>%
filter(is.na(continent)) %>%
droplevels()
tmp$country %>% levels()
cont_dat <- frame_data(
~country, ~continent,
'Armenia', 'FSU',
'Aruba', 'Americas',
'Australia', 'Oceania',
'Bahamas', 'Americas',
'Barbados', 'Americas',
'Belize', 'Americas',
'Canada', 'Americas',
'French Guiana', 'Americas',
'French Polynesia', 'Oceania',
'Georgia', 'FSU',
'Grenada', 'Americas',
'Guadeloupe', 'Americas',
'Haiti', 'Americas',
'Hong Kong, China', 'Asia',
'Maldives', 'Asia',
'Martinique', 'Americas',
'Micronesia, Fed. Sts.', 'Oceania',
'Netherlands Antilles', 'Americas',
'New Caledonia', 'Oceania',
'Papua New Guinea', 'Oceania',
'Reunion', 'Africa',
'Samoa', 'Oceania',
'Sao Tome and Principe', 'Africa',
'Tonga', 'Oceania',
'Uzbekistan', 'FSU',
'Vanuatu', 'Oceania')
gap_dat <- gap_dat %>%
merge(cont_dat, by = "country", all = TRUE) %>%
tbl_df() %>%
mutate(continent = factor(ifelse(is.na(continent.y),
as.character(continent.x),
as.character(continent.y))),
continent.x = NULL,
continent.y = NULL) %>%
arrange(country, year)
gap_dat %>% str()
gap_dat$continent %>% summary()
my_vars <- c('country', 'continent', 'year',
'lifeExp', 'pop', 'gdpPercap')
gap_dat <- gap_dat[my_vars]
write_tsv(gap_dat, "07_gap-merged-with-continent.tsv") |
lam = 14^(1/4);
rrat = 1825^(1/32);
eps = rrat*lam/(2*(rrat*lam-1));
epsCml = 0.72;
rStoc = rrat^(1:6);
rDeterm = rrat^(7:30);
stage1Cells = 1/((2*eps-1)*rrat/400);
moran = function(regIni=399, cmlIni=1, period, timestep = 0.0025)
{
numSteps = floor(period/timestep);
regCells = c(regIni,rep(0,times=numSteps));
cmlCells = c(cmlIni,rep(0,times=numSteps));
regFeed = rep(0,times=numSteps);
cmlFeed = regFeed;
prob = timestep;
ran = rbinom(numSteps,400,prob);
for (i in 2:(numSteps+1))
{
regCells[i] = regCells[i-1];
cmlCells[i] = cmlCells[i-1];
regFeed[i-1] = 0;
cmlFeed[i-1] = 0;
if (ran[i-1] > 0)
{
for (j in 1:ran[i-1])
{
r = runif(1);
if (r < cmlCells[i]/(cmlCells[i] + regCells[i]))
{
cmlCells[i] = cmlCells[i] - 1;
cmlFeed[i-1] = cmlFeed[i-1] + 1;
}
else
{
regCells[i] = regCells[i] - 1;
regFeed[i-1] = regFeed[i-1] + 1;
}
r = runif(1);
if (r < cmlCells[i]/(cmlCells[i] + regCells[i]))
{
cmlCells[i] = cmlCells[i] + 1;
}
else
{
regCells[i] = regCells[i] + 1;
}
}
}
}
return(list(regCells,cmlCells,regFeed,cmlFeed,timestep));
}
stocs = function(regIni = round(stage1Cells*lam^(0:5)), cmlIni = rep(0,times=6), regFeed, cmlFeed, timestep = 0.0025)
{
numSteps = length(regFeed);
regFeedNext = rep(0,times=numSteps);
cmlFeedNext = regFeedNext;
regCells = c(regIni,rep(0,times=6*numSteps));
dim(regCells) = c(6,numSteps+1);
cmlCells = c(cmlIni,rep(0,times=6*numSteps));
dim(cmlCells) = c(6,numSteps+1);
prob = timestep*rStoc;
for (i in 2:(numSteps+1))
{
regCells[1:6,i] = regCells[1:6,(i-1)];
cmlCells[1:6,i] = cmlCells[1:6,(i-1)];
regCells[1,i] = regCells[1,i] + regFeed[i-1];
cmlCells[1,i] = cmlCells[1,i] + cmlFeed[i-1];
for (j in 1:5)
{
p = prob[j];
regTotalReps = rbinom(1,regCells[j,i-1],p);
cmlTotalReps = rbinom(1,cmlCells[j,i-1],p);
regDifferentiates = rbinom(1,regTotalReps,eps);
cmldifferentiates = rbinom(1,cmlTotalReps,epsCml);
regReplicates = regTotalReps - regDifferentiates;
cmlReplicates = cmlTotalReps - cmldifferentiates;
regCells[j,i] = regCells[j,i] + regReplicates - regDifferentiates;
cmlCells[j,i] = cmlCells[j,i] + cmlReplicates - cmldifferentiates;
regCells[j+1,i] = regCells[j+1,i] + 2*regDifferentiates;
cmlCells[j+1,i] = cmlCells[j+1,i] + 2*cmldifferentiates;
}
regTotalReps = rbinom(1,regCells[6,i-1],prob[6]);
cmlTotalReps = rbinom(1,cmlCells[6,i-1],prob[6]);
regDifferentiates = rbinom(1,regTotalReps,eps);
cmldifferentiates = rbinom(1,cmlTotalReps,epsCml);
regReplicates = regTotalReps - regDifferentiates;
cmlReplicates = cmlTotalReps - cmldifferentiates;
regCells[6,i] = regCells[6,i] + regReplicates - regDifferentiates;
cmlCells[6,i] = cmlCells[6,i] + cmlReplicates - cmldifferentiates;
regFeedNext[i-1] = 2*regDifferentiates;
cmlFeedNext[i-1] = 2*cmldifferentiates;
}
return(list(regCells,cmlCells,regFeedNext,cmlFeedNext,timestep));
}
diffEq = function(y,params)
{
primes = rep(0,times=24);
e = params[[1]];
r = params[[2]];
primes[1] = -(2*e-1)*r[1]*y[1];
primes[2:24] = 2*e*r[1:23]*y[1:23] - (2*e-1)*r[2:24]*y[2:24];
return(primes);
}
determs = function(regIni = c(round(stage1Cells*lam^(6:29)),0), cmlIni = rep(0,times=25), regFeed, cmlFeed, timestep = 0.0025)
{
numSteps = length(regFeed);
regParams = list(eps,rDeterm);
cmlParams = list(epsCml,rDeterm);
regCells = c(regIni,rep(0,times=25*numSteps));
dim(regCells) = c(25,numSteps+1);
cmlCells = c(cmlIni,rep(0,times=25*numSteps));
dim(cmlCells) = c(25,numSteps+1);
for (i in 2:(numSteps+1))
{
k1reg = timestep*diffEq(regCells[1:24,i-1],regParams);
k2reg = timestep*diffEq(regCells[1:24,i-1]+k1reg/2,regParams);
k3reg = timestep*diffEq(regCells[1:24,i-1]+k2reg/2,regParams);
k4reg = timestep*diffEq(regCells[1:24,i-1]+k3reg,regParams);
regCells[1:24,i] = regCells[1:24,i-1] + 1/6*(k1reg + 2*k2reg + 2*k3reg + k4reg);
regCells[1,i] = regCells[1,i] + regFeed[i-1];
k1cml = timestep*diffEq(cmlCells[1:24,i-1],cmlParams);
k2cml = timestep*diffEq(cmlCells[1:24,i-1]+k1cml/2,cmlParams);
k3cml = timestep*diffEq(cmlCells[1:24,i-1]+k2cml/2,cmlParams);
k4cml = timestep*diffEq(cmlCells[1:24,i-1]+k3cml,cmlParams);
cmlCells[1:24,i] = cmlCells[1:24,i-1] + 1/6*(k1cml + 2*k2cml + 2*k3cml + k4cml);
cmlCells[1,i] = cmlCells[1,i] + cmlFeed[i-1];
}
regCells[25,] = timestep*(2*eps)*rDeterm[24]*regCells[24,];
cmlCells[25,] = timestep*(2*epsCml)*rDeterm[24]*cmlCells[24,];
return(list(regCells,cmlCells,timestep));
}
simulate = function(regIni = c(399,round(stage1Cells*lam^(0:29)),0),cmlIni = c(1,rep(0,times=31)),period,timestep = 0.0025)
{
regStemCells = regIni[1];
regStocCells = regIni[2:7];
regDetermCells = regIni[8:32];
cmlStemCells = cmlIni[1];
cmlStocCells = cmlIni[2:7];
cmlDetermCells = cmlIni[8:32];
p = period;
t = timestep;
moranCells = moran(regIni = regStemCells,cmlIni = cmlStemCells,period = p,timestep = t);
stocCells = stocs(regIni = regStocCells,cmlIni = cmlStocCells,regFeed = moranCells[[3]],cmlFeed = moranCells[[4]],timestep = t);
determCells = determs(regIni = regDetermCells,cmlIni = cmlDetermCells,regFeed = stocCells[[3]],cmlFeed = stocCells[[4]],timestep = t);
return(list(rbind(moranCells[[1]],stocCells[[1]],determCells[[1]]),rbind(moranCells[[2]],stocCells[[2]],determCells[[2]])));
}
diagnosisSim = function(regIni = c(399,round(stage1Cells*lam^(0:30)),0),cmlIni = c(1,rep(0,times=32)),period=50,timestep = 0.0025,diagnoses = 1000,stop=0)
{
diagnosisCount = 0;
numTotalSims = 0;
leukStemCellCount = 0;
diagnosisTimes = rep(0,times = diagnoses);
regular = regIni;
leukemic = cmlIni;
while (diagnosisCount < diagnoses & (numTotalSims < stop | stop == 0))
{
numTotalSims = numTotalSims + 1;
reg1 = regular;
leuk1 = leukemic;
for(i in 1:floor(period/timestep))
{
s = simulate(regIni = reg1,cmlIni = leuk1,period = timestep,timestep = timestep);
regCells = s[[1]];
cmlCells = s[[2]];
r1 = regCells[,2];
l1 = cmlCells[,2];
if (leuk1[32] >= 10^12*365*timestep)
{
diagnosisCount = diagnosisCount + 1;
diagnosisTimes[diagnosisCount] = i*timestep;
if (leuk1[1] > 0)
{
leukStemCellCount = leukStemCellCount + 1;
}
break;
}
else if (sum(leuk1[1:7]) == 0 & l1[32] < leuk1[32])
{
break;
}
reg1 = r1;
leuk1 = l1;
}
}
return(list(diagnosisTimes,numTotalSims,leukStemCellCount));
} |
if (compareVersion(paste0(version$major, ".", version$minor), "3.6") < 0) {
skip("Randomization algorithm has changed from R 3.6")
}
data(keyATM_data_bills)
bills_dfm <- keyATM_data_bills$doc_dfm
bills_keywords <- keyATM_data_bills$keywords
bills_cov <- keyATM_data_bills$cov
bills_time_index <- keyATM_data_bills$time_index
labels_use <- keyATM_data_bills$labels
keyATM_docs <- keyATM_read(bills_dfm)
base <- keyATM(docs = keyATM_docs,
no_keyword_topics = 3,
keywords = bills_keywords,
model = "base",
options = list(seed = 250, store_theta = TRUE, iterations = 30,
store_pi = 1, use_weights = 1))
test_that("keyATM base", {
expect_s3_class(plot_alpha(base, start = 10), "keyATM_fig")
expect_s3_class(plot_pi(base, method = "eti"), "keyATM_fig")
skip_on_os("linux") ; skip_on_cran()
expect_equal(base$model_fit$Perplexity[3], 1861.29, tolerance = 0.00001)
expect_equal(top_words(base)[1, 1], "education [\U2713]")
expect_equal(top_words(base)[3, 1], "educational")
expect_equal(base$pi$Proportion[3], 6.403216, tolerance = 0.00001)
})
out <- keyATM(docs = keyATM_docs,
no_keyword_topics = 3,
keywords = bills_keywords[1],
model = "base",
options = list(seed = 250, iterations = 5))
test_that("keyATM onle one keyword topic", {
skip_on_os("linux") ; skip_on_cran()
expect_equal(out$model_fit$Perplexity[2], 3064.172, tolerance = 0.0001)
})
bills_keywords2 <- list(
Education = c("education", "child", "student"),
Law = c("court", "law", "attorney"),
Health = c("public", "health", "student"),
Drug = c("drug", "court")
)
out <- keyATM(docs = keyATM_docs,
no_keyword_topics = 3,
keywords = bills_keywords2,
model = "base",
options = list(seed = 250, store_theta = TRUE, iterations = 12,
thinning = 2, use_weights = 1))
test_that("keyATM overlapping keywords", {
skip_on_os("linux") ; skip_on_cran()
expect_equal(out$model_fit$Perplexity[2], 2283.335, tolerance = 0.0001)
expect_equal(top_words(out)[1, 1], "education [\U2713]")
expect_equal(top_words(out)[2, 5], "commission")
expect_equal(out$pi$Proportion[2], 4.750078, tolerance = 0.00001)
})
bills_keywords_multiple <- bills_keywords
bills_keywords_multiple$New <- c("public", "drug", "health")
bills_keywords_multiple$New2 <- c("law", "public", "health")
bills_keywords_multiple$Education <- c("education", "child", "student", "law")
out <- keyATM(docs = keyATM_docs,
no_keyword_topics = 0,
keywords = bills_keywords_multiple,
model = "base",
options = list(seed = 250, store_theta = TRUE, iterations = 10))
test_that("keyATM same keywords in multiple topics", {
skip_on_os("linux") ; skip_on_cran()
expect_equal(out$model_fit$Perplexity[2], 2246.162, tolerance = 0.0001)
expect_equal(top_words(out)[1, 1], "education [\U2713]")
expect_equal(top_words(out)[2, 5], "follow")
expect_equal(top_words(out)[9, 6], "law [\U2713]")
}) |
est_saeRB = function (formula, vardir, weight, samevar = FALSE, MAXITER = 100, PRECISION = 1E-04, data) {
if (!is.list(formula))
formula = list(formula)
r = length(formula)
if (r > 1)
stop("You should using est_msaeRB() for multivariate")
R_function = function (vardir, n, r) {
if (r == 1) {
R = diag(vardir)
} else {
R = matrix(rep(0, times = n*r*n*r), nrow = n*r, ncol = n*r)
k = 1
for (i in 1:r) {
for (j in 1:r) {
if (i <= j) {
mat0 = matrix(rep(0, times = r*r), nrow = r, ncol = r)
mat0[i, j] = 1
matVr = diag(vardir[, k], length(vardir[, k]))
R_hasil = kronecker(mat0, matVr)
R = R + R_hasil
k = k + 1
}
}
}
R = forceSymmetric(R)
}
return(as.matrix(R))
}
namevar = deparse(substitute(vardir))
nameweight = deparse(substitute(weight))
if (!missing(data)) {
formuladata = lapply(formula, function(x) model.frame(x, na.action = na.omit, data))
y = unlist(lapply(formula, function(x) model.frame(x, na.action = na.omit, data)[[1]]))
X = Reduce(adiag, lapply(formula, function(x) model.matrix(x, data)))
W = as.matrix(data[, nameweight])
n = length(y)/r
if (any(is.na(data[, namevar])))
stop("Object vardir contains NA values.")
if (any(is.na(data[, nameweight])))
stop("Object weight contains NA values.")
R = R_function(data[, namevar], n, r)
vardir = data[, namevar]
} else {
formuladata = lapply(formula, function(x) model.frame(x, na.action = na.omit))
y = unlist(lapply(formula, function(x) model.frame(x, na.action = na.omit)[[1]]))
X = Reduce(adiag, lapply(formula, function(x) model.matrix(x)))
W = as.matrix(weight)
n = length(y)/r
if (any(is.na(vardir)))
stop("Object vardir contains NA values")
if (any(is.na(weight)))
stop("Object weight contains NA values.")
R = R_function(vardir, n, r)
}
y_names = sapply(formula, "[[", 2)
Ir = diag(r)
In = diag(n)
dV = list()
dV1 = list()
for (i in 1:r){
dV[[i]] = matrix(0, nrow = r, ncol = r)
dV[[i]][i, i] = 1
dV1[[i]] = kronecker(dV[[i]], In)
}
convergence = TRUE
if (samevar) {
Vu = median(diag(R))
k = 0
diff = rep(PRECISION + 1, r)
while (any(diff > PRECISION) & (k < MAXITER)) {
k = k + 1
Vu1 = Vu
Gr = kronecker(Vu1, Ir)
Gn = kronecker(Gr, In)
V = as.matrix(Gn + R)
Vinv = solve(V)
XtVinv = t(Vinv %*% X)
Q = solve(XtVinv %*% X)
P = Vinv - t(XtVinv) %*% Q %*% XtVinv
Py = P %*% y
s = (-0.5) %*% sum(diag(P)) + 0.5 %*% (t(Py) %*% Py)
iF = 0.5 %*% sum(diag(P %*% P))
Vu = Vu1 + solve(iF) %*% s
diff = abs((Vu - Vu1)/Vu1)
}
Vu = as.vector((rep(max(Vu, 0), r)))
names(Vu) = y_names
if (k >= MAXITER && diff >= PRECISION) {
convergence = FALSE
}
Gn = kronecker(diag(Vu), In)
V = as.matrix(Gn + R)
Vinv = solve(V)
XtVinv = t(Vinv %*% X)
Q = solve(XtVinv %*% X)
P = Vinv - t(XtVinv) %*% Q %*% XtVinv
Py = P %*% y
beta = Q %*% XtVinv %*% y
res = y - X %*% beta
eblup = data.frame(matrix(X %*% beta + Gn %*% Vinv %*% res, n, r))
names(eblup) = y_names
se.b = sqrt(diag(Q))
t.value = beta/se.b
p.value = 2 * pnorm(abs(as.numeric(t.value)), lower.tail = FALSE)
coef = as.matrix(cbind(beta, se.b, t.value, p.value))
colnames(coef) = c("beta", "std. error", "t value", "p-value")
rownames(coef) = colnames(X)
coef = as.data.frame(coef)
} else {
Vu = apply(matrix(diag(R), nrow = n, ncol = r), 2, median)
k = 0
diff = rep(PRECISION + 1, r)
while (any(diff > rep(PRECISION, r)) & (k < MAXITER)) {
k = k + 1
Vu1 = Vu
if (r == 1) {
Gr = Vu1
} else {
Gr = diag(as.vector(Vu1))
}
Gn = kronecker(Gr, In)
V = as.matrix(Gn + R)
Vinv = solve(V)
XtVinv = t(Vinv %*% X)
Q = solve(XtVinv %*% X)
P = Vinv - t(XtVinv) %*% Q %*% XtVinv
Py = P %*% y
s = sapply(dV1, function(x) (-0.5) * sum(diag(P %*% x)) + 0.5 * (t(Py) %*% x %*% Py))
iF = matrix(unlist(lapply(dV1, function(x) lapply(dV1, function(y) 0.5 * sum(diag(P %*% x %*% P %*% y))))), r)
Vu = Vu1 + solve(iF) %*% s
diff = abs((Vu - Vu1)/Vu1)
}
Vu = as.vector(sapply(Vu, max, 0))
if (k >= MAXITER && diff >= PRECISION){
convergence = FALSE
}
if (r == 1) {
Gr = Vu1
} else {
Gr = diag(as.vector(Vu1))
}
Gn = kronecker(Gr, In)
V = as.matrix(Gn + R)
Vinv = solve(V)
XtVinv = t(Vinv %*% X)
Q = solve(XtVinv %*% X)
P = Vinv - t(XtVinv) %*% Q %*% XtVinv
Py = P %*% y
beta = Q %*% XtVinv %*% y
res = y - X %*% beta
eblup = data.frame(matrix(X %*% beta + Gn %*% Vinv %*% res, n, r))
names(eblup) = y_names
se.b = sqrt(diag(Q))
t.value = beta/se.b
p.value = 2 * pnorm(abs(as.numeric(t.value)), lower.tail = FALSE)
coef = as.matrix(cbind(beta, se.b, t.value, p.value))
colnames(coef) = c("beta", "std. error", "t value", "p-value")
rownames(coef) = colnames(X)
coef = as.data.frame(coef)
}
random.effect = data.frame(matrix(Gn %*% Vinv %*% res, n, r))
names(random.effect) = y_names
y.mat = matrix(y, nrow = n, ncol = r)
eblup.mat = as.matrix(eblup)
eblup.ratio = eblup.mat %*% (colSums(W * y.mat)/colSums(W * eblup.mat))
eblup.ratio = as.data.frame(eblup.ratio)
names(eblup.ratio) = y_names
agregation.direct = diag(t(W) %*% y.mat)
agregation.eblup = diag(t(W) %*% eblup.mat)
agregation.eblup.ratio = diag(t(W) %*% as.matrix(eblup.ratio))
agregation = as.matrix(rbind(agregation.direct, agregation.eblup, agregation.eblup.ratio))
colnames(agregation) = y_names
agregation = as.data.frame(agregation)
result = list(eblup = list(est.eblup = NA, est.eblupRB = NA), fit = list(method = NA, convergence = NA, iteration = NA, estcoef = NA, refvar = NA), random.effect = NA, agregation = NA)
result$eblup$est.eblup = eblup
result$eblup$est.eblupRB = eblup.ratio
result$fit$method = "REML"
result$fit$convergence = convergence
result$fit$iteration = k
result$fit$estcoef = coef
result$fit$refvar = t(Vu)
result$random.effect = random.effect
result$agregation = agregation
return(result)
} |
new_challenge <- function(path = ".", out_rmdfile = "challenge.rmd",
recursive = FALSE, overwrite = recursive,
quiet = FALSE, showWarnings = FALSE,
template = c("en", "fr"),
data_dir = "data",
submissions_dir = "submissions",
hist_dir = "history",
install_data = TRUE,
baseline = "baseline",
add_baseline = install_data,
clear_history = overwrite,
title = "Challenge",
author = "",
date = "",
email = "[email protected]",
date_start = format(Sys.Date(), "%d %b %Y"),
deadline = paste(Sys.Date()+90, "23:59:59"),
data_list = data_split(get_data("german"))) {
dir.create(path, recursive = recursive, showWarnings = showWarnings)
if (!file.exists(path))
stop("could not create directory ", path)
stopifnot(is.character(out_rmdfile), length(out_rmdfile)==1, nzchar(out_rmdfile))
stopifnot(is.character(template), nzchar(template))
template = match.arg(template, c("en", "fr"))
dir.create(file.path(path, "data"), recursive = recursive, showWarnings = showWarnings)
if (install_data) {
data_train <- data_list$train
data_test <- data_list$test
y_test <- data_list$y_test
ind_quiz <- data_list$ind_quiz
tmpdir = tempdir()
save('data_train', file = file.path(tmpdir, 'data_train.rda'))
file.copy(file.path(tmpdir, 'data_train.rda'), file.path(path, data_dir),
overwrite=overwrite, recursive=recursive)
save('data_test', file = file.path(tmpdir, 'data_test.rda'))
file.copy(file.path(tmpdir, 'data_test.rda'), file.path(path, data_dir),
overwrite=overwrite, recursive=recursive)
save('y_test', file = file.path(tmpdir, 'y_test.rda'))
file.copy(file.path(tmpdir, 'y_test.rda'), file.path(path, data_dir),
overwrite=overwrite, recursive=recursive)
save('ind_quiz', file = file.path(tmpdir, 'ind_quiz.rda'))
file.copy(file.path(tmpdir, 'ind_quiz.rda'), file.path(path, data_dir),
overwrite=overwrite, recursive=recursive)
unlink(tmpdir)
}
dir.create(file.path(path, submissions_dir), recursive = recursive, showWarnings = showWarnings)
if (install_data && add_baseline) {
team_dir = new_team(baseline, path=path, submissions_dir = submissions_dir,
quiet = TRUE, showWarnings = showWarnings)
y_pred <- rep("Good", nrow(data_test))
tmpfile = tempfile()
write(y_pred, file = tmpfile)
file.copy(tmpfile, file.path(team_dir, 'all_good.csv'), overwrite=overwrite)
y_pred <- rep("Bad", nrow(data_test))
write(y_pred, file = tmpfile)
file.copy(tmpfile, file.path(team_dir, 'all_bad.csv'), overwrite=overwrite)
unlink(tmpfile)
}
if (clear_history)
unlink(file.path(path, hist_dir), recursive = TRUE)
dir.create(file.path(path, hist_dir), recursive = recursive, showWarnings = showWarnings)
expr = list(title = title, author = author, date = date, email = email,
date_start = date_start, deadline = deadline, baseline = baseline,
data_dir = data_dir, submissions_dir = submissions_dir,
hist_dir = hist_dir)
tpl = system.file('template', paste0("challenge_", template, ".rmd"), package = 'rchallenge')
if (!nzchar(tpl))
stop("could not find template ", template)
text = readLines(tpl)
for (n in names(expr))
text = gsub(paste0("@", toupper(n), "@"), expr[[n]], text)
tmpfile = tempfile()
writeLines(text, tmpfile)
file.copy(tmpfile, file.path(path, out_rmdfile), overwrite=overwrite)
unlink(tmpfile)
if (!quiet) {
cat('New challenge installed in: "', normalizePath(path), '"\n', sep='')
cat('Next steps to complete the installation:\n')
step <- 0
if (install_data) {
step <- step + 1
cat(step, '. Replace the data files in the "data" subdirectory.\n', sep='')
}
if (add_baseline) {
step <- step + 1
cat(step, '. Replace the baseline predictions in "', file.path(submissions_dir, baseline),'".\n', sep='')
}
step <- step + 1
cat(step, '. Customize the template R Markdown file "', out_rmdfile, '" as needed.\n', sep='')
step <- step + 1
cat(step, '. Create and share subdirectories in "', submissions_dir, '" for each team:\n', sep='')
cat(' rchallenge::new_team("team_foo", "team_bar", path="', path, '", submissions_dir="', submissions_dir, '")\n', sep='')
step <- step + 1
cat(step, '. Render the HTML page:\n', sep='')
cat(' rchallenge::publish("', file.path(path, out_rmdfile), '")\n', sep='')
step <- step + 1
template_html <- paste0(sub("([^.]+)\\.[[:alnum:]]+$", "\\1", basename(out_rmdfile)), ".html")
cat(step, '. Give the URL to "', template_html, '" to the participants.\n', sep='')
step <- step + 1
cat(step, '. Automate the updates of the webpage.\n', sep='')
if (.Platform$OS.type == "unix") {
cat(' On Unix systems, you can setup the following line to your crontab using "crontab -e":\n', sep='')
cat(' 0 * * * * Rscript -e \'rchallenge::publish("', normalizePath(file.path(path, out_rmdfile)), '")\'\n', sep='')
}
if (.Platform$OS.type == "windows") {
cat(' On Windows systems, you can use the Task Scheduler to create a new task with a "Start a program" action with the settings:')
cat(' - Program/script: Rscript.exe\n')
cat(' - options: -e rchallenge::publish(\'', normalizePath(file.path(path, out_rmdfile)), '\')\n', sep='')
}
}
invisible(normalizePath(path))
}
new_team <- function(..., path = ".", submissions_dir = "submissions",
quiet = FALSE, showWarnings = FALSE) {
names <- c(...)
stopifnot(is.character(names))
if (!file.exists(file.path(path, submissions_dir)))
stop("could not find submissions directory:", normalizePath(file.path(path, submissions_dir)))
for (i in seq_along(names)) {
if (!quiet) cat("Creating team subdirectory:", file.path(submissions_dir, names[i]), "\n")
dir.create(file.path(path, submissions_dir, names[i]), recursive = FALSE, showWarnings = showWarnings)
}
if (!quiet) cat("Next step: share the Dropbox folders with the corresponding teams.\n")
invisible(normalizePath(file.path(path, submissions_dir, names)))
}
publish <- function(input="challenge.rmd", output_file = "index.html",
output_dir = dirname(input),
quiet = FALSE, ...) {
wd <- getwd()
setwd(dirname(input))
out <- rmarkdown::render(input = basename(input), output_file = output_file,
output_dir = output_dir, quiet = quiet, ...)
setwd(wd)
if (!quiet)
cat('Next step: give the URL to "', file.path(output_dir, basename(out)), '" to the participants.\n', sep='')
invisible(out)
} |
`orglsegments` <-
function (object, groups, order.by, display = "sites", choices = 1:3,
col = "black", ...)
{
pts <- scores(object, display = display, choices = choices, ...)
if (!missing(order.by)) {
if (length(order.by) != nrow(pts))
stop(gettextf("the length of order.by (%d) does not match the number of points (%d)",
length(order.by), nrow(pts)))
ord <- order(order.by)
pts <- pts[ord,]
groups <- groups[ord]
}
inds <- names(table(groups))
if (is.factor(col))
col <- as.numeric(col)
col <- rep(col, length = length(inds))
names(col) <- inds
for (is in inds) {
X <- pts[groups == is, , drop = FALSE]
if (nrow(X) > 1) {
for (i in 2:nrow(X)) {
rgl.lines(c(X[i-1,1],X[i,1]), c(X[i-1,2],X[i,2]),
c(X[i-1,3],X[i,3]), col = col[is], ...)
}
}
}
invisible()
} |
project_waypoints <- function(
trajectory,
space,
waypoints = select_waypoints(trajectory),
trajectory_projection_sd = sum(trajectory$milestone_network$length) * 0.05
) {
assert_that(!is.null(space))
if ("geodesic_distances" %in% names(waypoints)) {
assert_that(all(rownames(space) %in% colnames(waypoints$geodesic_distances)))
} else {
waypoints$geodesic_distances <- calculate_geodesic_distances(
trajectory,
waypoint_milestone_percentages = waypoints$milestone_percentages
)[unique(waypoints$milestone_percentages$waypoint_id), , drop = FALSE]
}
weights <- waypoints$geodesic_distances %>%
stats::dnorm(sd = trajectory_projection_sd)
assert_that(all(!is.na(weights)))
weights <- weights / rowSums(weights)
weights <- weights[, rownames(space)]
assert_that(all(colnames(weights) == rownames(space)))
projected_space <- weights %*% space
projected_space
}
project_trajectory <- function(
trajectory,
dimred,
waypoints = select_waypoints(trajectory),
trajectory_projection_sd = sum(trajectory$milestone_network$length) * 0.05
) {
dimred_segment_points <- project_waypoints(
trajectory,
dimred,
waypoints = waypoints,
trajectory_projection_sd = trajectory_projection_sd
)
dimred_milestones <- project_milestones(
trajectory,
dimred,
trajectory_projection_sd = trajectory_projection_sd
)
lst(
dimred_segment_points = dimred_segment_points,
dimred_segment_progressions = waypoints$progressions %>% select(from, to, percentage),
dimred_milestones = dimred_milestones
)
}
project_milestones <- function(trajectory, dimred, trajectory_projection_sd = sum(trajectory$milestone_network$length) * 0.05) {
waypoints <- lst(
milestone_percentages = tibble(
waypoint_id = trajectory$milestone_ids,
milestone_id = waypoint_id,
percentage = 1
)
)
project_waypoints(
trajectory,
dimred,
waypoints = waypoints,
trajectory_projection_sd = trajectory_projection_sd
)
} |
test_that("ibdsim() returns objects of correct class", {
x = nuclearPed(1)
sim = ibdsim(x, N=1, verbose=F, map=uniformMap(1), model="haldane")
expect_is(sim, "genomeSimList")
})
test_that("Full sib mating yields all 9 jaquard states", {
x = fullSibMating(1)
sim = ibdsim(x, N=1, ids = 5:6, map=uniformMap(M=10), verbose=F, seed=1)[[1]]
expect_setequal(sim[, 'Sigma'], 1:9)
}) |
prism_archive_clean <- function(type, temp_period, years = NULL, mon = NULL,
minDate = NULL, maxDate = NULL, dates = NULL) {
prism_check_dl_dir()
pd <- prism_archive_subset(type, temp_period, years = years, mon = mon,
minDate = minDate, maxDate = maxDate, dates = dates)
early <- pd[stringr::str_detect(pd, "_early_")]
delete <- c()
if (length(early) > 0) {
prov <- stringr::str_replace(early, "_early_", "_provisional_")
stable <- stringr::str_replace(early, "_early_", "_stable_")
delete <- early[file.exists(file.path(prism_get_dl_dir(), prov))]
delete2 <- early[file.exists(file.path(prism_get_dl_dir(), stable))]
delete <- unique(c(delete, delete2))
}
prov <- pd[stringr::str_detect(pd, "_provisional_")]
delete2 <- c()
if (length(prov) > 0) {
stable <- stringr::str_replace(prov, "_provisional_", "_stable_")
delete2 <- prov[file.exists(file.path(prism_get_dl_dir(), stable))]
}
delete <- unique(c(delete, delete2))
if (length(delete) == 0) {
return(invisible(NULL))
}
delete <- folders_to_remove(delete)
del_paths <- file.path(prism_get_dl_dir(), delete)
unlink(del_paths, recursive = TRUE)
del_i <- dir.exists(del_paths)
no_delete <- delete[del_i]
delete <- delete[!del_i]
if (length(no_delete) > 0) {
warning(
paste0(
"Unable to remove the following folders. Check permissions.",
paste("\n -", no_delete, collapse = "\n - ")
)
)
}
invisible(delete)
}
folders_to_remove <- function(x) {
if (interactive()) {
msg <- "Please select the PRISM folders to remove:\n"
choices <- paste("1: None", "2: All", sep = "\n")
i <- 2
if (length(x) > 25) {
msg <- paste0(
msg, "There are ", length(x),
" folders to be removed, but only the first 25 are options.\n"
)
choices <- paste(
"1: None", "2: Only the 25 shown below.",
"3: All (even those not listed below).", sep = "\n"
)
i <- 3
}
msg <- paste0(msg, "\n", choices)
n <- min(25, length(x))
choices <- paste(paste0(seq(n) + i, ": ", x[seq(n)]), collapse = "\n")
msg <- paste0(msg, "\n", choices, "\n")
cat(msg)
ui <- readline("Enter choices seperated by commas: ")
ui <- as.numeric(stringr::str_split(
stringr::str_remove_all(ui, " "),
",",
simplify = TRUE
))
if (1 %in% ui) {
x <- NULL
message("No folders will be removed.\n")
} else {
del_i <- ui - i
if (-1 %in% del_i) {
x <- x[1:25]
}
else if (!(0 %in% del_i)) {
x <- x[del_i]
}
}
}
x
}
del_early_prov <- function (type, minDate = NULL, maxDate = NULL, dates = NULL)
{
.Deprecated("`prism_archive_clean()`")
prism_check_dl_dir()
dates <- gen_dates(minDate = minDate, maxDate = maxDate,
dates = dates)
pd <- prism_archive_subset(type = type, temp_period = "daily", dates = dates)
mddf <- pd_get_md(pd)
mddf$dates_str <- stringr::str_extract(mddf$PRISM_DATASET_FILENAME,
"[0-9]{8}")
duplicates <- mddf$dates_str[duplicated(mddf$dates_str)]
out <- NULL
for (dup in duplicates) {
dups <- mddf[mddf$dates_str == dup, ]
dups$dates_num <- as.numeric(dups$dates_str)
dups$priority <- sapply(dups$folder_path, function(x){
if(stringr::str_detect(x, "stable")){
return(1)
} else if (stringr::str_detect(x, "provisional")){
return(2)
} else if (stringr::str_detect(x, "early")){
return(3)
}
})
dups$to_delete <- dups$priority > min(dups$priority)
if(sum(!dups$to_delete) > 1){
dups$new_to_delete <- dups$to_delete
dups$new_to_delete[!dups$to_delete] <- TRUE
ll <- length(dups$to_delete[!dups$to_delete])
dups$new_to_delete[!dups$to_delete][ll] <- FALSE
dups$to_delete <- dups$new_to_delete
}
unlink(dups$folder_path[dups$to_delete], recursive = TRUE)
tmp <- lapply(dups$folder_path[dups$to_delete], function(x){
if(file.exists(x)) {
warning(paste0("Unable to remove folder ", x, ". Check permissions."))
} else {
x
}
})
out <- c(out, unlist(tmp))
}
invisible(out)
} |
context("test_prepare_number_args")
test_that("prepare number throws appropriate errors on vectors", {
expect_error(prepare_number(c("start", "end")),
regexp = "Only a single number can be passed")
expect_error(prepare_number(c(1234, 5678)),
regexp = "Only a single number can be passed")
})
iris <- data("iris")
test_that("prepare number throws appropriate errors on bad coercion", {
expect_error(prepare_number("start"),
regexp = "NAs were introduced.")
expect_error(prepare_number(iris),
regexp = "NAs were introduced.")
})
test_that("prepare number warns on logicals", {
expect_warning(prepare_number(TRUE),
regexp = "Coercing logical values to numeric.")
expect_warning(prepare_number(FALSE),
regexp = "Coercing logical values to numeric.")
})
rm(iris) |
interactivity <- function(linkingGroup = NULL,
linkingKey = NULL,
linkedStates = NULL,
sync = NULL,
active = NULL,
activeGeomLayers = NULL,
selected = NULL,
selectBy = NULL,
selectionLogic = NULL,
layerId = NULL,
scaleToFun = NULL,
itemLabel = NULL,
showItemLabels = NULL,
...) {
ggplot2::ggproto("Interactivity", NULL,
params = list(
linkingGroup = linkingGroup,
linkingKey = linkingKey,
linkedStates = linkedStates,
sync = sync,
active = active,
activeGeomLayers = activeGeomLayers,
selected = selected,
selectBy = selectBy,
selectionLogic = selectionLogic,
layerId = layerId,
scaleToFun = scaleToFun,
itemLabel = itemLabel,
showItemLabels = showItemLabels,
...
),
remove_null = function(..., as_list = TRUE) {
if(as_list)
Filter(Negate(is.null),
list(...)
)
else
Filter(Negate(is.null), ...)
},
warn_nDim_states = function(data, x) {
n <- nrow(data) %||% 0
if(length(x) != n && length(x) != 1) {
warning(
"The length of ", deparse(substitute(x)),
" is ", length(x),
" that does not match the number of observations(" ,
n, ").",
call. = FALSE
)
}
},
evalObj = function(data, params, x) {
obj <- params[[x]]
if(is.null(obj)) return(params)
if(rlang::is_formula(obj)) {
params[[x]] <- eval(obj[[2]], data)
} else if (is.atomic(obj)) {
NULL
} else {
params[[x]] <- tryCatch(
expr = {eval(obj, data)},
error = function(e) {
return(NULL)
}
)
}
return(params)
},
check_itemLabel = function(self, data, params) {
if(!is.null(params$showItemLabels) && !is.logical(params$showItemLabels)) {
stop("`showItemLabels` must be logical", call. = FALSE)
}
params <- self$evalObj(data, params, "itemLabel")
itemLabel <- params$itemLabel
if(is.null(itemLabel)) return(NULL)
self$warn_nDim_states(data = data,
x = itemLabel)
self$params <- params
},
check_selected = function(self, data, params) {
params <- self$evalObj(data, params, "selected")
selected <- params$selected
if(is.null(selected)) return(NULL)
if(!is.logical(selected)) {
stop("`selected` must be logical", call. = FALSE)
}
self$warn_nDim_states(data = data,
x = selected)
self$params <- params
},
check_active = function(self, data, params) {
params <- self$evalObj(data, params, "active")
active <- params$active
if(is.null(active)) return(NULL)
if(!is.logical(active)) {
stop("`active` must be logical", call. = FALSE)
}
self$warn_nDim_states(data = data,
x = active)
self$params <- params
},
check_linkingKey = function(self, data, params) {
params <- self$evalObj(data, params, "linkingKey")
linkingKey <- params$linkingKey
if(is.null(linkingKey)) return(NULL)
if(any(duplicated(linkingKey)))
stop("The linkingKey is duplicated", call. = FALSE)
self$warn_nDim_states(data = data,
x = linkingKey)
self$params <- params
}
)
}
linking <- function(linkingGroup = NULL,
linkingKey = NULL,
linkedStates = NULL,
sync = NULL) {
interactivity(linkingGroup = linkingGroup,
linkingKey = linkingKey,
linkedStates = linkedStates,
sync = sync)
}
active <- function(active = NULL,
activeGeomLayers = NULL) {
interactivity(active = active,
activeGeomLayers = activeGeomLayers)
}
selection <- function(selected = NULL,
selectBy = NULL,
selectionLogic = NULL) {
interactivity(selected = selected,
selectBy = selectBy,
selectionLogic = selectionLogic)
}
zoom <- function(layerId = NULL,
scaleToFun= NULL) {
interactivity(layerId = layerId,
scaleToFun = scaleToFun)
}
hover <- function(itemLabel = NULL,
showItemLabels = NULL) {
interactivity(itemLabel = itemLabel,
showItemLabels = showItemLabels)
}
ggplot_add.Interactivity <- function(object, plot, object_name) {
if(!is.l_ggplot(plot)) {
class(plot) <- c("l_ggplot", class(plot))
}
update_interactivity(plot, object)
}
update_interactivity <- function(p, interactivity) {
p$interactivity <- merge_interactivity(interactivity, p$interactivity)
p
}
merge_interactivity <- function (new, old) {
if(is.null(new)) return(old)
pre <- find(old = old$params,
new = new$params)
if(any(pre)) {
pre_name <- names(pre)[which(pre)]
message_wrap("Interactive component for '", pre_name,
"' is already present. Adding another interactive component for '", pre_name,
"', which will replace the existing one.")
}
new$params <- replace(new = new$params,
old = old$params)
new
}
find <- function(new, old) {
old <- remove_null(old, as_list = FALSE)
new <- remove_null(new, as_list = FALSE)
if(length(new) == 0) return(FALSE)
vapply(names(new), function(param) any(names(old) %in% param), logical(1))
}
replace <- function(new, old) {
old <- remove_null(old, as_list = FALSE)
new <- remove_null(new, as_list = FALSE)
if(length(new) == 0 && length(old) == 0) return(list())
states <- unique(c(names(old), names(new)))
stats::setNames(
lapply(states,
function(state) {
if(state %in% names(new)) {
new[[state]]
} else {
old[[state]]
}
}),
states
)
}
params_check <- function(interactivity, data) {
interactivity$check_selected(data, interactivity$params)
interactivity$check_itemLabel(data, interactivity$params)
interactivity$check_linkingKey(data, interactivity$params)
interactivity$check_active(data, interactivity$params)
} |
.buiCalc <- function(dmc, dc) {
bui1 <- ifelse(dmc == 0 & dc == 0, 0, 0.8 * dc * dmc/(dmc + 0.4 * dc))
p <- ifelse(dmc == 0, 0, (dmc - bui1)/dmc)
cc <- 0.92 + ((0.0114 * dmc)^1.7)
bui0 <- dmc - cc * p
bui0 <- ifelse(bui0 < 0, 0, bui0)
bui1 <- ifelse(bui1 < dmc, bui0, bui1)
return(bui1)
} |
print.coef.gnm <- function(x, ...) {
if (!is.null(attr(x, "ofInterest"))) {
if (length(attr(x, "ofInterest"))){
cat("Coefficients of interest:\n", sep = "")
print.default(format(x[attr(x, "ofInterest")]), quote = FALSE)
}
else
cat("No coefficients of interest\n")
}
else {
cat("Coefficients:\n")
print.default(format(x), quote = FALSE)
}
} |
sim2.bd.fast.age <-
function(n,numbsim,lambda,mu,rho,age,mrca=FALSE){ phy <- list()
for (j in 1:numbsim){
if (mrca==FALSE) {
temp1 <- sim2.bd.fast.single.origin(n,lambda,mu,rho,age)
temp<-collapse.singles(temp1)
} else {
temp <- sim2.bd.fast.single.mrca(n,lambda,mu,rho,age)
}
phy <- c(phy, list(temp))
}
phy
} |
open_curly_linter <- function(allow_single_line = FALSE) {
Linter(function(source_file) {
lapply(
ids_with_token(source_file, "'{'"),
function(id) {
parsed <- with_id(source_file, id)
tokens_before <- source_file$parsed_content$token[
source_file$parsed_content$line1 == parsed$line1 &
source_file$parsed_content$col1 < parsed$col1]
tokens_after <- source_file$parsed_content$token[
source_file$parsed_content$line1 == parsed$line1 &
source_file$parsed_content$col1 > parsed$col1 &
source_file$parsed_content$token != "COMMENT"]
if (isTRUE(allow_single_line) &&
"'}'" %in% tokens_after) {
return()
}
line <- source_file$lines[as.character(parsed$line1)]
some_before <- length(tokens_before) %!=% 0L
some_after <- length(tokens_after) %!=% 0L
content_after <- unname(substr(line, parsed$col1 + 1L, nchar(line)))
content_before <- unname(substr(line, 1, parsed$col1 - 1L))
only_comment <- rex::re_matches(content_after, rex::rex(any_spaces, "
double_curly <- rex::re_matches(content_after, rex::rex(start, "{")) ||
rex::re_matches(content_before, rex::rex("{", end))
if (double_curly) {
return()
}
whitespace_after <-
unname(substr(line, parsed$col1 + 1L, parsed$col1 + 1L)) %!=% ""
if (!some_before ||
some_after ||
(whitespace_after && !only_comment)) {
Lint(
filename = source_file$filename,
line_number = parsed$line1,
column_number = parsed$col1,
type = "style",
message = paste(
"Opening curly braces should never go on their own line and",
"should always be followed by a new line."
),
line = line
)
}
}
)
})
} |
read_xmile <- function(filepath, stock_list = NULL, const_list = NULL) {
model_structure <- extract_structure_from_XMILE(filepath)
if(!is.null(stock_list)) {
stocks_override <- names(stock_list)
lvl_names <- sapply(model_structure$levels,
function(lvl_obj) lvl_obj$name)
for(i in seq_len(length(stock_list))) {
stk <- stocks_override[[i]]
pos_stk <- which(stk == lvl_names)
model_structure$levels[[pos_stk]]$initValue <- stock_list[[stk]]
}
}
if(!is.null(const_list)) {
model_structure <- override_consts(model_structure, const_list)
}
deSolve_components <- get_deSolve_elems(model_structure)
igraph_inputs <- tryCatch(
error = function(cnd) {
warning("This model cannot be converted into a graph (network)",
call. = FALSE)
NULL
},
get_igraph_inputs(model_structure)
)
list(
description = model_structure,
deSolve_components = deSolve_components,
graph_dfs = igraph_inputs
)
}
xmile_to_deSolve <- function(filepath) {
model_structure <- extract_structure_from_XMILE(filepath)
deSolve_components <- get_deSolve_elems(model_structure)
}
override_consts <- function(mdl_structure, const_list) {
consts_override <- names(const_list)
const_names <- sapply(mdl_structure$constants,
function(const_obj) const_obj$name)
for(i in seq_len(length(const_list))) {
cst <- consts_override[[i]]
pos_cst <- which(cst == const_names)
if(length(pos_cst) == 0) {
msg <- paste0("Can't find constant: ", cst)
stop(msg, call. = FALSE)
}
mdl_structure$constants[[pos_cst]]$value <- const_list[[cst]]
}
mdl_structure
} |
format_splits <- function(distance, time) {
df <- data.frame(distance = distance, time = time)
create_lag <- function(x) {
x_lag <- x
for (i in seq(1, length(x) - 1)) {
x_lag[i + 1] <- x[i]
}
x_lag[1] <- 0
return(x_lag)
}
df <- df[order(df$distance), ]
df$split_distance_start <- create_lag(df$distance)
df$split_distance_stop <- df$distance
df$split_distance <- df$split_distance_stop - df$split_distance_start
df$split_time_start <- create_lag(df$time)
df$split_time_stop <- df$time
df$split_time <- df$split_time_stop - df$split_time_start
df$split_mean_velocity <- df$split_distance / df$split_time
df$split <- seq(1, nrow(df))
df <- df[c(
"split",
"split_distance_start",
"split_distance_stop",
"split_distance",
"split_time_start",
"split_time_stop",
"split_time",
"split_mean_velocity"
)]
return(df)
} |
testthat::context("as_rdf") |
if(require("suppdata") & require("testthat")){
context("PLOS")
test_that("PLOS works with minimal input", {
skip_on_cran()
expect_true(file.exists(suppdata("10.1371/journal.pone.0127900", 1)))
})
test_that("PLOS works specifying 'from'", {
skip_on_cran()
expect_true(file.exists(suppdata("10.1371/journal.pone.0127900", 1, "plos")))
})
test_that("PLOS fails with character SI info", {
skip_on_cran()
expect_error(suppdata("10.1371/journal.pone.0127900", "999"), "numeric SI info")
})
test_that("PLOS fails with unknown journal SI info", {
skip_on_cran()
expect_error(suppdata:::.suppdata.plos("10.1111/ele.12437", 1), "Unrecognised PLoS journal")
})
} |
DT_GA_C <- function(train, test, confidence=0.25, instancesPerLeaf=2, geneticAlgorithmApproach="GA-LARGE-SN", threshold=10, numGenerations=50, popSize=200, crossoverProb=0.8, mutProb=0.01, seed=-1){
alg <- RKEEL::R6_DT_GA_C$new()
alg$setParameters(train, test, confidence, instancesPerLeaf, geneticAlgorithmApproach, threshold, numGenerations, popSize, crossoverProb, mutProb, seed)
return (alg)
}
R6_DT_GA_C <- R6::R6Class("R6_DT_GA_C",
inherit = ClassificationAlgorithm,
public = list(
confidence = 0.25,
instancesPerLeaf = 2,
geneticAlgorithmApproach = "GA-LARGE-SN",
threshold = 10,
numGenerations = 50,
popSize = 200,
crossoverProb = 0.8,
mutProb = 0.01,
seed = -1,
setParameters = function(train, test, confidence=0.25, instancesPerLeaf=2,
geneticAlgorithmApproach="GA-LARGE-SN", threshold=10,
numGenerations=50, popSize=200, crossoverProb=0.8,
mutProb=0.01, seed=-1){
super$setParameters(train, test)
stopText <- ""
if((hasMissingValues(train)) || (hasMissingValues(test))){
stopText <- paste0(stopText, "Dataset has missing values and the algorithm does not accept it.\n")
}
if(stopText != ""){
stop(stopText)
}
self$confidence <- confidence
self$instancesPerLeaf <- 2
if((tolower(geneticAlgorithmApproach) == "ga-small") || (tolower(geneticAlgorithmApproach) == "ga-large-sn")){
self$geneticAlgorithmApproach <- toupper(geneticAlgorithmApproach)
}
else{
self$geneticAlgorithmApproach <- "GA-LARGE-SN"
}
self$threshold <- threshold
self$numGenerations <- numGenerations
self$popSize <- popSize
self$crossoverProb <- crossoverProb
self$mutProb <- mutProb
if(seed == -1) {
self$seed <- sample(1:1000000, 1)
}
else {
self$seed <- seed
}
}
),
private = list(
jarName = "DT_GA.jar",
algorithmName = "DT_GA-C",
algorithmString = "Hybrid Decision Tree/Genetic Algorithm",
getParametersText = function(){
text <- ""
text <- paste0(text, "seed = ", self$seed, "\n")
text <- paste0(text, "confidence = ", self$confidence, "\n")
text <- paste0(text, "isntancesPerLeaf = ", self$instancesPerLeaf, "\n")
text <- paste0(text, "Genetic Algorithm Approach = ", self$geneticAlgorithmApproach, "\n")
text <- paste0(text, "Threshold S to consider a Small Disjunt = ", self$threshold, "\n")
text <- paste0(text, "Number of Total Generations for the GA = ", self$numGenerations, "\n")
text <- paste0(text, "Number of chromosomes in the population = ", self$popSize, "\n")
text <- paste0(text, "Crossover Probability = ", self$crossoverProb, "\n")
text <- paste0(text, "Mutation Probability = ", self$mutProb, "\n")
return(text)
}
)
) |
n_eff <- function (kinship, max = TRUE, weights = NULL, nonneg = TRUE, algo = c('gradient', 'newton', 'heuristic'), tol = 1e-10) {
algo <- match.arg(algo)
if (missing(kinship))
stop('`kinship` matrix is required!')
validate_kinship(kinship)
n_ind <- ncol( kinship )
if (max) {
inverse_kinship <- solve(kinship)
weights <- rowSums( inverse_kinship )
if (min(weights) < 0 && nonneg) {
if (algo == 'gradient') {
obj <- n_eff_max_gradient(kinship, tol = tol)
} else if (algo == 'newton') {
obj <- n_eff_max_newton(kinship, tol = tol)
} else if (algo == 'heuristic') {
obj <- n_eff_max_heuristic(kinship, weights)
} else
stop('algorithm not implemented: ', algo)
} else {
n_eff <- sum( inverse_kinship )
weights <- weights / sum(weights)
obj <- list(n_eff = n_eff, weights = weights)
}
} else {
mean_kin <- mean_kinship(kinship, weights)
n_eff <- 1 / mean_kin
weights <- weights / sum(weights)
obj <- list(n_eff = n_eff, weights = weights)
}
if ( obj$n_eff < 1 ) {
obj$n_eff <- 1
} else if ( obj$n_eff > 2 * n_ind ) {
obj$n_eff <- 2 * n_ind
}
return(obj)
} |
Gupper<-function(theta,y,n,j)
{
if(j==length(y)) return (pbinom(q=y[j],size=n[j],prob=theta))
pbinom(q=y[j]-1,size=n[j],prob=theta)+dbinom(x=y[j],size=n[j],prob=theta)*Gupper(theta=theta,y=y,n=n,j=j+1)
}
Glower<-function(theta,y,n,j)
{
if(j==1) return (pbinom(q=y[j]-1,size=n[j],prob=theta,lower.tail=FALSE))
pbinom(q=y[j],size=n[j],prob=theta,lower.tail=FALSE)+dbinom(x=y[j],size=n[j],prob=theta)*Glower(theta=theta,y=y,n=n,j=j-1)
}
morrisUCL<-function(y,n,halfa=0.05)
{
m=length(y)
if(length(n)!=m) stop("Mismatched lengths in Morris.\n")
uout=rep(1,m)
a=m
while((y[a]==n[a] || n[a]==0) && a>=1) a=a-1
if(a<1) return(uout)
for(b in a:1)
{
uout[b]=uniroot(function(theta,h,d,alpha,...) h(theta=theta,...)-alpha,interval=c(0,1),
alpha=halfa,j=b,h=Gupper,n=n,y=y)$root
}
if(any(n==0))
{
uout = approx((1:m)[n>0],uout[n>0],xout=1:m,rule=2)$y
}
return(uout)
}
morrisLCL<-function(y,n,halfa=0.05)
{
m=length(y)
if(length(n)!=m) stop("Mismatched lengths in Morris.\n")
uout=rep(0,m)
a=1
while((y[a]==0 || n[a]==0) && a<=m) a=a+1
if(a>m) return(uout)
for(b in a:m)
{
uout[b]=uniroot(function(theta,h,d,alpha,...) h(theta=theta,...)-alpha,interval=c(0,1),
alpha=halfa,j=b,h=Glower,n=n,y=y)$root
}
if(any(n==0))
{
uout = approx((1:m)[n>0],uout[n>0],xout=1:m,rule=2)$y
}
return(uout)
}
morrisCI<-function(y,n,phat=y/n,conf=0.9,narrower=TRUE,alternate=wilsonCI,...)
{
if(conf<=0 || conf>=1) stop("Confidence must be between 0 and 1.\n")
tailp=(1-conf)/2
lcl=morrisLCL(y=y,n=n,halfa=tailp)
ucl=morrisUCL(y=y,n=n,halfa=tailp)
if(narrower)
{
relevants=which(n>0)
altout=alternate(phat=phat[relevants],n=n[relevants],conf=conf,...)
lcl[relevants]=cummax(pmax(lcl[relevants],altout[,1]))
ucl[relevants]=rev(cummin(rev(pmin(ucl[relevants],altout[,2]))))
}
return(cbind(lcl,ucl))
}
|
BayesSUR <- function(data = NULL, Y, X, X_0 = NULL,
covariancePrior = "HIW", gammaPrior = "hotspot", betaPrior = "independent",
nIter = 10000, burnin = 5000, nChains = 2,
outFilePath = "", gammaSampler = "bandit", gammaInit = "R", mrfG = NULL,
standardize = TRUE, standardize.response = TRUE, maxThreads = 1,
output_gamma = TRUE, output_beta = TRUE, output_Gy = TRUE, output_sigmaRho = TRUE,
output_pi = TRUE, output_tail = TRUE, output_model_size = TRUE, output_model_visit = FALSE,
output_CPO = FALSE, output_Y = TRUE, output_X = TRUE, hyperpar = list(), tmpFolder = "tmp/")
{
if(outFilePath == "")
stop("Please specify a directory to save all output files!")
outFilePathLength = nchar(outFilePath)
if( substr(outFilePath,outFilePathLength,outFilePathLength) != "/" )
outFilePath = paste( outFilePath , "/" , sep="" )
if(!file.exists(outFilePath))
dir.create(outFilePath)
tmpFolderLength = nchar(tmpFolder)
if( substr(tmpFolder,tmpFolderLength,tmpFolderLength) != "/" )
tmpFolder = paste( tmpFolder , "/" , sep="" )
tmpFolder = paste(outFilePath, tmpFolder, sep="")
if(!file.exists(tmpFolder))
dir.create(tmpFolder)
cl <- match.call()
if ( is.null( data ) ){
npY = dim(Y)
if ( (!is.numeric(Y)) | is.null(npY) )
my_stop("If 'data' is NULL, Y should be a numeric matrix",tmpFolder)
npX = dim(X)
if ( (!is.numeric(X)) | is.null(npX) | (npX[1]!=npY[1]) )
my_stop("If 'data' is NULL, X should be a numeric matrix and the same number of rows of Y",tmpFolder)
if ( is.null ( X_0 ) ){
X_0 = matrix(NA,nrow=npY[1],ncol=0)
}else{
npX0 = dim(X_0)
if ( (!is.numeric(X_0)) | is.null(npX0) | (npX0[1]!=npY[1]) )
my_stop("If 'data' is NULL and X_0 is provided, X_0 should be a numeric matrix and the same number of rows of Y",tmpFolder)
}
if( standardize ){
X = scale(X)
X_0 = scale(X_0)
}
if( standardize.response ) Y = scale(Y)
write.table(cbind(Y,X,X_0),paste(sep="",tmpFolder,"data.txt"), row.names = FALSE, col.names = FALSE)
data = paste(sep="",tmpFolder,"data.txt")
blockLabels = c( rep(0,ncol(Y)) , rep(1,ncol(X)) , rep(2,ncol(X_0)) )
write.table(Y,paste(sep="",outFilePath,"data_Y.txt"), row.names = FALSE, col.names = TRUE)
write.table(X,paste(sep="",outFilePath,"data_X.txt"), row.names = FALSE, col.names = TRUE)
write.table(X_0,paste(sep="",outFilePath,"data_X0.txt"), row.names = FALSE, col.names = TRUE)
}else{
npData = dim(data)
if( is.numeric(data) | (!is.null(npData)) | (npData[2]>=2) )
{
if( standardize ){
data[,X]= scale(data[,X])
data[,X_0] = scale(data[,X_0])
}
if( standardize.response ) data[,Y] = scale(data[,Y])
write.table(data[,Y],paste(sep="",outFilePath,"data_Y.txt"), row.names = FALSE, col.names = TRUE)
write.table(data[,X],paste(sep="",outFilePath,"data_X.txt"), row.names = FALSE, col.names = TRUE)
write.table(data[,X_0],paste(sep="",outFilePath,"data_X0.txt"), row.names = FALSE, col.names = TRUE)
write.table(data,paste(sep="",tmpFolder,"data.txt"), row.names = FALSE, col.names = FALSE)
data = paste(sep="",tmpFolder,"data.txt")
}else{
my_stop("Y should be NULL or a numeric matrix with 2 or more columns!")
}
if( is.character(data) & length(data) == 1 ){
if( substr(data,1,1) == "~" )
data = path.expand(data)
}
dataHeader = read.table(data,header = FALSE,nrows = 1)
nVariables = ncol(dataHeader)
if ( is.null(X_0) )
X_0 = as.numeric(c())
if ( !( is.vector(Y,"numeric") & is.vector(X,"numeric") & is.vector(X_0,"numeric") ) )
my_stop("When the 'data' argument is set, Y,X and X_0 need to be corresponding index vectors!",tmpFolder)
if ( length( c( intersect(Y,X) , intersect(Y,X_0) , intersect(X_0,X) ) ) != 0 )
my_stop("Y, X and X_0 need to be distinct index vectors!",tmpFolder)
if( length( c(Y,X,X_0) ) > nVariables )
my_stop("When the 'data' argument is set, Y,X and X_0 need to be corresponding index vectors!",tmpFolder)
blockLabels = rep(NA,nVariables)
blockLabels[Y] = 0
blockLabels[X] = 1
if ( length ( X_0 ) > 0 )
blockLabels[X_0] = 2
blockLabels[ is.na ( blockLabels ) ] = -1
}
dataLength = nchar(data)
if( dataLength == 0 )
my_stop("Please provide a correct path to a plain-text (.txt) file", tmpFolder)
dataString = head( strsplit( tail( strsplit(data,split = c("/"))[[1]] , 1 ) , ".txt" )[[1]] , 1 )
if ( length ( X_0 ) > 0 ){
structureGraph = structureGraph = matrix(c(0,0,0,1,0,0,2,0,0),3,3,byrow=TRUE)
}else structureGraph = structureGraph = matrix(c(0,0,1,0),2,2,byrow=TRUE)
write.table(blockLabels,paste(sep="",tmpFolder,"blockLabels.txt"), row.names = FALSE, col.names = FALSE)
blockList = paste(sep="",tmpFolder,"blockLabels.txt")
write.table(structureGraph, paste(sep="",tmpFolder,"structureGraph.txt"), row.names = FALSE, col.names = FALSE)
structureGraph = paste(sep="",tmpFolder,"structureGraph.txt")
if ( burnin < 0 ){
my_stop("Burnin must be positive or 0",tmpFolder)
}else{ if ( burnin > nIter ){
my_stop("Burnin might not be greater than nIter",tmpFolder)
}else{ if ( burnin < 1 ){
burnin = ceiling(nIter * burnin)
}}}
hyperpar.all <- list(a_w=2, b_w=5, a_o=2, b_o=sum(blockLabels==1)-2, a_pi=NA, b_pi=NA, nu=sum(blockLabels==0)+2, a_tau=0.1, b_tau=10, a_eta=0.1, b_eta=1, a_sigma=1, b_sigma=1, mrf_d=-3, mrf_e=0.03)
if(toupper(gammaPrior) %in% c("HOTSPOT", "HOTSPOTS", "HS")){
hyperpar.all$a_pi <- 2
hyperpar.all$b_pi <- 1
if(toupper(covariancePrior) %in% c("INDEPENDENT", "INDEP", "IG"))
hyperpar.all <- hyperpar.all[-c(7:11,14:15)]
if(toupper(covariancePrior) %in% c("DENSE", "IW"))
hyperpar.all <- hyperpar.all[-c(10:11,12:15)]
if(toupper(covariancePrior) %in% c("SPARSE", "HIW"))
hyperpar.all <- hyperpar.all[-c(12:15)]
}
if( toupper(gammaPrior) %in% c("HIERARCHICAL", "H")){
hyperpar.all$a_pi <- 1
hyperpar.all$b_pi <- sum(blockLabels==0) - 1
if(toupper(covariancePrior) %in% c("INDEPENDENT", "INDEP", "IG"))
hyperpar.all <- hyperpar.all[-c(3:6,7:11,14:15)]
if(toupper(covariancePrior) %in% c("DENSE", "IW"))
hyperpar.all <- hyperpar.all[-c(3:6,10:11,12:15)]
if(toupper(covariancePrior) %in% c("SPARSE", "HIW"))
hyperpar.all <- hyperpar.all[-c(3:6,12:15)]
}
if( toupper(gammaPrior) %in% c("MRF", "MARKOV RANDOM FIELD")){
if ( is.null(mrfG) )
my_stop("Argument 'mrfG' was specified!",tmpFolder)
if(toupper(covariancePrior) %in% c("INDEPENDENT", "INDEP", "IG"))
hyperpar.all <- hyperpar.all[-c(3:6,7:13)]
if(toupper(covariancePrior) %in% c("DENSE", "IW"))
hyperpar.all <- hyperpar.all[-c(3:6,10:13)]
if(toupper(covariancePrior) %in% c("SPARSE", "HIW"))
hyperpar.all <- hyperpar.all[-c(3:6,12:13)]
}
if(toupper(betaPrior) == "REGROUP"){
hyperpar.all$a_w0 <- hyperpar.all$a_w
hyperpar.all$b_w0 <- hyperpar.all$b_w
}
if(length(hyperpar)>0)
for( i in 1:length(hyperpar)){
if(names(hyperpar)[[i]] %in% names(hyperpar.all))
hyperpar.all[[which(names(hyperpar.all)==names(hyperpar)[[i]])]] <- hyperpar[[i]]
if(!is.null(hyperpar$a_omega)) hyperpar.all$a_pi <- hyperpar$a_omega
if(!is.null(hyperpar$b_omega)) hyperpar.all$b_pi <- hyperpar$b_omega
}
if ( toupper(covariancePrior) %in% c("SPARSE", "HIW") ){
covariancePrior <- "HIW"
}else if ( toupper(covariancePrior) %in% c("DENSE", "IW") ){
covariancePrior <- "IW"
}else if ( toupper(covariancePrior) %in% c("INDEPENDENT", "INDEP", "IG") ){
covariancePrior <- "IG"
}else
my_stop("Unknown covariancePrior argument: only sparse (HIW), dense(IW) or independent (IG) are available",tmpFolder)
if( gammaPrior == "" )
{
if ( is.null(mrfG) )
{
message( "Using default prior for Gamma - hotspot prior\n")
gammaPrior = "hotspot"
}else{
message( "No value for gammaPrior was specified, but mrfG was given - choosing MRF prior\n")
gammaPrior = "MRF"
}
}else{
if ( toupper(gammaPrior) %in% c("HOTSPOT", "HOTSPOTS", "HS") )
gammaPrior = "hotspot"
else if ( toupper(gammaPrior) %in% c("MRF", "MARKOV RANDOM FIELD") )
gammaPrior = "MRF"
else if ( toupper(gammaPrior) %in% c("HIERARCHICAL", "H") )
gammaPrior = "hierarchical"
else
my_stop("Unknown gammaPrior argument: only hotspot, MRF or hierarchical are available",tmpFolder)
}
if( !(is.character(mrfG) & length(mrfG) == 1) )
{
if( (is.numeric(mrfG) | is.data.frame(mrfG)) & !is.null(dim(mrfG)) )
{
if(ncol(mrfG) == 2)
mrfG = cbind( mrfG, rep(1,nrow(mrfG)) )
write.table(mrfG,paste(sep="",outFilePath,"mrfG.txt"), row.names = FALSE, col.names = FALSE)
mrfG = paste(sep="",outFilePath,"mrfG.txt")
}else if( is.null( mrfG )){
mrfG = matrix(c(0,0,0),ncol=3)
write.table(mrfG, paste(sep="",outFilePath,"mrfG.txt"), row.names = FALSE, col.names = FALSE)
mrfG = paste(sep="",outFilePath,"mrfG.txt")
}else
my_stop("Unknown mrfG argument: check the help function for possibile values",tmpFolder)
}
xml = as_xml_document(
list( hyperparameters = list(
lapply(hyperpar,function(x) list(x))
)))
hyperParFile = paste(sep="",tmpFolder,"hyperpar.xml")
write_xml(xml,file = hyperParFile)
ret = list( status=1, input=list(), output = list() )
class(ret) = "BayesSUR"
ret$input["nIter"] = nIter
ret$input["burnin"] = burnin
ret$input["nChains"] = nChains
ret$input["covariancePrior"] = covariancePrior
ret$input["gammaPrior"] = gammaPrior
ret$input["gammaSampler"] = gammaSampler
ret$input["gammaInit"] = gammaInit
ret$input["mrfG"] = mrfG
if( toupper(gammaPrior) %in% c("HIERARCHICAL", "H")){
names(hyperpar.all)[names(hyperpar.all)=="a_pi"] <- "a_omega"
names(hyperpar.all)[names(hyperpar.all)=="b_pi"] <- "b_omega"
}
ret$input$hyperParameters = hyperpar.all
methodString =
switch( covariancePrior,
"HIW" = "SSUR" ,
"IW" = "dSUR" ,
"IG" = "HRR" )
ret$call = cl
ret$output["outFilePath"] = outFilePath
ret$output["logP"] = paste(sep="", dataString , "_", methodString , "_logP_out.txt")
if ( output_gamma )
ret$output["gamma"] = paste(sep="", dataString , "_", methodString , "_gamma_out.txt")
if( gammaPrior %in% c("hierarchical","hotspot") & output_pi )
ret$output["pi"] = paste(sep="", dataString , "_", methodString , "_pi_out.txt")
if( gammaPrior == "hotspot" & output_tail )
ret$output["tail"] = paste(sep="", dataString , "_", methodString , "_hotspot_tail_p_out.txt")
if ( output_beta )
ret$output["beta"] = paste(sep="", dataString , "_", methodString , "_beta_out.txt")
if ( covariancePrior == "HIW" & output_Gy ){
ret$output["Gy"] = paste(sep="", dataString , "_", methodString , "_Gy_out.txt")
ret$output["Gvisit"] = paste(sep="", dataString , "_", methodString , "_Gy_visit.txt")
}
if ( covariancePrior %in% c("HIW","IW") & output_sigmaRho )
ret$output["sigmaRho"] = paste(sep="", dataString , "_", methodString , "_sigmaRho_out.txt")
if ( output_model_size )
ret$output["model_size"] = paste(sep="", dataString , "_", methodString , "_model_size_out.txt")
if ( output_CPO ){
ret$output["CPO"] = paste(sep="", dataString , "_", methodString , "_CPO_out.txt")
ret$output["CPOsumy"] = paste(sep="", dataString , "_", methodString , "_CPOsumy_out.txt")
ret$output["WAIC"] = paste(sep="", dataString , "_", methodString , "_WAIC_out.txt")
}
if ( output_Y )
ret$output["Y"] = paste(sep="", "data_Y.txt")
if ( output_X ){
ret$output["X"] = paste(sep="", "data_X.txt")
if( length(X_0)>0 )
ret$output["X0"] = paste(sep="", "data_X0.txt")
}
ret$status = BayesSUR_internal(data, mrfG, blockList, structureGraph, hyperParFile, outFilePath,
nIter, burnin, nChains,
covariancePrior, gammaPrior, gammaSampler, gammaInit, betaPrior, maxThreads,
output_gamma, output_beta, output_Gy, output_sigmaRho, output_pi, output_tail, output_model_size, output_CPO, output_model_visit)
obj_BayesSUR = list(status=ret$status, input=ret$input, output=ret$output, call=ret$call)
save(obj_BayesSUR, file=paste(sep="",outFilePath,"obj_BayesSUR.RData"))
if(outFilePath != tmpFolder)
unlink(tmpFolder,recursive = TRUE)
return(ret)
}
my_stop = function( msg , tmpFolder )
{
unlink(tmpFolder,recursive = TRUE)
stop(msg)
} |
SNPtm2<- function(trange=100,tsl=1.0,x6=NULL,r6=NULL) {
testrange<- is.null(trange)
if(testrange == TRUE) trange<- 400
testtsl<- is.null(tsl)
if(testtsl == TRUE) tsl<- 1.0
x<- rep(0,trange*6/tsl)
x<- matrix(x,ncol=6)
testr<- is.null(r6)
if(testr == TRUE) r<- c(0.025,0.040,0.045,0.045,0.045,0.045)
if(testr == FALSE) r<- r6
testx<- is.null(x6)
if(testx == TRUE) x[1,]<- c(0.50,0.70,1.70,6.0,13.0,78.0)
if(testx == FALSE) x[1,]<- x6
nt<- (trange/tsl)
tv<- seq(0,trange-tsl,tsl)
vegtypes<- c("Aconitum","Trisetum","Deschampsia","Festuca","Carex","Pinus")
K<- 100
dx<- rep(0,6)
for(t in 2:nt) {
dx[1]<- r[1]*x[t-1,1]*((K- x[t-1,1])/K)
dx[2]<- r[2]*x[t-1,2]*((K- x[t-1,1]-x[t-1,2])/K)
dx[3]<- r[3]*x[t-1,3]*((K- x[t-1,1]-x[t-1,2]-x[t-1,3])/K)
dx[4]<- r[4]*x[t-1,4]*((K- x[t-1,1]-x[t-1,2]-x[t-1,3]-x[t-1,4])/K)
dx[5]<- r[5]*x[t-1,5]*((K- x[t-1,1]-x[t-1,2]-x[t-1,3]-x[t-1,4]-x[t-1,5])/K)
dx[6]<- r[6]*x[t-1,6]*((K- x[t-1,1]-x[t-1,2]-x[t-1,3]-x[t-1,4]-x[t-1,5]-x[t-1,6])/K)
dx[2]<- dx[2] - (dx[1])*(x[t-1,2]/sum(x[t-1,2:6]))
dx[3]<- dx[3] - (dx[1]+dx[2])*(x[t-1,3]/sum(x[t-1,3:6]))
dx[4]<- dx[4] - (dx[1]+dx[2]+dx[3])*(x[t-1,4]/sum(x[t-1,4:6]))
dx[5]<- dx[5] - (dx[1]+dx[2]+dx[3]+dx[4])*(x[t-1,5]/sum(x[t-1,5:6]))
dx[6]<- dx[6] - (dx[1]+dx[2]+dx[3]+dx[4]+dx[5])*(x[t-1,6]/sum(x[t-1,6]))
for(v in 1:6){
x[t,v]<- x[t-1,v]+(dx[v]*tsl)
}
}
o.SNP<- list(n.time.steps=trange,time.step.length=tsl,time.vector=tv,veg.types=vegtypes,growth.rates=r,initial.cond=x[1,],sim.data=x)
} |
calcATHBpts <- function(trm, psych, ta, tr, vel, rh, met, wme, pb, ltime, ht, wt){
set <- calcATHBset(trm, psych, ta, tr, vel, rh, met, wme, pb, ltime, ht, wt)
ATHBpts <- .25 * set - 6.03
data.frame(ATHBpts)
} |
logLikVec.fExtremes_gev <- function(object, pars = NULL, ...) {
if (!missing(...)) {
warning("extra arguments discarded")
}
if (is.null(pars)) {
pars <- coef(object)
}
n_pars <- length(pars)
response_data <- object@fit$data
mu <- pars["mu"]
sigma <- pars["beta"]
xi <- pars["xi"]
if (sigma <= 0) {
val <- -Inf
} else {
val <- revdbayes::dgev(response_data, loc = mu, scale = sigma, shape = xi,
log = TRUE)
}
attr(val, "nobs") <- nobs(object)
attr(val, "df") <- length(pars)
class(val) <- "logLikVec"
return(val)
}
nobs.fExtremes_gev <- function(object, ...) {
return(object@fit$n)
}
coef.fExtremes_gev <- function(object, ...) {
return(object@fit$par.ests)
}
vcov.fExtremes_gev <- function(object, ...) {
return(object@fit$varcov)
}
logLik.fExtremes_gev <- function(object, ...) {
return(logLik(logLikVec(object)))
} |
plot.rlme <- function(x, ...) {
if (x$method == "GR") {
getgrstplot(x)
}
else if (x$method == "REML") {
getlmestplot(x)
}
else {
cat("rlme only supports plotting fits from GR and REML methods\n")
}
invisible()
}
rlme <- function(f, data, method = "gr", print = FALSE, na.omit = TRUE, weight = "wil", rprpair = "hl-disp", verbose=FALSE) {
location = 2
method = tolower(method)
fit = list(formula = f, location = location, scale = scale)
if (na.omit == TRUE) {
data = na.omit(data)
}
response_name = as.character(as.formula(f)[[2]])
random_terms = str_extract_all(as.character(f)[[3]], "\\(([0-9 a-zA-Z.:|]+)\\)")
random_terms = lapply(random_terms[[1]], function(str) substr(str,
2, nchar(str) - 1))
covariate_names = setdiff(attributes(terms(f))$term.labels,
random_terms)
school_name = section_name = ""
levels = 0
if (length(random_terms) == 2) {
levels = 3
school_name = tail(strsplit(random_terms[[1]], "\\W")[[1]],
1)
section_name = tail(strsplit(random_terms[[2]], "\\W")[[1]],
1)
I = length(unique(factor(data[[school_name]])))
sec = as.vector(sec_vec(data[[school_name]], data[[section_name]]))
fit$num.clusters = I
fit$num.subclusters = sum(sec)
mat = mat_vec(data[[school_name]], data[[section_name]])
ss = rowSums(mat)
J = sum(sec)
n = sum(mat)
one = rep(1, length(data[[response_name]]))
schsize = aggregate(one, by = list(data[[response_name]]),
FUN = sum)[, 2]
school1 = factor(rep(1:length(schsize), schsize))
sss = aggregate(one, by = list(data[[school_name]], data[[section_name]]),
FUN = sum)[, 3]
section = factor(rep(1:length(ss), ss))
}
if (length(random_terms) == 1) {
levels = 2
school_name = tail(strsplit(random_terms[[1]], "\\W")[[1]],
1)
I = length(unique(factor(data[[school_name]])))
mat = mat_vec(data[[school_name]], rep(1, length(data[[school_name]])))
n = sum(mat)
}
x = as.matrix(data[covariate_names])
x = apply(x, 2, function(x) {
x - mean(x)
})
y = data[[response_name]]
fit$num.obs = length(y)
fit$y = y
if (length(random_terms) == 0) {
cat("You have entered an independent linear model.\n")
cat("The function 'lmr' can be used to fit these models.\n")
cat("Continuing using lmr.\n")
fitw = lmr(f, data=data)
return(fitw)
}
if (method == "reml" || method == "ml") {
if (levels == 3) {
REML = LM_est(x, y, dat = data.frame(y = y, x, school = data[[school_name]],
section = data[[section_name]]), method = toupper(method))
}
else if (levels == 2) {
REML = LM_est2(x, y, dat = data.frame(y = y, x, school = data[[school_name]]),
method = toupper(method))
}
REMLb = REML$theta
REMLs = REML$sigma
REMLe = REML$ses
REML$ehat
tvalue = REMLb/REMLe
pvalue = 2 * pnorm(-abs(tvalue))
intracoeffs = c(REMLs[1]/sum(REMLs), (REMLs[1] + REMLs[2])/sum(REMLs))
fit$method = "REML"
fit$ehat = REML$ehat
fit$effect.err = REML$effect_err
fit$effect.cluster = REML$effect_sch
if (levels == 3) {
fit$effect.subcluster = REML$effect_sec
}
fit$fixed.effects = data.frame(RowNames = c("(Intercept)",
covariate_names), Estimate = REMLb, StdError = REMLe,
tvalue = tvalue, pvalue = pvalue)
fit$var.b = REML$varb
fit$intra.class.correlations = intracoeffs
fit$t.value = tvalue
fit$p.value = pvalue
fit$standr.lme = REML$standr.lme
}
else if (method == "jr") {
if (levels == 3) {
JR = JR_est(x, y, I, sec, mat, data[[school_name]],
data[[section_name]], rprpair = rprpair, verbose=verbose)
}
else if (levels == 2) {
JR = JR_est2(x, y, I, 1, mat, data[[school_name]],
rep(1, length(data[[school_name]])), rprpair = rprpair, verbose=verbose)
}
JRb = JR$theta
JRe = JR$ses
JRs = JR$sigma
tvalue = JRb/JRe
pvalue = 2 * pnorm(-abs(tvalue))
intracoeffs = c(JRs[1]/sum(JRs), (JRs[1] + JRs[2])/sum(JRs))
fit$method = "JR"
fit$ehat = JR$ehat
fit$fixed.effects = data.frame(RowNames = c("(Intercept)",
covariate_names), Estimate = JRb, StdError = JRe,
tvalue = tvalue, pvalue = pvalue)
fit$effect.err = JR$effect_err
fit$effect.cluster = JR$effect_sch
if (levels == 3) {
fit$random.effects = data.frame(Groups = c(paste(school_name,
":", section_name, sep = ""), school_name, "Residual"),
Name = c("(Intercept)", "(Intercept)", ""), Variance = JRs)
fit$effect.subcluster = JR$effect_sec
}
else if (levels == 2) {
fit$random.effects = data.frame(Groups = c(school_name,
"Residual"), Name = c("(Intercept)", ""), Variance = JRs)
}
fit$intra.class.correlations = intracoeffs
fit$var.b = JR$varb
fit$t.value = tvalue
fit$p.value = pvalue
}
else if (method == "gr") {
if (levels == 3) {
GR = GR_est(x, y, I, sec, mat, data[[school_name]],
data[[section_name]], rprpair = rprpair, verbose=verbose)
}
else if (levels == 2) {
GR = GR_est2(x, y, I, rep(1, length(data[[school_name]])),
mat, data[[school_name]], rep(1, length(data[[school_name]])),
rprpair = rprpair, verbose=verbose)
}
GRb = GR$theta
GRe = GR$ses
tvalue = GRb/GRe
pvalue = 2 * pnorm(-abs(tvalue))
varb = GR$varb
GRs = GR$sigma
intracoeffs = c(GRs[1]/sum(GRs), (GRs[1] + GRs[2])/sum(GRs))
coll.stres <- stanresidgr(GR$xstar, GR$ystar, resid = GR$ehats,
delta = 0.8, param = 2, conf = 0.95)
stresgr <- coll.stres$stanr
fit$method = "GR"
fit$ehat = GR$ehat
fit$ehats = GR$ehats
fit$xstar = GR$xstar
fit$ystar = GR$ystar
fit$fixed.effects = data.frame(RowNames = c("(Intercept)",
covariate_names), Estimate = GRb, StdError = GRe,
tvalue = tvalue, pvalue = pvalue)
fit$effect.err = GR$effect_err
fit$effect.cluster = GR$effect_sch
if (levels == 3) {
fit$random.effects = data.frame(Groups = c(school_name, paste(school_name,
":", section_name, sep = ""), "Residual"),
Name = c("(Intercept)", "(Intercept)", ""), Variance = GRs)
fit$effect.subcluster = GR$effect_sec
}
else if (levels == 2) {
fit$random.effects = data.frame(Groups = c(school_name,
"Residual"), Name = c("(Intercept)", ""), Variance = GRs)
}
fit$standard.residual = stresgr
fit$intra.class.correlations = intracoeffs
fit$var.b = varb
fit$t.value = tvalue
fit$p.value = pvalue
}
else if (method == "geer") {
tol = 1.1e-25
if (levels == 3) {
GEER = GEER_est(x, y, I, sec, mat, data[[school_name]],
data[[section_name]], weight = weight, rprpair = rprpair, verbose=verbose)
}
else if (levels == 2) {
GEER = GEER_est2(x, y, I, rep(1, length(data[[school_name]])),
mat, data[[school_name]], rep(1, length(data[[school_name]])),
weight = weight, rprpair = rprpair, verbose=verbose)
}
GEERb = GEER$theta
GEERs = GEER$sigma
GEER_e2 = GEER$ses_AP
intracoeffs = c(GEERs[1]/sum(GEERs), (GEERs[1] + GEERs[2])/sum(GEERs))
GEERe = GEER$ses_AP
tvalue = GEERb/GEERe
pvalue = 2 * pnorm(-abs(tvalue))
fit$method = "GEER"
fit$ehat = GEER$ehat
fit$fixed.effects = data.frame(RowNames = c("(Intercept)",
covariate_names), Estimate = GEERb, StdError = GEERe,
tvalue = tvalue, pvalue = pvalue)
fit$effect.err = GEER$effect_err
fit$effect.cluster = GEER$effect_sch
if (levels == 3) {
fit$random.effects = data.frame(Groups = c(school_name, paste(school_name,
":", section_name, sep = ""), "Residual"),
Name = c("(Intercept)", "(Intercept)", ""), Variance = GEERs)
fit$effect.subcluster = GEER$effect_sec
}
else if (levels == 2) {
fit$random.effects = data.frame(Groups = c(school_name,
"Residual"), Name = c("(Intercept)", ""), Variance = GEERs)
}
fit$intra.class.correlations = intracoeffs
fit$var.b = GEER$varb
fit$t.value = tvalue
fit$p.value = pvalue
}
class(fit) = "rlme"
if (print == TRUE) {
summary(fit)
}
return(fit)
}
summary.rlme <- function(object, ...) {
fit = object
cat("Linear mixed model fit by ", fit$method, "\n")
cat("Formula: ", deparse(fit$formula), "\n")
if ("random.effects" %in% attributes(fit)$names) {
cat("Random effects:\n")
random.effects = fit$random.effects
names(random.effects) = c("Groups", "Name", "Variance")
print(random.effects, row.names = FALSE, right = FALSE)
}
cat("\nNumber of obs:\n")
cat(fit$num.obs)
if ("num.clusters" %in% attributes(fit)$names && "num.subclusters" %in%
attributes(fit)$names) {
cat(" observations,", fit$num.clusters, "clusters,",
fit$num.subclusters, "subclusters")
}
cat("\n")
cat("\nFixed effects:\n")
fixed.effects = fit$fixed.effects
if(length(fixed.effects) == 5) {
names(fixed.effects) = c("", "Estimate", "Std. Error", "t value",
"p value")
} else if(length(fixed.effects) == 3) {
names(fixed.effects) = c("", "Estimate", "Std. Error")
} else if(length(fixed.effects) == 2) {
names(fixed.effects) = c("", "Estimate")
}
print(fixed.effects, row.names = FALSE, right = FALSE)
if ("intra.class.correlations" %in% attributes(fit)$names) {
cat("\nIntra-class correlation coefficients\n")
intra.coeffs = data.frame(names = c("intra-cluster",
"intra-subcluster"), Estimates = fit$intra.class.correlations)
names(intra.coeffs) = c("", "Esimates")
print(intra.coeffs, row.names = FALSE, right = FALSE)
}
if(!is.null(fit$var.b)) {
cat("\ncov-var (fixed effects)\n")
print(fit$var.b)
}
} |
library(ISLR)
data('Default')
str(Default)
LR1 = glm(default ~ ., family='binomial', data=Default)
summary(LR1)
LR2 = glm(default ~ student + balance, family='binomial', data=Default)
summary(LR2)
range(Default$balance)
ndata3 = Default[c(1,60,700),]
predict(LR2,newdata=ndata3, type='response' )
str(mtcars)
x = 1:5
x1 = c('a','b')
m1 = matrix(1:24, nrow=6)
m1
list1 = list(x, x1, m1)
list1
class(women)
women
str(women)
?women
women
head(women)
tail(women,n=3)
head(women, n=3)
names(women)
summary(women)
dim(women)
data()
library(MASS)
x = women$height
x
plot(x)
mean(x)
sd(x) ; var(x)
max(x)
median(x)
x
sort(x, decreasing = T)
table(x)
quantile(x)
x
seq(0,1,.1)
quantile(x, c(.1, .5, .8))
quantile(x,seq(0,1,.1) )
summary(x)
min(x); max(x)
boxplot(x)
abline(h= c(min(x), max(x),mean(x)+1, median(x)), col=1:5, lwd=4)
head(women)
names(women)
model1 = lm(weight ~ height, data=women)
plot(women)
?lm
summary(model1)
model1
y = 3.45 * x + - 87
women$height
fitted(model1)
cbind(women, fitted(model1))
residuals(model1)
cbind(women, fitted(model1), residuals(model1), diff= fitted(model1) - women$weight)
sqrt(sum(residuals(model1)^2)/nrow(women))
cbind(women, fitted(model1))
range(women$height)
new1= data.frame(height=c(57, 60.5,70))
p1=predict(model1, newdata = new1)
cbind(new1, p1)
names(mtcars)
?mtcars
mtmodel_1 = lm(mpg ~ wt, data=mtcars )
mtmodel_2 = lm(mpg ~ wt + disp, data=mtcars )
mtmodel_3 = lm(mpg ~ wt + disp + cyl, data=mtcars )
mtmodel_4 = lm(mpg ~ ., data=mtcars )
summary(mtmodel_1)
summary(mtmodel_2)
summary(mtmodel_3)
summary(mtmodel_4)
AIC(mtmodel_1, mtmodel_2,mtmodel_3,mtmodel_4)
summary(mtmodel_4)
step(lm(mpg ~ ., data=mtcars ))
mtmodel_5= lm(mpg ~ wt + qsec + am, data=mtcars)
summary(mtmodel_5)
attendance = 1:20
marks = 1:20
summary(lm(marks ~ attendance))
cbind(attendance, marks)
cor(attendance, marks)
x
y = 3.45 * x + - 87
x
head(women)
(y = 4.45 * 58 - 87)
plot(women)
abline(model1, col='red', lwd=4)
abline(v=64) ; abline(h=150)
x2 = floor(runif(1000, 50, 100))
x2
x2a= sort(x2)
x2a[1000/2]
median(x2)
sort(x)
t1= table(x2)
sort(t1, decreasing = T)
x1 = rep(10,10)
x1
sd(x1)
dim(mtcars)
mtlogmodel = glm(am ~ hp + wt, family='binomial', data=mtcars)
summary(mtlogmodel)
p1=predict(mtlogmodel, newdata=mtcars, type='response')
p2= round(p1, 3)
p3 = ifelse(p2<0.5,0,1)
cbind(mtcars$am, mtcars$hp, mtcars$wt, p2,p3, truefalse= mtcars$am == p3) |
gap.in.segments.f <- function(seg = NULL, geometry = "euc") {
num.seg <- dim(seg)[1]
gap <- rep(0, num.seg)
for (i in 1:(num.seg - 1)) {
if (seg$Transect.Label[i] == seg$Transect.Label[i + 1]) {
if (geometry == "euc") dist <- euc.distance.f(seg$x[i], seg$y[i], seg$x[i + 1], seg$y[i + 1])
if (geometry == "geo") dist <- geo.distance.f(seg$longitude[i], seg$latitude[i], seg$longitude[i + 1], seg$latitude[i + 1])
effort.dist <- (seg$Effort[i] / 2) + (seg$Effort[i + 1] / 2)
if (dist > effort.dist) gap[i + 1] <- 1
}
}
gap
}
define.blocks.f <- function(seg = NULL, covar.col = NULL, geometry = "euc") {
num.covar <- length(covar.col)
if (is.na(covar.col)) print("No covariates used to combined segments - using transects")
num.seg <- dim(seg)[1]
seg$Block.Label <- rep(NA, num.seg)
j <- 1
seg$Block.Label[1] <- j
gap <- gap.in.segments.f(seg = seg, geometry = geometry)
for (i in 2:num.seg) {
if (seg$Transect.Label[i] != seg$Transect.Label[i - 1]) j <- j + 1
if (gap[i] == 1) j <- j + 1
if (seg$Transect.Label[i] == seg$Transect.Label[i - 1]) {
chg <- 0
if (!is.na(covar.col)) {
for (k in 1:num.covar) {
if (seg[i, covar.col[k]] != seg[i - 1, covar.col[k]]) chg <- 1
}
}
j <- j + chg
}
seg$Block.Label[i] <- j
}
seg
}
get.blocks.f <- function(seg = NULL, geometry = "euc") {
name.blocks <- unique(seg$Block.Label)
num.blocks <- length(name.blocks)
blocks <- NULL
for (i in 1:num.blocks) {
temp <- seg[seg$Block.Label == name.blocks[i], ]
num.seg <- dim(temp)[1]
first <- temp[1, ]
if (geometry == "euc") {
first$end.x <- temp$end.x[num.seg]
first$end.y <- temp$end.y[num.seg]
}
if (geometry == "geo") {
first$end.lon <- temp$end.lon[num.seg]
first$end.lat <- temp$end.lat[num.seg]
}
first$Effort <- sum(temp$Effort)
if (i == 1) blocks <- first
if (i > 1) blocks <- rbind(blocks, first)
}
exc.labels <- c("quadrant", "angle", "what.angle", "x", "y", "latitude", "longitude")
col.names <- names(blocks)
col.names <- !is.element(col.names, exc.labels)
blocks <- blocks[, col.names]
blocks
}
add.labels.to.obs.f <- function(dists = NULL, obs = NULL, seg = NULL) {
num.dists <- dim(dists)[1]
if (num.dists != dim(obs)[1]) print("Perp distance data and observation data different number of records")
dists$Sample.Label <- obs$Sample.Label
dists$Block.Label <- rep(NA, num.dists)
for (i in 1:num.dists) {
temp <- seg[seg$Sample.Label == dists$Sample.Label[i], ]
if (dim(temp)[1] > 1) print(paste("More than one segment chosen for observation, object=", dists$object[i]))
dists$Block.Label[i] <- temp$Block.Label[1]
}
dists
}
combine.dsmdata.f <- function(blocks = NULL, dists = NULL) {
num.blocks <- dim(blocks)[1]
cols.dists <- names(dists)
exc.labels <- c("Sample.Label", "Block.Label", "Transect.Label", "Effort")
cols.dist <- !is.element(cols.dists, exc.labels)
one.dists.small <- dists[1, cols.dist]
one.dists.small[1, ] <- NA
k <- 0
for (i in 1:num.blocks) {
one.block <- blocks[i, ]
temp <- dists[dists$Block.Label == blocks$Block.Label[i], ]
num.temp <- dim(temp)[1]
if (num.temp > 0) {
dists.small <- temp[, cols.dist]
for (j in 1:num.temp) {
block.dist <- cbind(one.block, dists.small[j, ])
if (j == 1) comb <- rbind(cbind(one.block, one.dists.small), block.dist)
if (j > 1) comb <- rbind(comb, block.dist)
}
}
if (num.temp == 0) comb <- cbind(one.block, one.dists.small)
if (i == 1) all <- comb
if (i > 1) all <- rbind(all, comb)
}
exc.labels <- c("quadrant", "angle", "what.angle", "Sample.Label")
col.names <- names(all)
col.names <- !is.element(col.names, exc.labels)
all <- all[, col.names]
col1 <- match("Transect.Label", names(all))
col2 <- match("Block.Label", names(all))
col3 <- match("object", names(all))
names(all)[c(col1, col2, col3)] <- c("trans", "seg", "det")
all
}
get.direction.unit.f <- function(data = NULL, is.blocks = TRUE,
geometry = "euc") {
if (is.blocks) {
data$Unit <- data$Block.Label
name.label <- "Block.Label"
} else {
data$Unit <- data$Transect.Label
name.label <- "Transect.Label"
}
if (geometry == "euc") {
data$new.x <- data$x
data$new.y <- data$y
}
if (geometry == "geo") {
data$new.x <- data$longitude
data$new.y <- data$latitude
}
name.unit <- unique(data$Unit)
num.unit <- length(name.unit)
unit <- NULL
for (i in 1:num.unit) {
temp <- data[data$Unit == name.unit[i], ]
num.temp <- dim(temp)[1]
unit$Unit[i] <- as.character(name.unit[i])
quad <- get.quadrant.f(temp$new.x[1], temp$new.y[1], temp$new.x[num.temp], temp$new.y[num.temp])
unit$quadrant[i] <- quad
diff.x <- temp$new.x[num.temp] - temp$new.x[1]
diff.y <- temp$new.y[num.temp] - temp$new.y[1]
what.angle <- atan(diff.y / diff.x) * (180 / pi)
if (quad == 1) unit$angle[i] <- 0
if (quad == 2) unit$angle[i] <- 90
if (quad == 3) unit$angle[i] <- 180
if (quad == 4) unit$angle[i] <- 270
if (quad == 5) unit$angle[i] <- 90 - abs(what.angle)
if (quad == 6) unit$angle[i] <- 90 + abs(what.angle)
if (quad == 7) unit$angle[i] <- 180 + abs(what.angle)
if (quad == 8) unit$angle[i] <- 270 + abs(what.angle)
unit$what.angle[i] <- what.angle
}
unit <- data.frame(unit)
names(unit)[1] <- name.label
unit
}
get.direction.segment.f <- function(data = NULL, geometry = "euc") {
if (geometry == "euc") {
data$new.x <- data$x
data$new.y <- data$y
}
if (geometry == "geo") {
data$new.x <- data$longitude
data$new.y <- data$latitude
}
num.unit <- dim(data)[1]
data$quadrant <- rep(NA, num.unit)
data$angle <- rep(NA, num.unit)
data$quadrant.r <- rep(NA, num.unit)
data$angle.r <- rep(NA, num.unit)
for (i in 1:(num.unit - 1)) {
j <- i + 1
temp <- data[i:j, ]
quad <- get.quadrant.f(temp$new.x[1], temp$new.y[1], temp$new.x[2], temp$new.y[2])
data$quadrant[i] <- quad
diff.x <- temp$new.x[2] - temp$new.x[1]
diff.y <- temp$new.y[2] - temp$new.y[1]
data$angle[i] <- what.angle.f(dy = diff.y, dx = diff.x, quad = quad)
if (temp$Transect.Label[1] != temp$Transect.Label[2]) {
if (data$Transect.Label[i] == data$Transect.Label[i - 1]) {
data$quadrant[i] <- data$quadrant[i - 1]
data$angle[i] <- data$angle[i - 1]
}
if (data$Transect.Label[i] != data$Transect.Label[i - 1]) {
print(paste("Only one segment in transect", data$Transect.Label[i]))
}
}
}
if (data$Transect.Label[num.unit] == data$Transect.Label[num.unit - 1]) {
data$quadrant[num.unit] <- data$quadrant[num.unit - 1]
data$angle[num.unit] <- data$angle[num.unit - 1]
}
if (data$Transect.Label[num.unit] != data$Transect.Label[num.unit - 1]) print("Last segment on its own")
for (i in num.unit:2) {
j <- i - 1
temp <- rbind(data[i, ], data[j, ])
quad <- get.quadrant.f(temp$new.x[1], temp$new.y[1], temp$new.x[2], temp$new.y[2])
data$quadrant.r[i] <- quad
diff.x <- temp$new.x[2] - temp$new.x[1]
diff.y <- temp$new.y[2] - temp$new.y[1]
data$angle.r[i] <- what.angle.f(dy = diff.y, dx = diff.x, quad = quad)
if (temp$Transect.Label[1] != temp$Transect.Label[2]) {
if (data$Transect.Label[i] == data$Transect.Label[i - 1]) {
data$quadrant.r[i] <- data$quadrant.r[i - 1]
data$angle.r[i] <- data$angle.r[i - 1]
}
if (data$Transect.Label[i] != data$Transect.Label[i + 1]) {
print(paste("Only one segment in transect", data$Transect.Label[i]))
}
}
}
if (data$Transect.Label[1] == data$Transect.Label[2]) {
data$quadrant.r[1] <- data$quadrant.r[2]
data$angle.r[1] <- data$angle.r[2]
}
exc.labels <- c("new.x", "new.y")
col.names <- names(data)
col.names <- !is.element(col.names, exc.labels)
data <- data[, col.names]
data
}
start.end.points.segments.f <- function(seg = NULL, use.tran = FALSE, tran = NULL, geometry = "euc") {
num.seg <- dim(seg)[1]
for (i in 1:num.seg) {
if (use.tran) {
temp <- tran[tran$Transect.Label == seg$Transect.Label[i], ]
seg$quadrant[i] <- temp$quadrant[1]
seg$angle[i] <- temp$angle[1]
}
seg.half.len <- seg$Effort[i] / 2
if (geometry == "euc") {
if (seg$quadrant[i] == 1) {
seg$start.x[i] <- seg$x[i]
seg$start.y[i] <- seg$y[i] - seg.half.len
seg$end.x[i] <- seg$x[i]
seg$end.y[i] <- seg$y[i] + seg.half.len
}
if (seg$quadrant[i] == 2) {
seg$start.x[i] <- seg$x[i] - seg.half.len
seg$start.y[i] <- seg$y[i]
seg$end.x[i] <- seg$x[i] + seg.half.len
seg$end.y[i] <- seg$y[i]
}
if (seg$quadrant[i] == 3) {
seg$start.x[i] <- seg$x[i]
seg$start.y[i] <- seg$y[i] + seg.half.len
seg$end.x[i] <- seg$x[i]
seg$end.y[i] <- seg$y[i] - seg.half.len
}
if (seg$quadrant[i] == 4) {
seg$start.x[i] <- seg$x[i] + seg.half.len
seg$start.y[i] <- seg$y[i]
seg$end.x[i] <- seg$x[i] - seg.half.len
seg$end.y[i] <- seg$y[i]
}
if (seg$quadrant[i] == 5) {
angle <- 90 - seg$angle[i]
tri.sides <- get.triangle.sides.f(seg.len = seg.half.len, angle = angle)
seg$start.x[i] <- seg$x[i] - tri.sides[2]
seg$start.y[i] <- seg$y[i] - tri.sides[1]
seg$end.x[i] <- seg$x[i] + tri.sides[2]
seg$end.y[i] <- seg$y[i] + tri.sides[1]
}
if (seg$quadrant[i] == 6) {
angle <- 180.0 - seg$angle[i]
tri.sides <- get.triangle.sides.f(seg.len = seg.half.len, angle = angle)
seg$start.x[i] <- seg$x[i] - tri.sides[1]
seg$start.y[i] <- seg$y[i] + tri.sides[2]
seg$end.x[i] <- seg$x[i] + tri.sides[1]
seg$end.y[i] <- seg$y[i] - tri.sides[2]
}
if (seg$quadrant[i] == 7) {
angle <- seg$angle[i] - 180.0
tri.sides <- get.triangle.sides.f(seg.len = seg.half.len, angle = angle)
seg$start.x[i] <- seg$x[i] + tri.sides[1]
seg$start.y[i] <- seg$y[i] + tri.sides[2]
seg$end.x[i] <- seg$x[i] - tri.sides[1]
seg$end.y[i] <- seg$y[i] - tri.sides[2]
}
if (seg$quadrant[i] == 8) {
angle <- 360 - seg$angle[i]
tri.sides <- get.triangle.sides.f(seg.len = seg.half.len, angle = angle)
seg$start.x[i] <- seg$x[i] + tri.sides[1]
seg$start.y[i] <- seg$y[i] - tri.sides[2]
seg$end.x[i] <- seg$x[i] - tri.sides[1]
seg$end.y[i] <- seg$y[i] + tri.sides[2]
}
}
if (geometry == "geo") {
print("This option is not implemented yet - use geometry=euc!")
}
}
if (geometry == "euc") {
x.col <- match("x", names(seg))
y.col <- match("y", names(seg))
names(seg)[c(x.col, y.col)] <- c("mid.x", "mid.y")
}
seg
}
get.quadrant.f <- function(start.x, start.y, end.x, end.y, tol = 0.0000001) {
x.diff <- (start.x - end.x)
y.diff <- (start.y - end.y)
if (abs(x.diff) < tol & abs(y.diff) < tol) print("Segments on top of each other!")
quad <- NA
if (abs(x.diff) < tol) {
if (end.y > start.y) quad <- 1
if (end.y < start.y) quad <- 3
}
if (abs(y.diff) < tol) {
if (end.x > start.x) quad <- 2
if (end.x < start.x) quad <- 4
}
if (end.x > start.x & end.y > start.y) quad <- 5
if (end.x > start.x & end.y < start.y) quad <- 6
if (end.x < start.x & end.y < start.y) quad <- 7
if (end.x < start.x & end.y > start.y) quad <- 8
if (is.na(quad)) print("Quadrant not assigned")
quad
}
euc.distance.f <- function(x1, y1, x2, y2) {
x.diff <- abs(x1 - x2)
y.diff <- abs(y1 - y2)
hypot <- sqrt(x.diff^2 + y.diff^2)
hypot
}
geo.distance.f <- function(lon1, lat1, lon2, lat2) {
rad <- pi / 180
nm2km <- 1.852
if ((lat1 == lat2) & (lon1 == lon2)) {
posdist <- 0
} else {
rlat1 <- lat1 * rad
rlat2 <- lat2 * rad
rlon <- (lon2 - lon1) * rad
posdist <- 60 * (1 / rad) * acos(sin(rlat1) * sin(rlat2) + cos(rlat1) * cos(rlat2) * cos(rlon))
}
posdist <- posdist * nm2km
posdist
}
get.triangle.sides.f <- function(seg.len = NULL, angle = NULL) {
deg2rad <- pi / 180
x <- seg.len * sin(angle * deg2rad)
y <- seg.len * cos(angle * deg2rad)
triangle <- c(x, y)
triangle
}
generate.obs.location.f <- function(seg = NULL, dists = NULL, geometry = "euc",
do.plot = FALSE) {
deg2rad <- pi / 180
num.sgt <- dim(dists)[1]
new.sgt <- NULL
new.sgt$x <- rep(NA, num.sgt)
new.sgt$y <- rep(NA, num.sgt)
new.sgt <- data.frame(new.sgt)
if (do.plot) stop("do.plot code has been removed")
for (i in 1:num.sgt) {
temp <- seg[seg$Sample.Label == dists$Sample.Label[i], ]
new.coords <- get.point.along.segment.f(temp$start.x[1], temp$start.y[1], temp$end.x[1], temp$end.y[1], quad = temp$quadrant[1], seg.angle = temp$angle[1])
new.x <- new.coords[1]
new.y <- new.coords[2]
what.side <- sample(c(-1, 1), 1)
pd <- dists$distance[i]
sgt.coords <- get.coords.f(quad = temp$quadrant[1], alpha = temp$angle[1], new.x = new.x, new.y = new.y, pd = pd, side = what.side)
new.sgt$x[i] <- sgt.coords[1]
new.sgt$y[i] <- sgt.coords[2]
if (do.plot) {
stop("do.plot=TRUE. This code has been removed.")
}
}
new.sgt
}
get.point.along.segment.f <- function(x1, y1, x2, y2, quad = NULL, seg.angle) {
deg2rad <- pi / 180
if (quad == 1) {
new.x <- x1
new.y <- runif(1, min = y1, max = y2)
}
if (quad == 2) {
new.x <- runif(1, min = x1, max = x2)
new.y <- y1
}
if (quad == 3) {
new.x <- x1
new.y <- runif(1, min = y2, max = y1)
}
if (quad == 2) {
new.x <- runif(1, min = x2, max = x1)
new.y <- y1
}
if (quad == 5) {
theta <- 90 - seg.angle
diffx <- x2 - x1
diffy <- y2 - y1
hyp <- get.hypot.f(diffx, diffy)
smallhyp <- runif(1, 0, hyp)
smallx <- cos(theta * deg2rad) * smallhyp
smally <- sin(theta * deg2rad) * smallhyp
new.x <- x1 + smallx
new.y <- y1 + smally
}
if (quad == 6) {
theta <- 180 - seg.angle
diffx <- x2 - x1
diffy <- y1 - y2
hyp <- get.hypot.f(diffx, diffy)
smallhyp <- runif(1, 0, hyp)
smallx <- sin(theta * deg2rad) * smallhyp
smally <- cos(theta * deg2rad) * smallhyp
new.x <- x1 + smallx
new.y <- y1 - smally
}
if (quad == 7) {
theta <- seg.angle - 180
diffx <- x2 - x1
diffy <- y1 - y2
hyp <- get.hypot.f(diffx, diffy)
smallhyp <- runif(1, 0, hyp)
smallx <- sin(theta * deg2rad) * smallhyp
smally <- cos(theta * deg2rad) * smallhyp
new.x <- x1 - smallx
new.y <- y1 - smally
}
if (quad == 8) {
theta <- seg.angle - 270
diffx <- x1 - x2
diffy <- y1 - y2
hyp <- get.hypot.f(diffx, diffy)
smallhyp <- runif(1, 0, hyp)
smallx <- cos(theta * deg2rad) * smallhyp
smally <- sin(theta * deg2rad) * smallhyp
new.x <- x1 - smallx
new.y <- y1 + smally
}
new.coords <- c(new.x, new.y)
new.coords
}
get.coords.f <- function(quad = NULL, alpha = NULL, new.x = NULL, new.y = NULL, pd = NULL, side = NULL) {
deg2rad <- pi / 180
if (quad == 1 | quad == 3) {
sgt.x <- new.x + (side * pd)
sgt.y <- new.y
}
if (quad == 2 | quad == 4) {
sgt.x <- new.x
sgt.y <- new.y + (side * pd)
}
if (quad == 5) {
theta <- 90 - alpha
x1 <- sin(theta * deg2rad) * pd
y1 <- sqrt(pd^2 - x1^2)
if (side == 1) {
sgt.x <- new.x - x1
sgt.y <- new.y + y1
}
if (side == (-1)) {
sgt.x <- new.x + x1
sgt.y <- new.y - y1
}
}
if (quad == 6) {
theta <- alpha - 90
x1 <- pd / tan(deg2rad * theta)
y1 <- sin(theta * deg2rad) * x1
x2 <- sqrt(pd^2 - y1^2)
if (side == 1) {
sgt.x <- new.x + x2
sgt.y <- new.y + y1
}
if (side == (-1)) {
sgt.x <- new.x - x2
sgt.y <- new.y - y1
}
}
if (quad == 7) {
theta <- 270 - alpha
x1 <- sin(theta * deg2rad) * pd
y1 <- sqrt(pd^2 - x1^2)
if (side == 1) {
sgt.x <- new.x - x1
sgt.y <- new.y + y1
}
if (side == (-1)) {
sgt.x <- new.x + x1
sgt.y <- new.y - y1
}
}
if (quad == 8) {
theta <- 360 - alpha
x1 <- pd / tan(deg2rad * theta)
x2 <- sin(theta * deg2rad) * x1
y1 <- sqrt(pd^2 - x2^2)
if (side == 1) {
sgt.x <- new.x + x2
sgt.y <- new.y + y1
}
if (side == (-1)) {
sgt.x <- new.x - x2
sgt.y <- new.y - y1
}
}
sgt.coord <- c(sgt.x, sgt.y)
sgt.coord
}
get.hypot.f <- function(side1, side2) {
hyp <- sqrt(side1^2 + side2^2)
hyp
}
what.angle.f <- function(dy = NULL, dx = NULL, quad = NULL) {
what.angle <- atan(dy / dx) * (180 / pi)
if (quad == 1) angle <- 0
if (quad == 2) angle <- 90
if (quad == 3) angle <- 180
if (quad == 4) angle <- 270
if (quad == 5) angle <- 90 - abs(what.angle)
if (quad == 6) angle <- 90 + abs(what.angle)
if (quad == 7) angle <- 180 + (90 - abs(what.angle))
if (quad == 8) angle <- 270 + abs(what.angle)
angle
} |
check.mat <- function(H) {
if (isSymmetric(H) == FALSE) {stop("H must be a symmetric matrix")}
if (all(diag(H)>0) == FALSE) {stop("The diagonal elements of H must be positive")}
if (all(diag(H) == floor(diag(H))) == FALSE) {stop("The diagonal elements of H must be integers")}
if (mean(diag(H) == -apply(H-diag(diag(H)),1,sum)) != 1 | mean(diag(H) == -apply(H-diag(diag(H)),2,sum)) != 1) {stop("The diagonal elements of H should equal the number of neighbors for each region")}
if (sum(eigen(H)$values < 1e-5) > 1) {stop("The specified region must be contiguous for this analysis")}
} |
data_dir <- file.path("..", "testdata")
tempfile_nc <- function() {
tempfile_helper("cmsaf.add_")
}
file_out <- tempfile_nc()
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out)
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- c(seq(480, 524, by = 2),
seq(480, 524, by = 2),
seq(480, 484, by = 2))
expected <- array(expected_data, dim = c(7, 7))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
actual <- ncatt_get(file, "SIS", "standard_name")$value
expect_equal(actual, "SIS_standard")
actual <- ncatt_get(file, "SIS", "long_name")$value
expect_equal(actual, "Surface Incoming Shortwave Radiation")
actual <- ncatt_get(file, "SIS", "units")$value
expect_equal(actual, "W m-2")
actual <- ncatt_get(file, "SIS", "_FillValue")$value
expect_equal(actual, -999)
actual <- ncatt_get(file, "SIS", "cmsaf_info")$value
expect_equal(actual, "cmsafops::cmsaf.add for variable SIS")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(149016))
})
nc_close(file)
file_out <- tempfile_nc()
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out, nc34 = 4)
file <- nc_open(file_out)
test_that("data is correct in version 4", {
actual <- ncvar_get(file)
expected_data <- c(seq(480, 524, by = 2),
seq(480, 524, by = 2),
seq(480, 484, by = 2))
expected <- array(expected_data, dim = c(7, 7))
expect_equivalent(actual, expected)
})
test_that("attributes are correct in version 4", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct in version 4", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(149016))
})
nc_close(file)
file_out <- tempfile_nc()
test_that("error is thrown if ncdf version is wrong", {
expect_error(
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out, nc34 = 7),
"nc version must be in c(3, 4), but was 7", fixed = TRUE
)
})
file_out <- tempfile_nc()
test_that("ncdf version NULL throws an error", {
expect_error(
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out, nc34 = NULL),
"nc_version must not be NULL"
)
})
file_out <- tempfile_nc()
test_that("warning is shown if var does not exist", {
expect_warning(
cmsaf.add("lat", "lon",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out),
"Variable 'lat' not found. Variable 'SIS' will be used instead."
)
})
file <- nc_open(file_out)
test_that("data is correct if non-existing variable is given", {
actual <- ncvar_get(file)
expected_data <- c(seq(480, 524, by = 2),
seq(480, 524, by = 2),
seq(480, 484, by = 2))
expected <- array(expected_data, dim = c(7, 7))
expect_equivalent(actual, expected)
})
test_that("attributes are correct if non-existing variable is given", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct if non-existing variable is given", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(149016))
})
nc_close(file)
file_out <- tempfile_nc()
test_that("error is thrown if variable is NULL", {
expect_error(
cmsaf.add(NULL, "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out),
"variable must not be NULL"
)
})
file_out <- tempfile_nc()
test_that("warning is shown if var is empty", {
expect_warning(
cmsaf.add("SIS", "",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out),
"Variable '' not found. Variable 'SIS' will be used instead.")
})
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- c(seq(480, 524, by = 2),
seq(480, 524, by = 2),
seq(480, 484, by = 2))
expected <- array(expected_data, dim = c(7, 7))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(149016))
})
nc_close(file)
file_out <- tempfile_nc()
test_that("error is thrown if input file does not exist", {
expect_error(
cmsaf.add("SIS", "SIS",
file.path(data_dir, "xemaple1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out),
"Input file does not exist")
})
file_out <- tempfile_nc()
test_that("error is thrown if input filename is empty", {
expect_error(
cmsaf.add("SIS", "SIS",
"",
file.path(data_dir, "ex_normal2.nc"),
file_out),
"Input file does not exist")
})
file_out <- tempfile_nc()
test_that("error is thrown if input filename is NULL", {
expect_error(
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
NULL,
file_out),
"Input filepath must be of length one and not NULL")
})
file_out <- tempfile_nc()
cat("test\n", file = file_out)
test_that("error is thrown if output file already exists", {
expect_error(
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out),
paste0("File '",
file_out,
"' already exists. Specify 'overwrite = TRUE' if you want to overwrite it."),
fixed = TRUE
)
expect_equal(readLines(con = file_out), "test")
})
file_out <- tempfile_nc()
cat("test\n", file = file_out)
test_that("no error is thrown if overwrite = TRUE", {
expect_error(
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out,
overwrite = TRUE),
NA
)
})
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- c(seq(480, 524, by = 2),
seq(480, 524, by = 2),
seq(480, 484, by = 2))
expected <- array(expected_data, dim = c(7, 7))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(149016))
})
nc_close(file)
file_out <- tempfile_nc()
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_time_dim1.nc"),
file.path(data_dir, "ex_time_dim2.nc"),
file_out)
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- rep(seq(480, 524, by = 2), 4)
expected <- array(expected_data, dim = c(7, 7, 2))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(c(149016, 158544)))
})
nc_close(file)
file_out <- tempfile_nc()
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_time_dim2.nc"),
file_out)
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- c(rep(seq(480, 524, by = 2), 2),
seq(480, 484, by = 2),
seq(483, 521, by = 2),
seq(500, 504, by = 2),
seq(483, 521, by = 2),
seq(500, 504, by = 2),
seq(483, 487, by = 2))
expected <- array(expected_data, dim = c(7, 7, 2))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(c(167976, 177480)))
})
nc_close(file)
file_out <- tempfile_nc()
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_time_dim1.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out)
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- c(rep(seq(480, 524, by = 2), 2),
seq(480, 484, by = 2),
seq(483, 521, by = 2),
seq(500, 504, by = 2),
seq(483, 521, by = 2),
seq(500, 504, by = 2),
seq(483, 487, by = 2))
expected <- array(expected_data, dim = c(7, 7, 2))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(c(149016, 158544)))
})
nc_close(file)
file_out <- tempfile_nc()
test_that("error is thrown if dimensions do not match", {
expect_error(cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_time_dim3.nc"),
file.path(data_dir, "ex_time_dim2.nc"),
file_out), "Uncompatible time lengths!")
})
file_out <- tempfile_nc()
test_that("error is thrown if dimensions don't match", {
expect_error(cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_normal1.nc"),
file.path(data_dir, "ex_different_lon_length.nc"),
file_out),
"Dimensions of infiles do not match!")
})
file_out <- tempfile_nc()
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_additional_attr.nc"),
file.path(data_dir, "ex_normal2.nc"),
file_out)
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- c(seq(480, 524, by = 2),
seq(480, 524, by = 2),
seq(480, 484, by = 2))
expected <- array(expected_data, dim = c(7, 7))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 2)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
actual <- names(global_attr[2])
expect_equal(actual, "institution")
actual <- global_attr[[2]]
expect_equal(actual, "This is a test attribute.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(149016))
})
nc_close(file)
file_out <- tempfile_nc()
cmsaf.add("SIS", "SIS",
file.path(data_dir, "ex_v4_1.nc"),
file.path(data_dir, "ex_v4_2.nc"),
file_out)
file <- nc_open(file_out)
test_that("data is correct", {
actual <- ncvar_get(file)
expected_data <- c(seq(480, 524, by = 2),
seq(480, 524, by = 2),
seq(480, 484, by = 2))
expected <- array(expected_data, dim = c(7, 7))
expect_equivalent(actual, expected)
})
test_that("attributes are correct", {
actual <- ncatt_get(file, "lon", "units")$value
expect_equal(actual, "degrees_east")
actual <- ncatt_get(file, "lon", "long_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "standard_name")$value
expect_equal(actual, "longitude")
actual <- ncatt_get(file, "lon", "axis")$value
expect_equal(actual, "X")
actual <- ncatt_get(file, "lat", "units")$value
expect_equal(actual, "degrees_north")
actual <- ncatt_get(file, "lat", "long_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "standard_name")$value
expect_equal(actual, "latitude")
actual <- ncatt_get(file, "lat", "axis")$value
expect_equal(actual, "Y")
actual <- ncatt_get(file, "time", "units")$value
expect_equal(actual, "hours since 1983-01-01 00:00:00")
actual <- ncatt_get(file, "time", "long_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "standard_name")$value
expect_equal(actual, "time")
actual <- ncatt_get(file, "time", "calendar")$value
expect_equal(actual, "standard")
global_attr <- ncatt_get(file, 0)
expect_equal(length(global_attr), 1)
actual <- names(global_attr[1])
expect_equal(actual, "Info")
actual <- global_attr[[1]]
expect_equal(actual, "Created with the CM SAF R Toolbox.")
})
test_that("coordinates are correct", {
actual <- ncvar_get(file, "lon")
expect_identical(actual, array(seq(5, 8, 0.5)))
actual <- ncvar_get(file, "lat")
expect_identical(actual, array(seq(45, 48, 0.5)))
actual <- ncvar_get(file, "time")
expect_equal(actual, array(149016))
})
nc_close(file) |
context("Function scenarioBuilder")
sapply(studies, function(study) {
setup_study(study, sourcedir)
opts <- antaresRead::setSimulationPath(studyPath, "input")
test_that("scenarioBuilder works", {
sbuilder <- scenarioBuilder(
n_scenario = 2,
n_mc = 2,
areas = c("fr", "it", "be"),
areas_rand = c("it", "be")
)
sb <- structure(
c("1", "rand", "rand", "2", "rand", "rand"),
.Dim = 3:2,
.Dimnames = list(c("fr", "it", "be"), NULL)
)
expect_identical(sbuilder, sb)
})
test_that("scenarioBuilder works when areas_rand has length 1", {
sbuilder <- scenarioBuilder(
n_scenario = 2,
n_mc = 2,
areas = c("fr", "it", "be"),
areas_rand = "it"
)
sb <- structure(
c("1", "rand", "1", "2", "rand", "2"),
.Dim = 3:2,
.Dimnames = list(c("fr", "it", "be"), NULL)
)
expect_identical(sbuilder, sb)
})
test_that("Warning is thrown when n_mc differs from nbyears", {
expect_warning(scenarioBuilder(
n_scenario = 2,
n_mc = 3,
areas = c("fr", "it", "be"),
areas_rand = "it"
))
})
test_that("readScenarioBuilder works", {
expect_identical(
readScenarioBuilder(),
list(
l = structure(
c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Dim = c(9L, 2L), .Dimnames = list(
c("a", "a_offshore", "b", "c", "hub", "psp in", "psp in-2",
"psp out", "psp out-2"), NULL)
),
t = structure(
c(1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 2L, 2L, 2L, 2L, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA), .Dim = c(15L, 2L),
.Dimnames = list(
c("a_base", "a_base_must_run",
"a_peak", "a_peak_must_run_partial", "a_semi base", "b_base",
"b_peak", "b_semi base", "c_base", "c_peak", "c_semi base", "psp in-2_psp_in_2",
"psp in_psp_in", "psp out-2_psp_out_2", "psp out_psp_out"),
NULL
)
)
)
)
expect_warning(readScenarioBuilder(ruleset = "fake ruleset name"))
})
test_that("updateScenarioBuilder works", {
m <- scenarioBuilder(
n_scenario = 2,
n_mc = 2,
areas = c("a", "b", "c"),
areas_rand = c("b", "c")
)
m2 <- scenarioBuilder(
n_scenario = 2,
n_mc = 2,
areas = c("a", "b", "c"),
areas_rand = "c"
)
expect_error(updateScenarioBuilder(ldata = m))
expect_error(updateScenarioBuilder(ldata = m, series = "h"), NA)
updateScenarioBuilder(ldata = list(w = m, s = m2))
newSB <- readScenarioBuilder(as_matrix = TRUE)
m <- m[m[, 1] != "rand", , drop = FALSE]
m_out <- apply(m, 2, as.integer)
attributes(m_out) <- attributes(m)
expect_identical(newSB[["w"]]["a", , drop = FALSE], m_out)
m2 <- m2[m2[, 1] != "rand", , drop = FALSE]
m2_out <- apply(m2, 2, as.integer)
attributes(m2_out) <- attributes(m2)
expect_identical(newSB[["s"]][c("a", "b"), , drop = FALSE], m2_out)
})
test_that("clearScenarioBuilder works", {
expect_true(clearScenarioBuilder())
expect_length(readScenarioBuilder(), 0L)
})
unlink(x = file.path(pathstd, "test_case"), recursive = TRUE)
}) |
context("CHECK_X")
mat <- matrix(0, 10, 10)
X <- as.big.matrix(mat)
test_that("is a big.matrix?", {
expect_error(check_X(mat), ERROR_BIGMATRIX, fixed = TRUE)
expect_null(check_X(X))
}) |
setMethod("split_by_doc_id",
signature=signature(obj="kRp.corpus"),
function(obj, keepFeatures=TRUE){
return(
split_by_doc_id(
kRp_text(
lang=language(obj),
desc=describe(obj),
tokens=taggedText(obj),
features=slot(obj, "features"),
feat_list=slot(obj, "feat_list")
),
keepFeatures=keepFeatures
)
)
}
) |
"getStartTri" <- function(xx, nexp, ncomp){
y <- as.matrix(rowSums(xx))
dim(y) <- c(ncomp, nexp)
ampList <- vector("list", length=nexp)
for(i in 1:nexp) ampList[[i]] <- rep(0,ncomp)
fixedamps <- vector("list", length=ncomp)
for(i in 1:ncomp) {
ycol <- y[i,]
mind <- which(ycol==max(ycol))
ycol <- ycol / max(ycol)
fixedamps[[i]] <- c(mind,i)
for(j in 1:nexp)
ampList[[j]][i] <- ycol[j]
}
list(fixedamps=fixedamps, ampList=ampList)
} |
.euclideanDist.forNCP = function(x, y, use = 'p')
{
x = as.matrix(x);
y = as.matrix(y);
ny = ncol(y)
diff = matrix(NA, ncol(x), ny);
for (cy in 1:ny)
diff[, cy] = apply( (x - y[, cy])^2, 2, sum, na.rm = TRUE);
-diff;
}
nearestCentroidPredictor = function(
x, y,
xtest = NULL,
featureSignificance = NULL,
assocFnc = "cor", assocOptions = "use = 'p'",
assocCut.hi = NULL, assocCut.lo = NULL,
nFeatures.hi = 10, nFeatures.lo = 10,
weighFeaturesByAssociation = 0,
scaleFeatureMean = TRUE, scaleFeatureVar = TRUE,
centroidMethod = c("mean", "eigensample"),
simFnc = "cor", simOptions = "use = 'p'",
useQuantile = NULL,
sampleWeights = NULL,
weighSimByPrediction = 0,
CVfold = 0, returnFactor = FALSE,
randomSeed = 12345,
verbose = 2, indent = 0)
{
centroidMethod = match.arg(centroidMethod);
if (simFnc=="dist")
{
if (verbose > 0)
printFlush(paste("NearestCentroidPredictor: 'dist' is changed to a suitable",
"Euclidean distance function.\n",
" Note: simOptions will be disregarded."));
simFnc = ".euclideanDist.forNCP";
simOptions = "use = 'p'"
}
ySaved = y;
originalYLevels = sort(unique(y)) ;
y = as.numeric(as.factor(y));
x = as.matrix(x);
doTest = !is.null(xtest)
if (doTest)
{
xtest = as.matrix(xtest);
nTestSamples = nrow(xtest);
if (ncol(x)!=ncol(xtest))
stop("Number of learning and testing predictors (columns of x, xtest) must equal.");
} else {
if (weighSimByPrediction > 0)
stop("weighting similarity by prediction is not possible when xtest = NULL.");
nTestSamples = 0;
}
numYLevels = sort(unique(y));
minY = min(y);
maxY = max(y);
nSamples = length(y);
nVars = ncol(x);
if (!is.null(assocCut.hi))
{
if (is.null(assocCut.lo)) assocCut.lo = -assocCut.hi;
}
spaces = indentSpaces(indent);
if (!is.null(useQuantile))
{
if ( (useQuantile < 0) | (useQuantile > 1) )
stop("If 'useQuantile' is given, it must be between 0 and 1.");
}
if (is.null(sampleWeights)) sampleWeights = rep(1, nSamples);
if (CVfold > 0)
{
if (CVfold > nSamples )
{
printFlush("'CVfold' is larger than number of samples. Will perform leave-one-out cross-validation.");
CVfold = nSamples;
}
ratio = nSamples/CVfold;
if (floor(ratio)!=ratio)
{
smaller = floor(ratio);
nLarger = nSamples - CVfold * smaller
binSizes = c(rep(smaller, CVfold-nLarger), rep(smaller +1, nLarger));
} else
binSizes = rep(ratio, CVfold);
if (!is.null(randomSeed))
{
if (exists(".Random.seed"))
{
saved.seed = .Random.seed;
seedSaved = TRUE;
} else
seedSaved = FALSE;
set.seed(randomSeed);
}
sampleOrder = sample(1:nSamples);
CVpredicted = rep(NA, nSamples);
CVbin = rep(0, nSamples);
if (verbose > 0)
{
cat(paste(spaces, "Running cross-validation: "));
if (verbose==1) pind = initProgInd() else printFlush("");
}
if (!is.null(featureSignificance))
printFlush(paste("Warning in nearestCentroidPredictor: \n",
" cross-validation will be biased if featureSignificance was derived",
"from training data."));
ind = 1;
for (cv in 1:CVfold)
{
if (verbose > 1) printFlush(paste("..cross validation bin", cv, "of", CVfold));
end = ind + binSizes[cv] - 1;
samples = sampleOrder[ind:end];
CVbin[samples] = cv;
xCVtrain = x[-samples, , drop = FALSE];
xCVtest = x[samples, , drop = FALSE];
yCVtrain = y[-samples];
yCVtest = y[samples];
CVsampleWeights = sampleWeights[-samples];
pr = nearestCentroidPredictor(xCVtrain, yCVtrain, xCVtest,
featureSignificance = featureSignificance,
assocCut.hi = assocCut.hi,
assocCut.lo = assocCut.lo,
nFeatures.hi = nFeatures.hi,
nFeatures.lo = nFeatures.lo,
useQuantile = useQuantile,
sampleWeights = CVsampleWeights,
CVfold = 0, returnFactor = FALSE,
randomSeed = randomSeed,
centroidMethod = centroidMethod,
assocFnc = assocFnc, assocOptions = assocOptions,
scaleFeatureMean = scaleFeatureMean,
scaleFeatureVar = scaleFeatureVar,
simFnc = simFnc, simOptions = simOptions,
weighFeaturesByAssociation = weighFeaturesByAssociation,
weighSimByPrediction = weighSimByPrediction,
verbose = verbose - 2, indent = indent + 1)
CVpredicted[samples] = pr$predictedTest;
ind = end + 1;
if (verbose==1) pind = updateProgInd(cv/CVfold, pind);
}
if (verbose==1) printFlush("");
}
if (nrow(x)!=length(y))
stop("Number of observations in x and y must equal.");
xWeighted = x * sampleWeights;
yWeighted = y * sampleWeights;
if (is.null(featureSignificance))
{
corEval = parse(text = paste(assocFnc, "(xWeighted, yWeighted, ", assocOptions, ")"));
featureSignificance = as.vector(eval(corEval));
} else {
if (length(featureSignificance)!=nVars)
stop("Given 'featureSignificance' has incorrect length (must be nFeatures).");
}
nGood = nVars;
nNA = sum(is.na(featureSignificance));
testCentroidSimilarities = list();
xSD = apply(x, 2, sd, na.rm = TRUE);
keep = is.finite(featureSignificance) & (xSD>0);
nKeep = sum(keep);
keepInd = c(1:nVars)[keep];
order = order(featureSignificance[keep]);
levels = sort(unique(y));
nLevels = length(levels);
if (is.null(assocCut.hi))
{
nf = c(nFeatures.hi, nFeatures.lo);
if (nf[2] > 0) ind1 = c(1:nf[2]) else ind1 = c();
if (nf[1] > 0) ind2 = c((nKeep-nf[1] + 1):nKeep) else ind2 = c();
indexSelect = unique(c(ind1, ind2));
if (length(indexSelect) < 1)
stop("No features were selected. At least one of 'nFeatures.hi', 'nFeatures.lo' must be nonzero.");
indexSelect = indexSelect[indexSelect > 0];
select = keepInd[order[indexSelect]];
} else {
indexSelect = (1:nKeep)[ featureSignificance[keep] >= assocCut.hi |
featureSignificance[keep] <= assocCut.lo ]
if (length(indexSelect)<2)
stop(paste("'assocCut.hi'", assocCut.hi, "and assocCut.lo", assocCut.lo,
"are too stringent, less than 3 features were selected.\n",
"Please relax the cutoffs."));
select = keepInd[indexSelect];
}
if ((length(select) < 3) && (simFnc!='dist'))
{
stop(paste("Less than 3 features were selected. Please either relax",
"the selection criteria of use simFnc = 'dist'."));
}
selectedFeatures = select;
nSelect = length(select);
xSel = x[, select];
selectSignif = featureSignificance[select];
if (scaleFeatureMean)
{
if (scaleFeatureVar)
{
xSD = apply(xSel, 2, sd, na.rm = TRUE);
} else
xSD = rep(1, nSelect);
xMean = apply(xSel, 2, mean, na.rm = TRUE);
} else {
if (scaleFeatureVar)
{
xSD = sqrt(apply(xSel^2, 2, sum, na.rm = TRUE)) / pmax(apply(!is.na(xSel), 2, sum) - 1, rep(1, nSelect));
} else
xSD = rep(1, nSelect);
xMean = rep(0, nSelect);
}
xSel = scale(xSel, center = scaleFeatureMean, scale = scaleFeatureVar);
if (doTest)
{
xtestSel = xtest[, select];
xtestSel = (xtestSel - matrix(xMean, nTestSamples, nSelect, byrow = TRUE) ) /
matrix(xSD, nTestSamples, nSelect, byrow = TRUE);
} else
xtestSel = NULL;
xWeighted = xSel * sampleWeights;
if (weighSimByPrediction > 0)
{
pr = .quickGeneVotingPredictor.CV(xSel, xtestSel, c(1:nSelect))
dCV = sqrt(colMeans( (pr$CVpredicted - xSel)^2, na.rm = TRUE));
dTS = sqrt(colMeans( (pr$predictedTest - xtestSel)^2, na.rm = TRUE));
dTS[dTS==0] = min(dTS[dTS>0]);
validationWeight = (dCV/dTS)^weighSimByPrediction;
validationWeight[validationWeight > 1] = 1;
} else
validationWeight = rep(1, nSelect);
nTestSamples = if (doTest) nrow(xtest) else 0;
predicted = rep(0, nSamples);
predictedTest = rep(0, nTestSamples);
clusterLabels = list();
clusterNumbers = list();
if ( (centroidMethod=="eigensample") )
{
if (sum(is.na(xSel)) > 0)
{
xImp = t(impute.knn(t(xSel), k = min(10, nSelect - 1))$data);
} else
xImp = xSel;
if (doTest && sum(is.na(xtestSel))>0)
{
xtestImp = t(impute.knn(t(xtestSel), k = min(10, nSelect - 1))$data);
} else
xtestImp = xtestSel;
}
clusterNumbers = rep(1, nLevels);
sampleModules = list();
for (l in 1:nLevels)
clusterLabels[[l]] = rep(l, sum(y==levels[l]))
nClusters = sum(clusterNumbers);
centroidSimilarities = array(NA, dim = c(nSamples, nClusters));
testCentroidSimilarities = array(NA, dim = c(nTestSamples, nClusters));
cluster2level = rep(c(1:nLevels), clusterNumbers);
featureWeight = validationWeight;
if (is.null(useQuantile))
{
centroidProfiles = array(0, dim = c(nSelect, nClusters));
for (cl in 1:nClusters)
{
l = cluster2level[cl];
clusterSamples = c(1:nSamples)[ y==l ] [ clusterLabels[[l]]==cl ];
if (centroidMethod=="mean")
{
centroidProfiles[, cl] = apply(xSel[clusterSamples, , drop = FALSE],
2, mean, na.rm = TRUE);
} else if (centroidMethod=="eigensample")
{
cp = svd(xSel[clusterSamples,], nu = 0, nv = 1)$v[, 1];
cor = cor(t(xSel[clusterSamples,]), cp);
if (sum(cor, na.rm = TRUE) < 0) cp = -cp;
centroidProfiles[, cl] = cp;
}
}
if (weighFeaturesByAssociation > 0)
featureWeight = featureWeight * sqrt(abs(selectSignif)^weighFeaturesByAssociation);
wcps = centroidProfiles * featureWeight;
wxSel = t(xSel) * featureWeight;
distExpr = spaste( simFnc, "( wcps, wxSel, ", simOptions, ")");
sample.centroidSim = eval(parse(text = distExpr));
if (doTest)
{
wxtestSel = t(xtestSel) * featureWeight
distExpr = spaste( simFnc, "( wcps, wxtestSel, ", simOptions, ")");
testSample.centroidSim = eval(parse(text = distExpr));
}
} else {
labelVector = y;
for (l in 1:nLevels)
labelVector[y==l] = clusterLabels[[l]];
keepSamples = labelVector!=0;
nKeepSamples = sum(keepSamples);
keepLabels = labelVector[keepSamples];
if (weighFeaturesByAssociation > 0)
featureWeight = featureWeight * sqrt(abs(selectSignif)^weighFeaturesByAssociation);
wxSel = t(xSel) * featureWeight;
wxSel.keepSamples = t(xSel[keepSamples, ]) * featureWeight;
distExpr = spaste( simFnc, "( wxSel.keepSamples, wxSel, ", simOptions, ")");
dst = eval(parse(text = distExpr));
if (doTest)
{
wxtestSel = t(xtestSel) * featureWeight
distExpr = spaste( simFnc, "( wxSel.keepSamples, wxtestSel, ", simOptions, ")");
dst.test = eval(parse(text = distExpr));
sample.centroidSim = matrix(0, nClusters, nSamples);
testSample.centroidSim = matrix(0, nClusters, nTestSamples);
}
for (l in 1:nClusters)
{
lSamples = c(1:nKeepSamples)[keepLabels==l];
sample.centroidSim[l, ] = colQuantileC(dst[lSamples, ], 1-useQuantile);
testSample.centroidSim[l, ] = colQuantileC(dst.test[lSamples, ], 1-useQuantile);
}
}
centroidSimilarities = t(sample.centroidSim);
prediction = cluster2level[apply(sample.centroidSim, 2, which.max)];
predicted = prediction;
if (doTest)
{
testCentroidSimilarities = t(testSample.centroidSim);
testprediction = cluster2level[apply(testSample.centroidSim, 2, which.max)];
predictedTest = testprediction;
}
if (returnFactor)
{
predicted.out = factor(originalYLevels[[t]][predicted])
if (doTest) predictedTest.out = factor(originalYLevels[[t]][predictedTest]);
if (CVfold > 0)
CVpredicted.out = factor(originalYLevels[[t]][CVpredicted]);
} else {
predicted.out = originalYLevels[predicted];
if (doTest) predictedTest.out = originalYLevels[predictedTest];
if (CVfold > 0)
CVpredicted.out = originalYLevels[CVpredicted];
}
out = list(predicted = predicted.out,
predictedTest = if (doTest) predictedTest.out else NULL,
featureSignificance = featureSignificance,
selectedFeatures = selectedFeatures,
centroidProfiles = if (is.null(useQuantile)) centroidProfiles else NULL,
testSample2centroidSimilarities = if (doTest) testCentroidSimilarities else NULL,
featureValidationWeights = validationWeight
)
if (CVfold > 0)
out$CVpredicted = CVpredicted.out;
out;
} |
get_par_filenames = function(ids = get_ids(),
modalities = c("FLAIR", "MPRAGE", "T2w",
"fMRI", "DTI"),
visits = c(1,2)){
modalities = unique(modalities)
visits = as.numeric(visits)
visits = sprintf("%02.0f", visits)
v_ids = c(outer(ids, visits, paste, sep="-"))
fnames = c(outer(v_ids, modalities, paste, sep="-"))
fnames = paste0(fnames, ".par.gz")
df = data.frame(fname = fnames, stringsAsFactors = FALSE)
ss = strsplit(df$fname, "-")
df$id = sapply(ss, `[`, 1)
df$visit = as.numeric(sapply(ss, `[`, 2))
df$fname = file.path(paste0("visit_", df$visit), df$id, df$fname)
df$id = NULL
filenames = system.file( df$fname, package="kirby21")
return(filenames)
} |
context("config_file_location")
skip_on_cran()
loc <- config_file_location()
test_that("config_file_location returns a character", {
expect_is(loc, "character")
})
test_that("should be able to set and get a default qsub config", {
if (file.exists(loc)) {
prev <- get_default_qsub_config()
} else {
prev <- NULL
}
on.exit({
set_default_qsub_config(prev)
})
config <- create_qsub_config(remote = "foo", local_tmp_path = "/help", remote_tmp_path = "/bar")
set_default_qsub_config(config)
expect_equal(get_default_qsub_config(), config)
config2 <- create_qsub_config(remote = "foo2", local_tmp_path = "/help2", remote_tmp_path = "/bar2")
set_default_qsub_config(config2, permanent = FALSE)
expect_equal(get_default_qsub_config(), config2)
set_default_qsub_config(NULL, permanent = FALSE)
expect_equal(get_default_qsub_config(), config)
}) |
plot.lab.qcs <- function(x, title = NULL, xlab = NULL, ylab = NULL, col = NULL, ylim = NULL, ...)
{
if(!is.null(x) & !inherits(x, "lab.qcs") & !is.list(x))
stop("x must be an objects of class (or extending) 'lab.qcs'")
data.name <- attributes(x)$object.name
type.data <- attributes(x)$type.data
oldpar <- par(mar = c(4, 3, 1, 1) + 0.1)
if (is.null(title)) title <- data.name
if (is.null(xlab)) xlab <- "Laboratory"
if (is.null(ylab)) ylab <- "Statistical"
if (type.data == "lab.qcs")
{
print(dotplot(rownames(x$statistics.material) ~ S+S_r+S_B+S_R,
data = x$statistics.material,horizontal = T,
key = simpleKey(c("S","S_r","S_B","S_R"), space = "right"),
xlab = xlab,
aspect=0.5, ylab = ylab))
}
else {
st<-t(x[[6]])
p <-x$p
m <- x$m
crit <- x[[7]]
if (is.null(col)){
if (m==1){
col <- terrain.colors(p)
}else{
col <- terrain.colors(m)
}
}
legend.text = rownames(t(x[[6]]))
if (type.data=="h.qcs")
{
if (is.null(ylim)) ylim <- c(-3,3)
if (m>1){
barplot(height = st,width = 0.05,main=title,names.arg = colnames(t(x[[6]])),
beside = T,
col=col,xlab=xlab,
ylim=ylim,axisnames=T)
legend ("topleft",legend = legend.text,bty = "n",pch = 22,pt.bg = col,cex = 0.8)
}else{
barplot(height = st,width = 0.05,main=title,names.arg = colnames(t(x[[6]])),
beside = T,
col=col,xlab=xlab,
ylim=ylim,axisnames=T)
}
abline(h=c(crit,-crit),lty="dashed")
}
else{
if (is.null(ylim)) ylim <- c(0,3)
if (m>1){
barplot(height = st,width = 0.05,main=title,names.arg = colnames(t(x[[6]])),
beside = T,
horiz=F,col=col,xlab=xlab,
ylim=ylim,axisnames=T)
legend ("topleft",legend = legend.text,bty = "n",pch = 22,pt.bg = col,cex = 0.8,pt.cex = 1.5)
}else{
barplot(height = st,width = 0.05,main=title,names.arg = colnames(t(x[[6]])),
beside = T,
horiz=F,col=col,xlab=xlab,
ylim=ylim,axisnames=T)
}
abline(h=crit,lty="dashed")
}
}
par(oldpar)
} |
estimateNoise <- function(x, y, intercept = TRUE) {
n <- NROW(x)
p <- NCOL(x)
stopifnot(n > p)
fit <- stats::lm.fit(x, y)
sqrt(sum(fit$residuals^2) / (n-p+intercept))
} |
DFS_is_available <- function( henv = hive() ) {
if( DFS_is_registered(henv) ){
stat <- .DFS_stat( "/", henv )
if( !(is.null(stat) || is.na(stat)) )
return( TRUE )
}
FALSE
}
DFS_is_registered <- function(henv = hive()){
if( is.null(HDFS(henv)) || is.null(IOUTILS(henv)) ){
return(FALSE)
}
TRUE
}
DFS_file_exists <- function( file, henv = hive() ) {
stopifnot(DFS_is_registered(henv))
hdfs <- HDFS(henv)
hdfs$exists(HDFS_path(file))
}
DFS_dir_exists <- function( path, henv = hive() ) {
path <- file.path(path)
status <- tryCatch(.DFS_getFileStatus(path, henv ), error = identity)
if(inherits(status, "error"))
return(FALSE)
status$isDir()
}
DFS_dir_create <- function( path, henv = hive() ) {
if( DFS_dir_exists(path, henv) ) {
warning( sprintf("directory '%s' already exists.", path) )
return( invisible(FALSE) )
}
if( DFS_file_exists(path, henv) ) {
warning( sprintf("'%s' already exists but is not a directory", path) )
return( invisible(FALSE) )
}
status <- .DFS_mkdir( path, henv )
if( is.null(status) ) {
warning( sprintf("cannot create dir '%s'.", path) )
return( invisible(FALSE) )
}
invisible( TRUE )
}
DFS_delete <- function( file, recursive = FALSE, henv = hive() ) {
if( DFS_dir_exists(file, henv) && !recursive){
warning(sprintf("cannot remove directory '%s'. Use 'recursive = TRUE' instead.", file))
return(FALSE)
}
status <- .DFS_delete( file, henv )
if(!status){
warning(sprintf("cannot remove file '%s'.", file))
return(FALSE)
}
TRUE
}
DFS_dir_remove <- function(path, recursive = TRUE, henv = hive()){
if( DFS_dir_exists(path, henv) ){
DFS_delete(path, recursive, henv)
TRUE
} else {
warning(sprintf("'%s' is not a directory.", path))
FALSE
}
}
DFS_list <- function( path = ".", henv = hive() ) {
globstat <- .DFS_stat(path, henv)
if( is.null(globstat) ){
warning(sprintf("'%s' is not a readable directory", path))
return(character(0))
}
splitted <- strsplit(grep(path, .DFS_intern("-ls", path, henv), value = TRUE), path)
sapply(splitted, function(x) basename(x[2]))
}
DFS_cat <- function( file, con = stdout(), henv = hive() ){
stopifnot( DFS_file_exists(file, henv) )
.DFS("-cat", file, henv)
}
DFS_rename <- function( from, to, henv = hive() ){
stopifnot( DFS_file_exists(from, henv) )
.DFS_rename( from, to, henv )
}
DFS_tail <- function(file, n = 6L, size = 1024, henv = hive() ){
stopifnot( as.integer(n) > 0L )
stopifnot( DFS_file_exists(file, henv) )
out <- .DFS_tail(file, size, henv = henv)
len <- length(out)
if( len < n )
n <- len
out[(len - (n - 1)) : len]
}
.DFS_tail <- function(file, size = 1024, henv = hive()){
stopifnot(DFS_is_registered(henv))
hdfs <- HDFS(henv)
ioutils <- IOUTILS(henv)
hdfs_file <- HDFS_path(file)
len <- hdfs$getFileStatus(hdfs_file)$getLen()
offset <- ifelse(len > size, len - size, 0)
inputstream <- hdfs$open(hdfs_file)
inputstream$seek(.jlong(offset))
routput <- .jnew("org/rosuda/JRI/RConsoleOutputStream", .jengine(TRUE), as.integer(0))
out <- character(0)
con <- textConnection("out", open = "w", local = TRUE)
sink(file = con)
ioutils$copyBytes(inputstream, routput, as.integer(1024), TRUE)
sink()
close(con)
out
}
DFS_put <- function( files, path = ".", henv = hive() ) {
if(length(files) == 1)
status <- .DFS("-put", paste(files, path), henv )
else {
if( !DFS_dir_exists(path, henv) )
DFS_dir_create( path, henv )
status <- .DFS("-put", paste(paste(files, collapse = " "), path), henv )
}
if( status ){
warning( sprintf("Cannot put file(s) to '%s'.", path) )
return( invisible(FALSE) )
}
invisible( TRUE )
}
DFS_put_object <- function( obj, file, henv = hive() ) {
con <- .DFS_pipe( "-put", file, open = "w", henv = henv )
status <- tryCatch(serialize( obj, con ), error = identity)
close.connection(con)
if(inherits(status, "error"))
stop("Serialization failed.")
invisible(file)
}
DFS_write_lines <- function( text, file, henv = hive() ) {
stopifnot(DFS_is_registered(henv = henv))
if(DFS_file_exists(file)){
warning(sprintf("file '%s' already exists.", file))
return(NA)
}
if(!length(text))
stop("text length of zero not supported.")
hdfs <- HDFS(henv)
outputstream <- hdfs$create(HDFS_path(file))
for( i in seq_along(text) ){
outputstream$writeBytes(text[i])
outputstream$writeBytes("\n")
}
outputstream$close()
invisible(file)
}
DFS_read_lines <- function( file, n = -1L, henv = hive() ) {
if(!DFS_file_exists(file)){
warning(sprintf("file '%s' does not exists.", file))
return(NA)
}
hdfs <- HDFS(henv)
ioutils <- IOUTILS(henv)
offset <- 0
inputstream <- hdfs$open(HDFS_path(file))
if( n <= 0 ){
inputstream$seek(.jlong(offset))
routput <- .jnew("org/rosuda/JRI/RConsoleOutputStream", .jengine(TRUE), as.integer(0))
con <- textConnection("out", open = "w", local = TRUE)
sink(file = con)
ioutils$copyBytes(inputstream, routput, as.integer(1024), TRUE)
sink()
close(con)
}
else {
out <- character(n)
for(i in 1:n)
out[i] <- inputstream$readLine()
inputstream$close()
}
out
}
DFS_get_object <- function( file, henv = hive() ) {
con <- .DFS_pipe( "-cat", file, open = "r", henv = henv )
obj <- tryCatch( unserialize(con), error = identity)
close.connection(con)
if(inherits(obj, "error"))
return(NA)
obj
}
.DFS_delete <- function(x, henv){
stopifnot( DFS_is_registered(henv) )
hdfs <- HDFS(henv)
hdfs$delete(HDFS_path(x))
}
.DFS_mkdir <- function(x, henv){
stopifnot( DFS_is_registered(henv) )
hdfs <- HDFS(henv)
hdfs$mkdirs(HDFS_path(x))
}
.DFS_stat <- function(x, henv){
stat <- DFS_file_exists(x, henv)
if(!stat){
warning(sprintf("cannot stat '%s': No such file or directory", x))
return(NULL)
}
TRUE
}
.DFS_rename <- function( from, to, henv ){
stopifnot( DFS_is_registered(henv) )
hdfs <- HDFS(henv)
hdfs$rename(HDFS_path(from), HDFS_path(to))
}
.DFS_getFileStatus <- function(x, henv){
stopifnot( DFS_is_registered(henv) )
hdfs <- HDFS(henv)
hdfs$getFileStatus(HDFS_path(x))
}
.DFS <- function( cmd, args, henv )
system( .DFS_create_command(cmd, args, henv), ignore.stderr = TRUE )
.DFS_pipe <- function( cmd, args, open = "w", henv ){
if(open == "w")
pipe(.DFS_create_command(cmd, sprintf("- %s", args), henv), open = open)
else
pipe(.DFS_create_command(cmd, args, henv), open = open)
}
.DFS_intern <- function( cmd, args, henv )
system( .DFS_create_command(cmd, args, henv), intern = TRUE, ignore.stderr = TRUE )
.DFS_create_command <- function( cmd, args, henv )
sprintf("%s fs %s %s", hadoop(henv), cmd, args)
.DFS_format <- function(henv){
stopifnot(hive_stop(henv))
machines <- unique(c(hive_get_workers(henv), hive_get_masters(henv)))
DFS_root <- gsub("\\$\\{user.name\\}", system("whoami", intern = TRUE),
hive_get_parameter("hadoop.tmp.dir", henv))
for(machine in machines){
command <- sprintf("ssh %s 'rm -rf %s/*
rm -rf %s-*' ", machine, DFS_root, DFS_root)
system(command)
}
system(sprintf("%s namenode -format", hadoop(henv)))
} |
crono <- read.csv(text="Name,Group,start_year,end_year
First long period,long,1800-01-01,1899-12-31
Second period,short,1870-01-01,1910-12-31
Another long period,long,1900-01-01,1990-12-31
More events on period time,short,1965-01-01,1985-12-31")
events <- read.csv(text="Name,start_year
Person 1 was born,1870-01-01
Person 1 first novel,1895-01-01
Build the new building,1905-01-01
Death person 1,1930-01-01
renovation building,1950-01-01
collection,1970-01-01")
crono <- sapply(crono, as.character)
events$end_year <- NA
events$Group<-c(1,2,1,2,1,2)
events <- sapply(events, as.character)
data <- as.data.frame(rbind(crono, events[, c(1,4,2,3)]))
vistime(data, col.event="Name", col.start="start_year", col.end ="end_year", linewidth=70)
vistime(data, col.event="Name", col.start="start_year", col.end ="end_year", col.group ="Group", linewidth=70)
hc_vistime(data, col.event="Name", col.start="start_year", col.end ="end_year", col.group ="Group", ) |
x <- seq(0, 30, by=0.25)
density <- 0.3 * dnorm(x,8,2) + 0.7 * dnorm(x,16,3)
xyplot(density~x, type="l", main="pdf of a mixture of normals") |
tar_test("backoff actually slows down and then resets", {
test_backoff <- function(backoff) {
expect_equal(backoff$index, 0L)
begin <- unname(proc.time()["elapsed"])
for (index in seq_len(30L)) {
bound <- round(backoff$bound(), 4L)
backoff$wait()
end <- unname(proc.time()["elapsed"])
elapsed <- end - begin
begin <- end
msg <- paste0(
"bound: ",
bound,
" | elapsed: ",
round(elapsed, 4),
"\n"
)
cat(msg)
}
}
backoff <- backoff_init(min = 0.001, max = 2, rate = 1.5)
test_backoff(backoff)
backoff$reset()
cat("\n")
test_backoff(backoff)
})
tar_test("backoff interval is uniformly distributed", {
set.seed(1)
backoff <- backoff_init(min = 1, max = 10, rate = 2)
map(seq_len(1e3), ~backoff$increment())
out <- map_dbl(seq_len(1e5), ~backoff$interval())
expect_equal(min(out), 1, tolerance = 1e-3)
expect_equal(max(out), 10, tolerance = 1e-3)
hist(out)
}) |
mcmc_mra <- function(
y,
X,
locs,
params,
priors = NULL,
M = 4,
n_neighbors = 68,
n_coarse_grid = 10,
n_padding = 5L,
n_cores = 1L,
inits = NULL,
config = NULL,
verbose = FALSE,
use_spam = TRUE,
n_chain = 1
) {
if (!is_numeric_vector(y, length(y)))
stop("y must be a numeric vector of length N.")
if (length(y) != nrow(X))
stop("X must have the same number of rows as the length of y.")
if (!is_numeric_matrix(X, length(y), ncol(X)))
stop("X must be a numeric matrix with N rows.")
if (!is_numeric_matrix(locs, length(y), 2))
stop("locs must be a numeric matrix with N rows and 2 columns.")
if (!is_positive_integer(params$n_adapt, 1))
stop("params must contain a positive integer n_adapt.")
if (!is_positive_integer(params$n_mcmc, 1))
stop("params must contain a positive integer n_mcmc.")
if (!is_positive_integer(params$n_thin, 1))
stop("params must contain a positive integer n_thin.")
if (!is_positive_integer(params$n_message, 1))
stop("params must contain a positive integer n_message.")
params$n_adapt <- as.integer(params$n_adapt)
params$n_mcmc <- as.integer(params$n_mcmc)
params$n_thin <- as.integer(params$n_thin)
params$n_message <- as.integer(params$n_message)
if (!is.logical(verbose) || length(verbose) != 1 || is.na(verbose)) {
stop("verbose must be either TRUE or FALSE.")
}
if (!is.logical(use_spam) || length(use_spam) != 1 || is.na(use_spam)) {
stop("use_spam must be either TRUE or FALSE.")
}
if (!is_positive_integer(n_cores, 1)) {
stop("n_cores must be a positive integer")
}
n_cores <- as.integer(n_cores)
if (!is_positive_integer(n_chain, 1)) {
stop("n_chain must be a positive integer")
}
n_chain <- as.integer(n_chain)
sample_beta <- TRUE
if (!is.null(config)) {
if (!is.null(config[['sample_beta']])) {
sample_beta <- config[['sample_beta']]
if (!is.logical(sample_beta) | is.na(sample_beta))
stop('If specified, sample_beta must be TRUE or FALSE')
}
}
sample_tau2 <- TRUE
if (!is.null(config)) {
if (!is.null(config[['sample_tau2']])) {
sample_tau2 <- config[['sample_tau2']]
if (!is.logical(sample_tau2) | is.na(sample_tau2))
stop('If specified, sample_tau2 must be TRUE or FALSE')
}
}
sample_lambda <- TRUE
if (!is.null(config)) {
if (!is.null(config[['sample_lambda']])) {
sample_lambda <- config[['sample_lambda']]
if (!is.logical(sample_lambda) | is.na(sample_lambda))
stop('If specified, sample_lambda must be TRUE or FALSE')
}
}
sample_sigma2 <- TRUE
if (!is.null(config)) {
if (!is.null(config[['sample_sigma2']])) {
sample_sigma2 <- config[['sample_sigma2']]
if (!is.logical(sample_sigma2) | is.na(sample_sigma2))
stop('If specified, sample_sigma2 must be TRUE or FALSE')
}
}
sample_alpha <- TRUE
if (!is.null(config)) {
if (!is.null(config[['sample_alpha']])) {
sample_alpha <- config[['sample_alpha']]
if (!is.logical(sample_alpha) | is.na(sample_alpha))
stop('If specified, sample_alpha must be TRUE or FALSE')
}
}
num_chol_failures <- 0
N <- length(y)
p <- ncol(X)
MRA <- mra_wendland_2d(
locs = locs,
M = M,
n_coarse_grid = n_coarse_grid,
n_padding = n_padding,
n_neighbors = n_neighbors,
use_spam = use_spam
)
W_list <- MRA$W
tW_list <- vector(mode = 'list', length = M)
tWW_list <- vector(mode = 'list', length = M)
for (m in 1:M) {
if (use_spam) {
tW_list[[m]] <- t(W_list[[m]])
} else {
stop("Only support use_spam = TRUE")
}
tWW_list[[m]] <- tW_list[[m]] %*% W_list[[m]]
}
n_dims <- rep(NA, length(W_list))
dims_idx <- c()
for (i in 1:M) {
n_dims[i] <- ncol(W_list[[i]])
dims_idx <- c(dims_idx, rep(i, n_dims[i]))
}
W <- do.call(cbind, W_list)
tW <- NULL
if (use_spam) {
tW <- t(W)
} else {
stop ('Only support use_spam = TRUE')
}
tWW <- tW %*% W
tX <- t(X)
tXX <- tX %*% X
mu_beta <- rep(0, p)
Sigma_beta <- 10 * diag(p)
if (!is.null(priors[['mu_beta']])) {
if(!is_numeric_vector(priors[['mu_beta']], p))
stop("If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
if (all(!is.na(priors[['mu_beta']]))) {
mu_beta <- priors[['mu_beta']]
}
}
if (!is.null(priors[['Sigma_beta']])) {
if(!is_sympd_matrix(priors[['Sigma_beta']], p))
stop("If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
if (all(!is.na(priors[['Sigma_beta']]))) {
Sigma_beta <- priors[['Sigma_beta']]
}
}
Sigma_beta_chol <- tryCatch(
chol(Sigma_beta),
error = function(e) {
if (verbose)
message("The Cholesky decomposition of the prior covariance Sigma_beta was ill-conditioned and mildy regularized.")
chol(Sigma_beta + 1e-8 * diag(p))
}
)
Sigma_beta_inv <- chol2inv(Sigma_beta_chol)
beta <- as.vector(lm (y ~ X - 1)$coeff)
X_beta <- X %*% beta
Q_alpha <- make_Q_alpha_2d(sqrt(n_dims), rep(0.999, length(n_dims)), use_spam = use_spam)
scale_lambda <- 0.5
lambda <- rgamma(M, 0.5, scale_lambda)
tau2 <- rep(1, M)
alpha_tau2 <- 1
beta_tau2 <- 1
if (!is.null(priors[['alpha_tau2']])) {
if (!is_positive_numeric(priors[['alpha_tau2']], 1))
stop("If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
if (all(!is.na(priors[['alpha_tau2']]))) {
alpha_tau2 <- priors[['alpha_tau2']]
}
}
if (!is.null(priors[['beta_tau2']])) {
if (!is_positive_numeric(priors[['beta_tau2']], 1))
stop("If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
if (all(!is.na(priors[['beta_tau2']]))) {
beta_tau2 <- priors[['beta_tau2']]
}
}
Q_alpha_tau2 <- make_Q_alpha_tau2(Q_alpha, tau2, use_spam = use_spam)
alpha_sigma2 <- 1
beta_sigma2 <- 1
if (!is.null(priors[['alpha_sigma2']])) {
if (!is_positive_numeric(priors[['alpha_sigma2']], 1))
stop("If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
if (all(!is.na(priors[['alpha_sigma2']]))) {
alpha_sigma2 <- priors[['alpha_sigma2']]
}
}
if (!is.null(priors[['beta_sigma2']])) {
if (!is_positive_numeric(priors[['beta_sigma2']], 1))
stop("If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
if (all(!is.na(priors[['beta_sigma2']]))) {
beta_sigma2 <- priors[['beta_sigma2']]
}
}
sigma2 <- pmax(pmin(1 / rgamma(1, alpha_sigma2, beta_sigma2), 5), 0.1)
sigma <- sqrt(sigma2)
alpha <- NULL
if (use_spam) {
A_alpha <- 1 / sigma2 * tWW + Q_alpha_tau2
Rstruct <- chol(A_alpha)
b_alpha <- 1 / sigma2 * tW %*% (y - X_beta)
alpha <- rep(0, sum(n_dims))
} else {
stop("The only sparse matrix pacakage available is spam")
}
A_constraint <- t(
sapply(1:M, function(i){
tmp = rep(0, sum(n_dims))
tmp[dims_idx == i] <- 1
return(tmp)
})
)
a_constraint <- rep(0, M)
Q_alpha <- make_Q_alpha_2d(sqrt(n_dims), rep(1, length(n_dims)), use_spam = use_spam)
Q_alpha_tau2 <- make_Q_alpha_tau2(Q_alpha, tau2, use_spam = use_spam)
W_alpha <- W %*% alpha
if (!is.null(inits[['beta']])) {
if(!is_numeric_vector(inits[['beta']], p))
stop("initial value for beta must be a numeric vector of length p")
if (all(!is.na(inits[['beta']]))) {
beta <- inits[['beta']]
}
}
if (!is.null(inits[['sigma2']])) {
if(!is_positive_numeric(inits[['sigma2']], 1))
stop("initial value for sigma2 must be a positive numeric value")
if (all(!is.na(inits[['sigma2']]))) {
sigma2 <- inits[['sigma2']]
}
}
if (!is.null(inits[['alpha']])) {
if(!is_numeric_vector(inits[['alpha']], length(alpha)))
stop("initial value for alpha must be positive numeric vector of length equal to the number of all grid points")
if (all(!is.na(inits[['alpha']]))) {
alpha <- inits[['alpha']]
}
}
if (!is.null(inits[['tau2']])) {
if(!is_positive_numeric(inits[['tau2']], M) | !is.vector(inits[['tau2']]))
stop("initial value for tau2 must be a positive numeric vector of length M")
if (all(!is.na(inits[['tau2']]))) {
tau2 <- inits[['tau2']]
}
}
n_save <- params$n_mcmc / params$n_thin
beta_save <- matrix(0, n_save, p)
tau2_save <- matrix(0, n_save, M)
sigma2_save <- rep(0, n_save)
alpha_save <- matrix(0, n_save, sum(n_dims))
lambda_save <- matrix(0, n_save, M)
message("Starting MCMC for chain ", n_chain, ", running for ", params$n_adapt, " adaptive iterations and ", params$n_mcmc, " fitting iterations \n")
for (k in 1:(params$n_adapt + params$n_mcmc)) {
if (k == params$n_adapt + 1) {
message("Starting MCMC fitting for chain ", n_chain, ", running for ", params$n_mcmc, " iterations \n")
}
if (k %% params$n_message == 0) {
if (k <= params$n_adapt) {
message("MCMC adaptation iteration ", k, " for chain ", n_chain)
} else {
message("MCMC fitting iteration ", k - params$n_adapt, " for chain ", n_chain)
}
}
if (sample_sigma2) {
if (verbose)
message("sample sigma2")
devs <- y - X_beta - W_alpha
SS <- sum(devs^2)
sigma2 <- 1 / rgamma(1, N / 2 + alpha_sigma2, SS / 2 + beta_sigma2)
sigma <- sqrt(sigma2)
}
if (sample_beta) {
if (verbose)
message("sample beta")
A <- 1 / sigma2 * tXX + Sigma_beta_inv
b <- 1 / sigma2 * tX %*% (y - W_alpha) + Sigma_beta_inv %*% mu_beta
A <- (A + t(A)) / 2
beta <- rmvn_arma(A, b)
X_beta <- X %*% beta
}
if (sample_alpha) {
if (verbose)
message("sample alpha")
A_alpha <- 1 / sigma2 * tWW + Q_alpha_tau2
b_alpha <- 1 / sigma2 * tW %*% (y - X_beta)
alpha <- as.vector(rmvnorm.canonical.const(1, b_alpha, A_alpha, Rstruct = Rstruct,
A = A_constraint, a = a_constraint))
}
W_alpha <- W %*% alpha
if (sample_tau2) {
if (verbose)
message("sample tau2")
for (m in 1:M) {
devs <- alpha[dims_idx == m]
SS <- as.numeric(devs %*% (Q_alpha[[m]] %*% devs))
tau2[m] <- rgamma(1, alpha_tau2 + n_dims[m] / 2, beta_tau2 + SS / 2)
}
}
Q_alpha_tau2 <- make_Q_alpha_tau2(Q_alpha, tau2, use_spam = use_spam)
if (sample_lambda) {
if (verbose)
message("sample lambda")
for (m in 1:M) {
lambda[m] <- rgamma(1, 1, scale_lambda + 1 / tau2[m])
}
}
if (k >= params$n_adapt) {
if (k %% params$n_thin == 0) {
save_idx <- (k - params$n_adapt) / params$n_thin
beta_save[save_idx, ] <- beta
tau2_save[save_idx, ] <- tau2
sigma2_save[save_idx] <- sigma2
alpha_save[save_idx, ] <- alpha
lambda_save[save_idx, ] <- lambda
}
}
}
if (num_chol_failures > 0)
warning("The Cholesky decomposition for the update of alpha was ill-conditioned and mildy regularized ", num_chol_failures, " times. If this warning is rare, this should be safe to ignore.")
out <- list(
beta = beta_save,
tau2 = tau2_save,
sigma2 = sigma2_save,
alpha = alpha_save,
lambda = lambda_save,
MRA = MRA
)
class(out) <- "mcmc_mra"
return(out)
} |
dirm <- function(formula,
state.specification,
data,
prior = NULL,
contrasts = NULL,
na.action = na.pass,
niter,
ping = niter / 10,
model.options = DirmModelOptions(),
timestamps = NULL,
seed = NULL,
...) {
check.nonnegative.scalar(niter)
check.scalar.integer(ping)
stopifnot(is.null(seed) || length(seed) == 1)
stopifnot(inherits(model.options, "DirmModelOptions"))
if (!is.null(seed)) {
seed <- as.integer(seed)
}
function.call <- match.call()
my.model.frame <- match.call(expand.dots = FALSE)
frame.match <- match(c("formula", "data", "na.action"),
names(my.model.frame), 0L)
my.model.frame <- my.model.frame[c(1L, frame.match)]
my.model.frame$drop.unused.levels <- TRUE
if (! "na.action" %in% names(my.model.frame)) {
my.model.frame$na.action <- na.pass
}
my.model.frame[[1L]] <- as.name("model.frame")
my.model.frame <- eval(my.model.frame, parent.frame())
model.terms <- attr(my.model.frame, "terms")
predictors <- model.matrix(model.terms, my.model.frame, contrasts)
if (any(is.na(predictors))) {
stop("dirm does not allow NA's in the predictors.")
}
response <- model.response(my.model.frame, "any")
sample.size <- if (is.matrix(response)) nrow(response) else length(response)
stopifnot(nrow(predictors) == sample.size)
if (missing(data)) {
data <- NULL
}
timestamp.info <- TimestampInfo(response, data, timestamps)
data.list <- list(response = as.numeric(response),
predictors = predictors,
response.is.observed = !is.na(response),
timestamp.info = timestamp.info)
if (is.null(prior)) {
prior <- SpikeSlabPrior(
x = predictors,
y = response,
optional.coefficient.estimate = rep(0, ncol(predictors)),
sigma.upper.limit = 1.2 * sd(response, na.rm = TRUE),
...)
}
stopifnot(inherits(prior, "SpikeSlabPriorBase"))
predictor.sd <- apply(predictors, 2, sd)
prior$prior.inclusion.probabilities[predictor.sd <= 0] <- 0
if (is.null(prior$max.flips)) {
prior$max.flips <- -1
}
ans <- .Call("analysis_common_r_fit_dirm_",
data.list,
state.specification,
prior,
model.options,
niter,
ping,
seed,
PACKAGE = "bsts")
ans$has.regression <- TRUE
ans$state.specification <- state.specification
ans$prior <- prior
ans$timestamp.info <- timestamp.info
ans$niter <- niter
if (!is.null(ans$ngood)) {
ans <- .Truncate(ans)
}
ans$original.series <- response
number.of.state.components <- length(state.specification)
state.names <- character(number.of.state.components)
for (i in seq_len(number.of.state.components)) {
state.names[i] <- state.specification[[i]]$name
}
dimnames(ans$state.contributions) <- list(
mcmc.iteration = NULL,
component = state.names,
time = NULL)
ans$contrasts <- attr(predictors, "contrasts")
ans$xlevels <- .getXlevels(model.terms, my.model.frame)
ans$terms <- model.terms
ans$predictors <- predictors
variable.names <- colnames(predictors)
if (!is.null(variable.names)) {
colnames(ans$coefficients) <- variable.names
}
class(ans) <- c("DynamicIntercept", "bsts")
return(ans)
}
DirmModelOptions <- function(timeout.seconds = Inf,
high.dimensional.threshold.factor = 1.0) {
stopifnot(is.numeric(timeout.seconds),
length(timeout.seconds) == 1,
timeout.seconds >= 0)
stopifnot(is.numeric(high.dimensional.threshold.factor),
length(high.dimensional.threshold.factor) == 1,
high.dimensional.threshold.factor >= 0)
ans <- list(timeout.seconds = timeout.seconds,
high.dimensional.threshold.factor = high.dimensional.threshold.factor)
class(ans) <- "DirmModelOptions"
return(ans)
}
.RemoveIntercept <- function(predictors) {
stopifnot(is.matrix(predictors))
is.intercept <- rep(FALSE, ncol(predictors))
for (i in 1:ncol(predictors)) {
if (all(predictors[, i]) == 1) {
is.intercept[i] <- TRUE
}
}
return(predictors[, !is.intercept])
} |
dhalfcauchy <-
function(x, location = 0, scale = 1) {
ifelse(x < 0, 0, 1) * dcauchy(x, location, scale) / (1 - pcauchy(
0,
location, scale
))
} |
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
)
library(tidyverse)
library(pharmaRTF)
ht <- huxtable::as_hux(iris[, c("Species", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")],
add_colnames=TRUE)
ht[1:10, c('Species', 'Petal.Width')]
huxtable::print_screen(ht[1:10,])
huxtable::width(ht) <- 1.5
ht <- ht %>%
huxtable::set_width(1.5)
ht[1:10,]
huxtable::col_width(ht) <- c(.4, .15, .15, .15, .15)
ht <- ht %>%
huxtable::set_col_width(c(.4, .15, .15, .15, .15))
ht[1:10,]
ht[1,]
column_headers <- data.frame(
Species = "Species of Flower",
Sepal.Length = "Sepal Length",
Sepal.Width = "Sepal Width",
Petal.Length = "Petal Length",
Petal.Width = "Petal Width",
stringsAsFactors = FALSE
)
ht <- huxtable::as_hux(rbind(column_headers, iris))
ht[1:10,]
huxtable::bold(ht[1, ]) <- TRUE
huxtable::italic(ht[2, ]) <- TRUE
ht <- ht %>%
huxtable::set_bottom_border(1, 1:ncol(ht), 1) %>%
huxtable::set_bottom_border(2, 1:ncol(ht), 4) %>%
huxtable::set_align(3, 2, 'right')
ht[1:10,]
column_headers <- data.frame(
Species = c("", "Species of Flower"),
Sepal.Length = c("Sepal", "Length"),
Sepal.Width = c("", "Width"),
Petal.Length = c("Petal", "Length"),
Petal.Width = c("", "Width"),
stringsAsFactors = FALSE
)
ht <- huxtable::as_hux(rbind(column_headers, iris)) %>%
huxtable::merge_cells(1, 2:3) %>%
huxtable::merge_cells(1, 4:5) %>%
huxtable::set_align(1,2, 'center') %>%
huxtable::set_align(1,4, 'center') %>%
huxtable::set_bold(1:2, 1:ncol(iris), TRUE) %>%
huxtable::set_bottom_border(1, 2:4, 1) %>%
huxtable::set_bottom_border(2, 1:ncol(iris), 2)
ht[1:10,]
ht[2, 1] <- "Species of\\line flower"
huxtable::escape_contents(ht) <- FALSE
huxtable::width(ht) <- 1.5
ht[1:10,]
doc <- rtf_doc(ht, header_rows = 2)
write_rtf(doc, file='table4.rtf')
knitr::include_graphics("table4_img1.png") |
library(lamme)
data("schoene")
attach(schoene)
summary(schoene)
c('sd_pre'=sd(pre_HRT),'sd_post'=sd(post_HRT))
abc(post_HRT,group,pre_HRT)
lancova(post_HRT,group,pre_HRT)
boot.es(post_HRT,group,pre_HRT)
par(mfrow=c(2,2))
lanova(post_HRT,group)
pwr.lanova(3,40,.01,.05)
pwr.lancova(3,40,.01,.5,.05)
ss.lanova(3,.01,.8,.05)
ss.lancova(3,.01,.5,.8,.05) |
AdditionFI_W3 <- function() {
self <- environment()
class(self) <- append('AdditionFI_W3', class(self))
addNumeric <- function(x_n, y_n) x_n + y_n
addDouble <- function(x_d, y_d) x_d + y_d
addInteger <- function(x_i, y_i) x_i + y_i
divideByZero <- function(x_n) x_n / 0
generateWarning <- function(x_) 1:3 + 1:7
generateError <- function() stop('generated error')
function_return_types <- data.table(
function_name = c('addNumeric', 'addDouble', 'addInteger', 'generateWarning', 'generateError'),
return_value = c('x_n', 'x_d', 'x_i', 'x_i', 'x')
)
label <- 'erroneous function return type definition - unknown return type x'
self
} |
"theta" <-
c(0, 0)
"X" <-
c(0, 0, 0) |
setClassUnion("data.frameOrNULL",members=c("data.frame", "NULL"))
setClassUnion("numericOrNULL",members=c("numeric", "NULL"))
pednames <- c("famid", "id", "father", "mother", "sex", "pheno")
snpnames <- c("chr", "id", "dist", "pos", "A1", "A2")
snpstatnames0 <- c("N0", "N1", "N2", "NAs", "callrate", "maf", "hz", "N0.f", "N1.f", "N2.f", "NAs.f" )
snpstatnames <- c(snpstatnames0, "hwe")
pedstatnames <- c("N0", "N1", "N2", "NAs", "N0.x", "N1.x", "N2.x", "NAs.x",
"N0.y", "N1.y", "N2.y", "NAs.y", "N0.mt", "N1.mt", "N2.mt", "NAs.mt",
"callrate", "hz", "callrate.x", "hz.x", "callrate.y", "hz.y", "callrate.mt", "hz.mt")
is.null.df <- function(x) is.data.frame(x) & nrow(x) == 0 & ncol(x) == 0
setClass("bed.matrix", representation(
ped = 'data.frame',
snps = 'data.frame',
bed = 'externalptr',
p = 'numericOrNULL',
mu = 'numericOrNULL',
sigma = 'numericOrNULL',
standardize_p = 'logical',
standardize_mu_sigma = 'logical' ))
setValidity('bed.matrix',
function(object) {
errors <- character()
if ( object@standardize_p & object@standardize_mu_sigma )
errors <- c(errors, "Only one center scale parameter can be TRUE.")
if ( object@standardize_p & is.null(object@p) )
errors <- c(errors, "If 'standardize_p' is TRUE, 'p' must be defined.")
if ( object@standardize_mu_sigma & ( is.null(object@mu) | is.null(object@sigma) ) )
errors <- c(errors, "If 'standardize_mu_sigma' is TRUE, 'mu' and 'sigma' must be defined.")
if ( !is.null(object@p) & length(object@p) != ncol(object) )
errors <- c(errors, "The length of 'p' must be equal to the number of markers.")
if ( !is.null(object@mu) & length(object@mu) != ncol(object) )
errors <- c(errors, "The length of 'mu' must be equal to the number of markers.")
if ( !is.null(object@sigma) & length(object@sigma) != ncol(object) )
errors <- c(errors, "The length of 'sigma' must be equal to the number of markers.")
if(.Call("isnullptr", PACKAGE = 'gaston', object@bed))
errors <- c(errors, 'The externalptr is broken')
if ( length(errors)==0 ) return(TRUE) else return(errors)
} );
setAs("bed.matrix", "matrix",
function(from) {
validObject(from)
to <- if(from@standardize_p)
.Call('gg_m4_as_scaled_matrix_p', PACKAGE = 'gaston', from@bed, from@p)
else if(from@standardize_mu_sigma)
.Call('gg_m4_as_scaled_matrix_mu_sigma', PACKAGE = 'gaston', from@bed, from@mu, from@sigma)
else
.Call('gg_m4_as012', PACKAGE = 'gaston', from@bed)
colnames(to) <- from@snps$id
rownames(to) <- if(any(duplicated(from@ped$id))) paste(from@ped$fam, from@ped$id, sep=":")
else from@ped$id
to
} );
setGeneric('as.matrix')
setMethod("as.matrix", signature="bed.matrix", definition = function(x) as(x,"matrix") )
setAs("matrix", "bed.matrix",
function(from) {
bed <- .Call('gg_as_matrix4', PACKAGE = 'gaston', from)
ped <- if(is.null(rownames(from)))
structure(list(), row.names = c(NA, -nrow(from)), class = "data.frame")
else
data.frame(famid = rownames(from), id = rownames(from), father = 0, mother = 0, sex = 0, pheno = 0, stringsAsFactors = FALSE)
snp <- if(is.null(colnames(from)))
structure(list(), row.names = c(NA, -ncol(from)), class = "data.frame")
else
data.frame(chr = NA, id = colnames(from), dist = NA, pos = NA, A1 = NA, A2 = NA, stringsAsFactors = FALSE)
x <- new("bed.matrix", bed = bed, snps = snp, ped = ped, p = NULL, mu = NULL,
sigma = NULL, standardize_p = FALSE, standardize_mu_sigma = FALSE )
if(getOption("gaston.auto.set.stats", TRUE)) x <- set.stats(x, verbose = FALSE)
x
} );
setGeneric('dim')
setMethod("dim", signature = "bed.matrix",
function(x) c(.Call('gg_ninds', PACKAGE = 'gaston', x@bed), .Call('gg_nsnps', PACKAGE = 'gaston', x@bed)))
setGeneric('head')
setMethod( 'head', signature(x='bed.matrix'), function(x, nrow=10, ncol=10) print( as.matrix( x[1:min( nrow, nrow(x) ),1:min( ncol, ncol(x) )] ) ) )
setMethod(show, signature("bed.matrix"),
function(object) {
if(.Call("isnullptr", PACKAGE = 'gaston', object@bed))
cat("A bed.matrix with a broken externalptr!\nHint: don't save/load bed.matrices with other functions than write.bed.matrix and read.bed.matrix\n")
else {
cat('A bed.matrix with ', nrow(object), ' individuals and ', ncol(object), ' markers.\n', sep='')
if(anyDuplicated(object@snps$id)) cat("There are some duplicated SNP id's\n")
if(all(snpstatnames0 %in% names(object@snps))) {
cat("snps stats are set\n");
a <- sum(object@snps$NAs == nrow(object))
if(a > 1) cat(" There are ", a, " SNPs with a callrate equal to zero\n");
if(a == 1) cat(" There is one SNP with a callrate equal to zero\n");
a <- sum(object@snps$maf == 0, na.rm = TRUE)
if(a > 1) cat(" There are ", a, " monomorphic SNPs\n");
if(a == 1) cat(" There is one monomorphic SNP\n");
} else
cat("snps stats are not set (or incomplete)\n")
if(anyDuplicated(object@ped[, c("famid", "id")]))
cat("There are some duplicated individual id's\n")
if(all(pedstatnames %in% names(object@ped))) {
cat("ped stats are set\n");
a <- sum(object@ped$NAs == ncol(object))
if(a > 1) cat(" There are ", a, " individuals with a callrate equal to zero\n");
if(a == 1) cat(" There is one individual with a callrate equal to zero\n");
} else cat("ped stats are not set (or incomplete)\n")
}
}
) |
varlog.lam <-
function(sight1,sight2){
if(sight1$call$form != sight2$call$form){
stop("Need same sightability model to calculate the covariance")
}
if(is.null(sight1$sight$note)){
varbeta <- vcov(sight1$sight)
beta <- coef(sight1$sight)
}else{
varbeta <- sight1$sight$varbet
beta <- sight1$sight$bet
}
y1 <- sight1$odat$total
y2 <- sight2$odat$total
n1 <- nrow(sight1$odat)
n2 <- nrow(sight2$odat)
inv.srate1 <- 1/sight1$odat$samp.rates
inv.srate2 <- 1/sight2$odat$samp.rates
fo <- sight1$call$form
class(fo) <- "formula"
tempnm1 <- terms(fo, data = sight1$odat)
tempnm2 <- attr(tempnm1, "term.labels")
covars1 <- sight1$odat[, tempnm2]
covars2 <- sight2$odat[, tempnm2]
xdat1 <- as.matrix(cbind(rep(1, n1), covars1))
xdat2 <- as.matrix(cbind(rep(1, n2), covars2))
xb1 <- xdat1%*%beta
xb2 <- xdat2%*%beta
xbb <- kronecker(xb1, t(xb2), FUN = "+")
xvarbeta <- xdat1%*%varbeta%*%t(xdat2)
smat <- matrix(0, n1, n2)
for(i in 1:n1){
for(j in 1:n2){
xtemp1 <- as.vector(xdat1[i, ], mode = "numeric")
xtemp2 <- as.vector(xdat2[j, ], mode = "numeric")
xtot <- t(xtemp1+xtemp2)
smat[i,j] <- (xtot%*%varbeta%*%t(xtot))/2
}
}
smat.cov <- exp(-xbb-smat)*(exp(xvarbeta)-1)
y.p1 <- as.matrix(y1*inv.srate1, n1, 1)
y.p2 <- as.matrix(y2*inv.srate2, n2, 1)
cov.total <- t(y.p1)%*%smat.cov%*%y.p2
var.tau1.tau2 <- matrix(c(sight2$est[2], cov.total, cov.total, sight1$est[2]), ncol = 2, byrow = TRUE)
dfs <- matrix(c(1/sight2$est[1], -1/sight1$est[1]), 1, 2)
varloglam <- list(loglamda = log(sight2$est[1]/sight1$est[1]), varloglamda = dfs%*%var.tau1.tau2%*%t(dfs))
return(varloglam = varloglam)
} |
NNS.FSD.uni <- function(x, y, type = "discrete"){
if(any(class(x)==c("tbl", "data.table"))) x <- as.vector(unlist(x))
if(any(class(y)==c("tbl", "data.table"))) y <- as.vector(unlist(y))
if(sum(is.na(cbind(x,y))) > 0) stop("You have some missing values, please address.")
type <- tolower(type)
if(!any(type%in%c("discrete", "continuous"))){
warning("type needs to be either discrete or continuous")
}
if(min(y) > min(x)){
return(0)
} else {
Combined_sort <- sort(c(x, y), decreasing = FALSE)
if(type == "discrete"){
degree <- 0
} else {
degree <- 1
}
L.x <- LPM(degree, Combined_sort, x)
LPM_x_sort <- L.x / (UPM(degree, Combined_sort, x) + L.x)
L.y <- LPM(degree, Combined_sort, y)
LPM_y_sort <- L.y / (UPM(degree, Combined_sort, y) + L.y)
x.fsd.y <- any(LPM_x_sort > LPM_y_sort)
ifelse(!x.fsd.y && min(x) >= min(y) && !identical(LPM_x_sort, LPM_y_sort),
return(1),
return(0))
}
}
NNS.SSD.uni <- function(x, y){
if(any(class(x)==c("tbl", "data.table"))) x <- as.vector(unlist(x))
if(any(class(y)==c("tbl", "data.table"))) y <- as.vector(unlist(y))
if(sum(is.na(cbind(x,y))) > 0) stop("You have some missing values, please address.")
if(min(y) > min(x) | mean(y) > mean(x)) {
return(0)
} else {
Combined_sort <- sort(c(x, y), decreasing = FALSE)
LPM_x_sort <- LPM(1, Combined_sort, x)
LPM_y_sort <- LPM(1, Combined_sort, y)
x.ssd.y <- any(LPM_x_sort > LPM_y_sort)
ifelse(!x.ssd.y && min(x) >= min(y) && !identical(LPM_x_sort, LPM_y_sort),
return(1),
return(0))
}
}
NNS.TSD.uni <- function(x, y){
if(any(class(x)==c("tbl", "data.table"))) x <- as.vector(unlist(x))
if(any(class(y)==c("tbl", "data.table"))) y <- as.vector(unlist(y))
if(sum(is.na(cbind(x,y))) > 0) stop("You have some missing values, please address.")
if(min(y) > min(x) | mean(y) > mean(x)) {
return(0)
} else {
Combined_sort <- sort(c(x, y), decreasing = FALSE)
LPM_x_sort <- LPM(2, Combined_sort, x)
LPM_y_sort <- LPM(2, Combined_sort, y)
x.tsd.y <- any(LPM_x_sort > LPM_y_sort)
ifelse(!x.tsd.y && min(x) >= min(y) && !identical(LPM_x_sort, LPM_y_sort),
return(1),
return(0))
}
} |
test_that("invalid seeds are forbidden", {
expect_error(flametree_grow(seed = NULL), "must not be null")
expect_error(flametree_grow(seed = NA), "must not contain missing values")
expect_error(flametree_grow(seed = .1234), "must be integer valued")
expect_error(flametree_grow(seed = 1:3), "must have length")
expect_error(flametree_grow(seed = numeric(0L)), "must have length")
expect_error(flametree_grow(seed = "abc"), "must be numeric")
expect_error(flametree_grow(seed = TRUE), "must be numeric")
expect_error(flametree_grow(seed = list(1)), "must be numeric")
expect_error(flametree_grow(seed = c(1, NA)))
expect_error(flametree_grow(seed = list(1, NA)))
expect_silent(flametree_grow(seed = 0))
expect_silent(flametree_grow(seed = -1))
})
test_that("invalid times are forbidden", {
expect_error(flametree_grow(time = 0))
expect_error(flametree_grow(time = -1))
expect_error(flametree_grow(time = .3))
expect_error(flametree_grow(time = 1:4))
expect_error(flametree_grow(time = NA_integer_))
expect_error(flametree_grow(time = NaN))
expect_error(flametree_grow(time = Inf))
expect_error(flametree_grow(time = "abc"))
expect_error(flametree_grow(time = NULL))
expect_error(flametree_grow(time = TRUE))
expect_error(flametree_grow(time = list(2)))
expect_silent(flametree_grow(time = 3))
})
test_that("invalid scales are forbidden",{
expect_error(flametree_grow(scale = "abc"))
expect_error(flametree_grow(scale = TRUE))
expect_error(flametree_grow(scale = list()))
expect_error(flametree_grow(scale = NULL))
expect_error(flametree_grow(scale = NA))
expect_error(flametree_grow(scale = -.123))
expect_error(flametree_grow(scale = numeric(0)))
expect_error(flametree_grow(scale = character(0)))
expect_error(flametree_grow(scale = c(.8, -.123, .1)))
expect_error(flametree_grow(scale = c(.8, NA, .1)))
expect_error(flametree_grow(scale = 0))
expect_silent(flametree_grow(scale = c(.8, .9)))
expect_silent(flametree_grow(scale = c(.8, .9, 1.1)))
})
test_that("invalid angles are forbidden",{
expect_error(flametree_grow(angle = "abc"))
expect_error(flametree_grow(angle = TRUE))
expect_error(flametree_grow(angle = list()))
expect_error(flametree_grow(angle = NULL))
expect_error(flametree_grow(angle = NA))
expect_error(flametree_grow(angle = numeric(0)))
expect_error(flametree_grow(angle = character(0)))
expect_error(flametree_grow(angle = c(.8, NA, 10)))
expect_error(flametree_grow(angle = -12.3))
expect_silent(flametree_grow(angle = c(0, -12.3)))
expect_silent(flametree_grow(angle = c(-700, 1000)))
})
test_that("invalid splits are forbidden", {
expect_error(flametree_grow(split = 0))
expect_error(flametree_grow(split = -1))
expect_error(flametree_grow(split = .3))
expect_error(flametree_grow(split = 1:4))
expect_error(flametree_grow(split = NA_integer_))
expect_error(flametree_grow(split = NaN))
expect_error(flametree_grow(split = Inf))
expect_error(flametree_grow(split = "abc"))
expect_error(flametree_grow(split = NULL))
expect_error(flametree_grow(split = TRUE))
expect_error(flametree_grow(split = list(2)))
expect_silent(flametree_grow(split = 3))
})
test_that("flametree data has correct columns", {
dat <- flametree_grow()
expect_s3_class(dat, "tbl")
expect_named(
object = dat,
expected = c(
"coord_x", "coord_y", "id_tree", "id_time", "id_path", "id_leaf",
"id_pathtree", "id_step", "seg_deg", "seg_len", "seg_col", "seg_wid"
)
)
expect_type(dat$coord_x, "double")
expect_type(dat$coord_y, "double")
expect_type(dat$seg_deg, "double")
expect_type(dat$seg_len, "double")
expect_type(dat$seg_col, "double")
expect_type(dat$seg_wid, "double")
expect_type(dat$id_time, "integer")
expect_type(dat$id_path, "integer")
expect_type(dat$id_step, "integer")
expect_type(dat$id_leaf, "logical")
expect_type(dat$id_tree, "integer")
expect_type(dat$id_pathtree, "character")
})
test_that("flametree edges are well defined", {
dat <- flametree_grow()
expect_equal(nrow(dat) %% 3, 0)
expect_equal(nrow(dat), length(unique(dat$id_path)) * 3)
expect_equal(sum(dat$id_step == 0), sum(dat$id_step == 1))
expect_equal(sum(dat$id_step == 0), sum(dat$id_step == 2))
expect_true(all(table(paste(dat$id_path, dat$id_step)) == 1))
}) |
get_genes_in_region <- function(chr=chr, xmin=xmin,xmax=xmax,protein_coding_only=F, show_exons=F,show_genes=T){
if(show_genes){
genes <- get_genes(chr,xmin,xmax,protein_coding_only = protein_coding_only )
}else if(show_exons){
genes <- get_exons(chr,xmin,xmax,protein_coding_only = protein_coding_only)
}else{
if(xmax-xmin < 1000001){
genes <- get_exons(chr,xmin,xmax,protein_coding_only = protein_coding_only )
}
else{
genes <- get_genes(chr,xmin,xmax,protein_coding_only = protein_coding_only )
}
}
return(genes)
}
get_main_LD_snp <- function(dat){
label_cols <- c("CHROM","POS","P","ID","log10p")
top_snps <- data.frame(matrix(nrow = 0, ncol = length(label_cols)))
colnames(top_snps) <- label_cols
for(i in seq_along(dat)){
top_snps <- rbind(top_snps, dat[[i]] %>% dplyr::filter("R2" >= 1) %>% dplyr::distinct(ID, .keep_all=T) %>% dplyr::arrange(-R2) %>% utils::head(n=1) %>% dplyr::select("CHROM","POS","P","ID","log10p") )
}
return(top_snps)
}
get_annotation <- function(dat, annotate=1e-09, region_size=1000000,distinct_gene_labels=FALSE,protein_coding_only=FALSE, verbose=FALSE){
if(is.data.frame(dat)){dat <- list(dat)}
if("log10p" %in% colnames(dat[[1]])){
label_cols <- c("CHROM","POS","P","ID","Gene_Symbol","biotype", "log10p", "color","alpha","size","shape")
}
else{
label_cols <- c("CHROM","POS","P","ID","Gene_Symbol","biotype")
}
plot_labels <- data.frame(matrix(nrow = 0, ncol = length(label_cols)))
colnames(plot_labels) <- label_cols
for(i in seq_along(dat)){
df <- as.data.frame(dat[[i]])
if(is.vector(annotate)){
annot_thresh <- ifelse(i <= length(annotate), annotate[i], annotate[length(annotate)])
}
else{ annot_thresh <- annotate}
tmp_labels <- get_best_snp_per_MB(df, thresh = annot_thresh, region_size=region_size, .checked=TRUE, protein_coding_only = protein_coding_only, verbose=FALSE)
if(nrow(tmp_labels) > 0){
if(!"biotype" %in% tmp_labels){tmp_labels$biotype <- "unknown"}
if(! "Gene_Symbol" %in% colnames(tmp_labels)){
tmp_labels <- annotate_with_nearest_gene(tmp_labels, protein_coding_only=protein_coding_only)
}
if("log10p" %in% colnames(dat[[1]])){
tmp_labels <- tmp_labels %>% dplyr::select("CHROM","POS","P","ID","Gene_Symbol","biotype", "log10p", "color","alpha","size","shape")
}
else{
tmp_labels <- tmp_labels %>% dplyr::select("CHROM","POS","P","ID","Gene_Symbol","biotype")
}
}
plot_labels <- rbind(plot_labels,tmp_labels)
}
if(distinct_gene_labels){
plot_labels <- plot_labels %>% dplyr::arrange(CHROM,POS,P) %>% dplyr::distinct(CHROM,POS,Gene_Symbol, .keep_all = TRUE)
}
return(plot_labels)
}
annotate_with_nearest_gene <- function(variants, protein_coding_only=FALSE){
if("POS" %in% colnames(variants) & "CHROM" %in% colnames(variants)){
if(length(variants$POS) > 1000){
warning(paste("The dataset includes [",length(variants$POS),"] variants. This may take a while...", sep=""))
}
for(i in seq_along(variants$POS)){
if(length(variants$POS) > 1000){
if(i %% 1000==0) {
print(paste(i," variants annotated", sep=""))
}
}
nearest_gene <- NULL
variant <- variants[i,]
chr <- gsub("chr", "", variant$CHROM)
chr <- paste("chr",chr,sep="")
genes_on_chr <- toprdata::ENSGENES %>% dplyr::filter(chrom == chr) %>% dplyr::arrange(gene_start)
if(protein_coding_only){
genes_on_chr <- genes_on_chr %>% dplyr::filter(biotype == "protein_coding")
}
within_gene <- genes_on_chr %>% dplyr::filter(gene_end >= variant$POS & gene_start <= variant$POS)
if(length(within_gene$gene_symbol) > 0 ){
if(length(within_gene) == 1){ nearest_gene <- within_gene$gene_symbol }
else{
prot_coding <- within_gene %>% dplyr::filter(biotype=="protein_coding")
if(length(prot_coding$gene_symbol) > 0){ nearest_gene <- prot_coding %>% utils::head(n=1)}
else{ nearest_gene <- within_gene %>% utils::head(n=1) }
}
}else{
genes_left <- genes_on_chr %>% dplyr::filter(gene_end <= variant$POS) %>% dplyr::arrange(gene_end)
genes_right <- genes_on_chr %>% dplyr::filter(gene_start >= variant$POS) %>% dplyr::arrange(gene_start)
if(length(genes_left$gene_symbol)>0 & length(genes_right$gene_symbol)> 0){
gene_left <- genes_left[as.numeric(length(genes_left$gene_symbol)),]
gene_right <- genes_right[1,]
dist_left <- variant$POS-gene_left$gene_end
dist_right <- gene_right$gene_start-variant$POS
if(abs(dist_left) < abs(dist_right)){ nearest_gene <- gene_left }
else{ nearest_gene <- gene_right }
}
else if(length(genes_left$gene_symbol)== 0 & length(genes_right$gene_symbol)> 0){
nearest_gene <- genes_right[1,]
}
else if(length(genes_left$gene_symbol)> 0 & length(genes_right$gene_symbol)==0){
nearest_gene <- genes_left[as.numeric(length(genes_left$gene_symbol)),]
}
}
if(! is.null(nearest_gene)){
variants[i,"Gene_Symbol"] <- nearest_gene$gene_symbol
variants[i, "biotype"] <- nearest_gene$biotype
}else{
variants[i,"Gene_Symbol"] <- "not_found"
variants[i, "biotype"] <- "."
}
}
}
else{
stop("Cannot find the columns CHROM and POS in the input data. Add the required columns and try again, or rename existing columns, e.g. df=df %>% dplyr::rename(CHROM=yourColname)")
}
return(variants)
} |
.qbbinom <- function(p, N, shape1, shape2, lower.tail = TRUE, log.p = FALSE) {
if (shape1 == 0 || shape2 == 0) {
return(Inf)
}
if (log.p) p <- exp(p)
if (!lower.tail) p <- 1 - p
x <- extraDistr::dbbinom(x = 0:N, size = N, alpha = shape1, beta = shape2)
q <- which(cumsum(x) > p)[1] - 1
return(q)
}
.modebbinom <- function(N, shape1, shape2) {
if ((shape1 == 1 && shape2 == 1) || shape1 == 0 || shape2 == 0) {
return(NA)
}
if (shape1 == 1 && shape2 > 1) {
return(0)
}
index <- which.max(extraDistr::dbbinom(x = 0:N, size = N, alpha = shape1, beta = shape2)) - 1
return(index)
}
.qhyper <- function(p, N, n, k) {
K <- k:N
cdf <- stats::phyper(q = k, m = K, n = N - K, k = n)
return(max(K[cdf > (1 - p)]))
}
.getfun <- function(x) {
if (length(grep("::", x)) > 0) {
parts <- strsplit(x, "::")[[1]]
return(getExportedValue(parts[1], parts[2]))
} else {
return(x)
}
} |
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
)
xropi = c(11:9,10:8,9,10,9,10:7,8:11,10:12,11:7,8,7:10,9,8,9,8:10,9,10,9,10)
xlevo = c(11,10,11,10,11:9,10:7,8,7,8:5,6:8,7,8:6,7,6,7,6,7:5,6,7,6:12)
library(cir)
bhamou03ropi = DRtrace(x=xropi[-40]/100, y=(1-diff(xropi))/2)
bhamou03levo = DRtrace(x=xlevo[-40]/100, y=(1-diff(xlevo))/2)
par(mfrow=c(1,2), las=1, mar=c(4,4,4,1))
doserange = c(5,12)/100
plot(bhamou03ropi, ylim=doserange, ylab="Concentration (%)", main='Ropivacaine Arm')
legend('bottomright',legend=c('Effective','Ineffective'),pch=c(19,1),bty='n')
plot(bhamou03levo, ylim=doserange, ylab="Concentration (%)", main='Levobupivacaine Arm')
bhamou03ropiRates = doseResponse(bhamou03ropi)
bhamou03levoRates = doseResponse(bhamou03levo)
knitr::kable(bhamou03ropiRates, row.names=FALSE,align='ccr',digits=c(2,4,0))
knitr::kable(bhamou03levoRates, row.names=FALSE,align='ccr',digits=c(2,4,0))
ropiTargetCIR=quickInverse(bhamou03ropiRates, target=0.5, adaptiveShrink=TRUE)
ropiTargetCIR
levoTargetCIR=quickInverse(bhamou03levoRates, target=0.5, adaptiveShrink=TRUE)
levoTargetCIR
quickInverse(bhamou03ropiRates, target=0.5, adaptiveShrink=TRUE, conf=0.83)
quickInverse(bhamou03levoRates, target=0.5, adaptiveShrink=TRUE, conf=0.83)
quickInverse(bhamou03ropiRates, target=0.5, estfun=oldPAVA, conf=0.83)
quickInverse(bhamou03levoRates, target=0.5, estfun=oldPAVA, conf=0.83)
ropiCurveIR = oldPAVA(bhamou03ropiRates, full=TRUE)
ropiCurveCIR = cirPAVA(bhamou03ropiRates, target=0.5, adaptiveShrink=TRUE, full=TRUE)
levoCurveIR = oldPAVA(bhamou03levoRates, full=TRUE)
levoCurveCIR = cirPAVA(bhamou03levoRates, target=0.5, adaptiveShrink=TRUE, full=TRUE)
ropiCurveCIR
par(mfrow=c(1,2), las=1, mar=c(4,4,4,1))
plot(bhamou03ropiRates, xlab="Concentration (%)",
ylab="Proportion Effective", main='Ropivacaine Arm')
lines(y~x, data=ropiCurveIR$output, lty=2)
lines(y~x, data=ropiCurveCIR$shrinkage, col='blue',lwd=2)
points(target~point, data=ropiTargetCIR, col='purple', pch=19, cex=2)
lines(c(ropiTargetCIR$lower90conf,ropiTargetCIR$upper90conf), rep(0.5,2), col='purple', lwd=2)
legend('bottomright', legend=c("Observed Proportions",'Isotonic Regression',
'Centered Isotonic Regression','Estimate +/- 90% CI'),
bty='n',pch=c(4,rep(NA,2),16),col=c('black','black','blue','purple'),lty=c(0,2,1,1))
plot(bhamou03levoRates, xlab="Concentration (%)",
ylab="Proportion Effective", main='Levobupivacaine Arm', ylim=0:1)
lines(y~x, data=levoCurveIR$output, lty=2)
lines(y~x, data=levoCurveCIR$shrinkage, col='blue',lwd=2)
points(target~point, data=levoTargetCIR, col='purple', pch=19, cex=2)
lines(c(levoTargetCIR$lower90conf,levoTargetCIR$upper90conf), rep(0.5,2), col='purple', lwd=2) |
context("anova_apa")
library(dplyr, warn.conflicts = FALSE)
library(magrittr, warn.conflicts = FALSE)
test_that("Formal structure for anova_apa output", {
library(ez)
data(ANT)
data <-
ANT %>%
filter(error == 0) %>%
group_by(subnum, group, cue, flank) %>%
summarise(rt = mean(rt)) %>%
filter(!is.nan(rt)) %>%
as.data.frame
anova <- anova_apa(
ezANOVA(data, dv = rt, wid = subnum, within = c(cue, flank),
between = group, detailed = TRUE),
print = FALSE
)
expect_equal(nrow(anova), 1 + 3 + 3 + 1)
expect_match(`[.data.frame`(anova, anova$effect == "group", "text"),
paste0("F\\([[:digit:]]+, [[:digit:]]+\\) = [[:digit:]]+\\.",
"[[:digit:]]{2}, p [=<] \\.[[:digit:]]{3}, petasq ",
"[=<] \\.[[:digit:]]{2}"))
})
test_that("Output for anova_apa: oneway between ANOVA", {
data <- data.frame(id = factor(1:15),
dose = rep(c("placebo", "low dose", "high dose"),
each = 5),
libido = c(3, 2, 1, 1, 4, 5, 2, 4, 2, 3, 7, 4, 5, 3, 6))
anova_afex <- anova_apa(
afex::aov_ez(id = "id", dv = "libido", data = data, between = "dose"),
print = FALSE
)
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "dose",
"text"),
"F(2, 12) = 5.12, p = .025, petasq = .46")
anova_ez <- anova_apa(
ez::ezANOVA(data, dv = libido, wid = id, between = dose, detailed = TRUE),
print = FALSE
)
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "dose", "text"),
"F(2, 12) = 5.12, p = .025, petasq = .46")
})
test_that("Output for anova_apa: factorial between ANOVA", {
data <- data.frame(
id = factor(1:48),
gender = rep(c("female", "male"), each = 24),
alcohol = rep(c("none", "2 pints", "4 pints"), each = 8, times = 2),
attractiveness = c(65, 70, 60, 60, 60, 55, 60, 55, 70, 65, 60, 70, 65, 60,
60, 50, 55, 65, 70, 55, 55, 60, 50, 50, 50, 55, 80, 65,
70, 75, 75, 65, 45, 60, 85, 65, 70, 70, 80, 60, 30, 30,
30, 55, 35, 20, 45, 40)
)
anova_afex <- anova_apa(
afex::aov_ez(id = "id", dv = "attractiveness", data = data,
between = c("gender", "alcohol")),
print = FALSE
)
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "gender",
"text"),
"F(1, 42) = 2.03, p = .161, petasq = .05")
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "alcohol",
"text"),
"F(2, 42) = 20.07, p < .001, petasq = .49")
expect_identical(`[.data.frame`(anova_afex,
anova_afex$effect == "gender:alcohol",
"text"),
"F(2, 42) = 11.91, p < .001, petasq = .36")
anova_ez <- anova_apa(
ez::ezANOVA(data, dv = attractiveness, wid = id,
between = c(gender, alcohol), detailed = TRUE, type = 3),
print = FALSE
)
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "gender",
"text"),
"F(1, 42) = 2.03, p = .161, petasq = .05")
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "alcohol",
"text"),
"F(2, 42) = 20.07, p < .001, petasq = .49")
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "gender:alcohol",
"text"),
"F(2, 42) = 11.91, p < .001, petasq = .36")
})
test_that("Output for anova_apa: repeated-measures ANOVA", {
data <- data.frame(
id = factor(rep(1:8, each = 4)),
animal = rep(c("stick insect", "kangaroo testicle", "fish eye",
"witchetty grub"), times = 8),
retch = c(8, 7, 1, 6, 9, 5, 2, 5, 6, 2, 3, 8, 5, 3, 1, 9, 8, 4, 5, 8, 7, 5,
6, 7, 10, 2, 7, 2, 12, 6, 8, 1)
)
anova_afex <- anova_apa(
afex::aov_ez(id = "id", dv = "retch", data = data, within = "animal"),
print = FALSE
)
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "animal",
"text"),
"F(1.60, 11.19) = 3.79, p = .063, petasq = .35")
anova_ez <- anova_apa(
ez::ezANOVA(data, dv = retch, wid = id, within = animal, detailed = TRUE),
print = FALSE
)
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "animal",
"text"),
"F(1.60, 11.19) = 3.79, p = .063, petasq = .35")
})
test_that("Output for anova_apa: factorial repeated-measures ANOVA", {
data <- data.frame(
id = factor(rep(1:20, each = 9)),
gender = rep(c("male", "female"), each = 10 * 9),
imagery = rep(c("positive", "negative", "neutral"), times = 60),
drink = rep(c("beer", "wine", "water"), each = 3, times = 20),
attitude = c(1, 6, 5, 38, -5, 4, 10, -14, -2, 43, 30, 8, 20, -12, 4, 9, -10,
-13, 15, 15, 12, 20, -15, 6, 6, -16, 1, 40, 30, 19, 28, -4, 0,
20, -10, 2, 8, 12, 8, 11, -2, 6, 27, 5, -5, 17, 17, 15, 17, -6,
6, 9, -6, -13, 30, 21, 21, 15, -2, 16, 19, -20, 3, 34, 23, 28,
27, -7, 7, 12, -12, 2, 34, 20, 26, 24, -10, 12, 12, -9, 4, 26,
27, 27, 23, -15, 14, 21, -6, 0, 1, -19, -10, 28, -13, 13, 33,
-2, 9, 7, -18, 6, 26, -16, 19, 23, -17, 5, 22, -8, 4, 34, -23,
14, 21, -19, 0, 30, -6, 3, 32, -22, 21, 17, -11, 4, 40, -6, 0,
24, -9, 19, 15, -10, 2, 15, -9, 4, 29, -18, 7, 13, -17, 8, 20,
-17, 9, 30, -17, 12, 16, -4, 10, 9, -12, -5, 24, -15, 18, 17,
-4, 8, 14, -11, 7, 34, -14, 20, 19, -1, 12, 15, -6, 13, 23,
-15, 15, 29, -1, 10)
)
anova_afex <- anova_apa(
afex::aov_ez(id = "id", dv = "attitude", data = data,
within = c("drink", "imagery")),
print = FALSE
)
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "drink",
"text"),
"F(1.15, 21.93) = 5.11, p = .030, petasq = .21")
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "imagery",
"text"),
"F(1.49, 28.40) = 122.56, p < .001, petasq = .87")
expect_identical(`[.data.frame`(anova_afex,
anova_afex$effect == "drink:imagery",
"text"),
"F(4, 76) = 17.15, p < .001, petasq = .47")
anova_ez <- anova_apa(
ez::ezANOVA(data, dv = attitude, wid = id, within = c(drink, imagery),
type = 3, detailed = TRUE),
print = FALSE
)
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "drink", "text"),
"F(1.15, 21.93) = 5.11, p = .030, petasq = .21")
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "imagery",
"text"),
"F(1.49, 28.40) = 122.56, p < .001, petasq = .87")
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "drink:imagery",
"text"),
"F(4, 76) = 17.15, p < .001, petasq = .47")
})
test_that("Output for anova_apa: mixed ANOVA", {
data <- data.frame(
id = factor(rep(1:20, each = 9)),
gender = rep(c("male", "female"), each = 10 * 9),
looks = rep(c("attractive", "average", "ugly"), times = 60),
personality = rep(c("high carisma", "some charisma", "dullard"), each = 3,
times = 20),
rating = c(86, 84, 67, 88, 69, 50, 97, 48, 47, 91, 83, 53, 83, 74, 48, 86,
50, 46, 89, 88, 48, 99, 70, 48, 90, 45, 48, 89, 69, 58, 86, 77,
40, 87, 47, 53, 80, 81, 57, 88, 71, 50, 82, 50, 45, 80, 84, 51,
96, 63, 42, 92, 48, 43, 89, 85, 61, 87, 79, 44, 86, 50, 45, 100,
94, 56, 86, 71, 54, 84, 54, 47, 90, 74, 54, 92, 71, 58, 78, 38,
45, 89, 86, 63, 80, 73, 49, 91, 48, 39, 89, 91, 93, 88, 65, 54,
55, 48, 52, 84, 90, 85, 95, 70, 60, 50, 44, 45, 99, 100, 89, 80,
79, 53, 51, 48, 44, 86, 89, 83, 86, 74, 58, 52, 48, 47, 89, 87,
80, 83, 74, 43, 58, 50, 48, 80, 81, 79, 86, 59, 47, 51, 47, 40,
82, 92, 85, 81, 66, 47, 50, 45, 47, 97, 69, 87, 95, 72, 51, 45,
48, 46, 95, 92, 90, 98, 64, 53, 54, 53, 45, 95, 93, 96, 79, 66,
46, 52, 39, 47)
)
anova_afex <- anova_apa(
afex::aov_ez(id = "id", dv = "rating", data = data,
between = "gender",
within = c("looks", "personality")),
print = FALSE
)
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "gender",
"text"),
"F(1, 18) = 0.00, p = .946, petasq < .01")
expect_identical(`[.data.frame`(anova_afex, anova_afex$effect == "looks",
"text"),
"F(2, 36) = 423.73, p < .001, petasq = .96")
expect_identical(`[.data.frame`(anova_afex,
anova_afex$effect == "personality", "text"),
"F(2, 36) = 328.25, p < .001, petasq = .95")
expect_identical(`[.data.frame`(anova_afex,
anova_afex$effect == "gender:looks", "text"),
"F(2, 36) = 80.43, p < .001, petasq = .82")
expect_identical(`[.data.frame`(anova_afex,
anova_afex$effect == "gender:personality",
"text"),
"F(2, 36) = 62.45, p < .001, petasq = .78")
expect_identical(`[.data.frame`(anova_afex,
anova_afex$effect == "looks:personality",
"text"),
"F(4, 72) = 36.63, p < .001, petasq = .67")
anova_ez <- anova_apa(
ez::ezANOVA(data, dv = rating, wid = id, between = gender,
within = c(looks, personality), type = 3, detailed = TRUE),
print = FALSE
)
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "gender",
"text"),
"F(1, 18) = 0.00, p = .946, petasq < .01")
expect_identical(`[.data.frame`(anova_ez, anova_ez$effect == "looks", "text"),
"F(2, 36) = 423.73, p < .001, petasq = .96")
expect_identical(`[.data.frame`(anova_ez,
anova_ez$effect == "personality", "text"),
"F(2, 36) = 328.25, p < .001, petasq = .95")
expect_identical(`[.data.frame`(anova_ez,
anova_ez$effect == "gender:looks", "text"),
"F(2, 36) = 80.43, p < .001, petasq = .82")
expect_identical(`[.data.frame`(anova_ez,
anova_ez$effect == "gender:personality",
"text"),
"F(2, 36) = 62.45, p < .001, petasq = .78")
expect_identical(`[.data.frame`(anova_ez,
anova_ez$effect == "looks:personality",
"text"),
"F(4, 72) = 36.63, p < .001, petasq = .67")
})
data <- data.frame(id = factor(1:15),
dose = rep(c("placebo", "low dose", "high dose"),
each = 5),
libido = c(3, 2, 1, 1, 4, 5, 2, 4, 2, 3, 7, 4, 5, 3, 6))
anova_afex <- suppressMessages(
afex::aov_ez(id = "id", dv = "libido", data = data, between = "dose")
)
anova_ez <- ez::ezANOVA(data, dv = libido, wid = id, between = dose,
detailed = TRUE)
test_that("anova_apa: markdown", {
expect_identical(anova_apa(anova_afex, effect = "dose", print = FALSE,
format = "markdown"),
"*F*(2, 12) = 5.12, *p* = .025, *petasq* = .46")
expect_identical(anova_apa(anova_afex, effect = "dose", print = FALSE,
format = "markdown"),
"*F*(2, 12) = 5.12, *p* = .025, *petasq* = .46")
})
test_that("anova_apa: rmarkdown", {
expect_identical(anova_apa(anova_afex, effect = "dose", print = FALSE,
format = "rmarkdown"),
"*F*(2, 12) = 5.12, *p* = .025, $\\eta^2_p$ = .46")
expect_identical(anova_apa(anova_ez, effect = "dose", print = FALSE,
format = "rmarkdown"),
"*F*(2, 12) = 5.12, *p* = .025, $\\eta^2_p$ = .46")
})
test_that("anova_apa: html", {
expect_identical(anova_apa(anova_afex, effect = "dose", print = FALSE,
format = "html"),
paste0("<i>F</i>(2, 12) = 5.12, <i>p</i> = .025, ",
"<i>η<sup>2</sup><sub>p</sub></i> = .46"))
expect_identical(anova_apa(anova_afex, effect = "dose", print = FALSE,
format = "html"),
paste0("<i>F</i>(2, 12) = 5.12, <i>p</i> = .025, ",
"<i>η<sup>2</sup><sub>p</sub></i> = .46"))
})
test_that("anova_apa: latex", {
expect_identical(anova_apa(anova_afex, effect = "dose", print = FALSE,
format = "latex"),
paste0("\\textit{F}(2,~12)~=~5.12, \\textit{p}~=~.025, ",
"$\\eta^2_p$~=~.46"))
expect_identical(anova_apa(anova_ez, effect = "dose", print = FALSE,
format = "latex"),
paste0("\\textit{F}(2,~12)~=~5.12, \\textit{p}~=~.025, ",
"$\\eta^2_p$~=~.46"))
})
test_that("anova_apa: plotmath", {
expect_identical(as.character(anova_apa(anova_afex, effect = "dose",
print = FALSE, format = "plotmath")),
paste0("paste(italic(\"F\"), \"(2, 12) = 5.12, \", ",
"italic(\"p\"), \" = .025, \", ",
"eta[p]^2, \" = .46\")"))
expect_identical(as.character(anova_apa(anova_ez, effect = "dose",
print = FALSE, format = "plotmath")),
paste0("paste(italic(\"F\"), \"(2, 12) = 5.12, \", ",
"italic(\"p\"), \" = .025, \", ",
"eta[p]^2, \" = .46\")"))
}) |
shrinkTVP <- function(formula,
data,
mod_type = "double",
niter = 10000,
nburn = round(niter / 2),
nthin = 1,
learn_a_xi = TRUE,
learn_a_tau = TRUE,
a_xi = 0.1,
a_tau = 0.1,
learn_c_xi = TRUE,
learn_c_tau = TRUE,
c_xi = 0.1,
c_tau = 0.1,
a_eq_c_xi = FALSE,
a_eq_c_tau = FALSE,
learn_kappa2_B = TRUE,
learn_lambda2_B = TRUE,
kappa2_B = 20,
lambda2_B = 20,
hyperprior_param,
display_progress = TRUE,
sv = FALSE,
sv_param,
MH_tuning,
starting_vals){
if (!mod_type %in% c("triple", "double", "ridge")) {
stop("mod_type has to be a string equal to 'triple', 'double' or 'ridge'")
}
default_hyper <- list(c0 = 2.5,
g0 = 5,
G0 = 5 / (2.5 - 1),
e1 = 0.001,
e2 = 0.001,
d1 = 0.001,
d2 = 0.001,
beta_a_xi = 10,
beta_a_tau = 10,
alpha_a_xi = 5,
alpha_a_tau = 5,
beta_c_xi = 2,
beta_c_tau = 2,
alpha_c_xi = 5,
alpha_c_tau = 5)
default_hyper_sv <- list(Bsigma_sv = 1,
a0_sv = 5,
b0_sv = 1.5,
bmu = 0,
Bmu = 1)
default_tuning_par <- list(a_xi_adaptive = TRUE,
a_xi_tuning_par = 1,
a_xi_target_rate = 0.44,
a_xi_max_adapt = 0.01,
a_xi_batch_size = 50,
a_tau_adaptive = TRUE,
a_tau_tuning_par = 1,
a_tau_target_rate = 0.44,
a_tau_max_adapt = 0.01,
a_tau_batch_size = 50,
c_xi_adaptive = TRUE,
c_xi_tuning_par = 1,
c_xi_target_rate = 0.44,
c_xi_max_adapt = 0.01,
c_xi_batch_size = 50,
c_tau_adaptive = TRUE,
c_tau_tuning_par = 1,
c_tau_target_rate = 0.44,
c_tau_max_adapt = 0.01,
c_tau_batch_size = 50)
if (missing(MH_tuning)){
MH_tuning <- default_tuning_par
} else {
MH_tuning <- list_merger(default_tuning_par, MH_tuning)
}
if (missing(hyperprior_param)){
hyperprior_param <- default_hyper
} else {
hyperprior_param <- list_merger(default_hyper, hyperprior_param)
}
if (missing(sv_param) | sv == FALSE){
sv_param <- default_hyper_sv
} else {
sv_param <- list_merger(default_hyper_sv, sv_param)
}
to_test_num <- list(lambda2_B = lambda2_B,
kappa2_B = kappa2_B,
a_xi = a_xi,
a_tau = a_tau,
c_xi = c_xi,
c_tau = c_tau)
if (missing(hyperprior_param) == FALSE){
to_test_num <- c(to_test_num, hyperprior_param)
}
if (missing(sv_param) == FALSE){
to_test_num <- c(to_test_num, sv_param[names(sv_param) != "bmu"])
}
if (missing(MH_tuning) == FALSE){
to_test_num <- c(to_test_num, MH_tuning[!grepl("(batch|adaptive)", names(MH_tuning))])
}
bad_inp <- sapply(to_test_num, numeric_input_bad)
if (any(bad_inp)){
stand_names <- c(names(default_hyper), names(default_hyper_sv), "lambda2_B", "kappa2_B", "a_xi", "a_tau", "c_xi", "c_tau")
bad_inp_names <- names(to_test_num)[bad_inp]
bad_inp_names <- bad_inp_names[bad_inp_names %in% stand_names]
stop(paste0(paste(bad_inp_names, collapse = ", "),
ifelse(length(bad_inp_names) == 1, " has", " have"),
" to be a real, positive number"))
}
if (!is.numeric(sv_param$bmu) | !is.scalar(sv_param$bmu)){
stop("bmu has to be a real number")
}
if (any(0 > MH_tuning[grepl("rate", names(MH_tuning))] | MH_tuning[grepl("rate", names(MH_tuning))] > 1)) {
stop("all target_rate parameters in MH_tuning have to be > 0 and < 1")
}
to_test_int <- c(niter = niter,
nburn = nburn,
nthin = nthin,
MH_tuning[grepl("batch", names(MH_tuning))])
bad_int_inp <- sapply(to_test_int, int_input_bad)
if (any(bad_int_inp)){
bad_inp_names <- names(to_test_int)[bad_int_inp]
stop(paste0(paste(bad_inp_names, collapse = ", "),
ifelse(length(bad_inp_names) == 1, " has", " have"),
" to be a single, positive integer"))
}
if ((niter - nburn) < 2){
stop("niter has to be larger than or equal to nburn + 2")
}
if (nthin == 0){
stop("nthin can not be 0")
}
if ((niter - nburn)/2 < nthin){
stop("nthin can not be larger than (niter - nburn)/2")
}
to_test_bool <- c(learn_lambda2_B = learn_lambda2_B,
learn_kappa2_B = learn_kappa2_B,
learn_a_xi = learn_a_xi,
learn_a_tau = learn_a_tau,
display_progress = display_progress,
sv = sv,
MH_tuning[grepl("adaptive", names(MH_tuning))])
bad_bool_inp <- sapply(to_test_bool, bool_input_bad)
if (any(bad_bool_inp)){
bad_inp_names <- names(to_test_bool)[bad_bool_inp]
stop(paste0(paste(bad_inp_names, collapse = ", "),
ifelse(length(bad_inp_names) == 1, " has", " have"),
" to be a single logical value"))
}
if (inherits(formula, "formula") == FALSE){
stop("formula is not of class formula")
}
mf <- match.call(expand.dots = FALSE)
m <- match(x = c("formula", "data"), table = names(mf), nomatch = 0L)
mf <- mf[c(1L, m)]
mf$drop.unused.levels <- TRUE
mf$na.action <- na.pass
mf[[1L]] <- quote(stats::model.frame)
mf <- eval(expr = mf, envir = parent.frame())
y <- model.response(mf, "numeric")
mt <- attr(x = mf, which = "terms")
x <- model.matrix(object = mt, data = mf)
if (any(is.na(y))) {
stop("No NA values are allowed in response variable")
}
if (any(is.na(x))){
stop("No NA values are allowed in covariates")
}
if (missing(data)){
index <- zoo::index(y)
} else {
index <- zoo::index(data)
}
p <- 0
if (p != 0){
x <- cbind(x, mlag(y, p))[(p + 1):nrow(x), ]
colnames(x)[(ncol(x) - p + 1):ncol(x)] <- paste0("ar", 1:p)
y <- y[(p + 1):length(y)]
index <- index[(p + 1):length(index)]
}
colnames(x)[colnames(x) == "(Intercept)"] <- "Intercept"
d <- dim(x)[2]
default_starting_vals <- list(beta_mean_st = rep(0, d),
theta_sr_st = rep(1, d),
tau2_st = rep(1, d),
xi2_st = rep(1, d),
kappa2_st = rep(1, d),
lambda2_st = rep(1, d),
kappa2_B_st = 20,
lambda2_B_st = 20,
a_xi_st = 0.1,
a_tau_st = 0.1,
c_xi_st = 0.1,
c_tau_st = 0.1,
sv_mu_st = -10,
sv_phi_st = 0.5,
sv_sigma2_st = 1,
C0_st = 1,
sigma2_st = 1,
h0_st = 0)
if (sv == TRUE){
default_starting_vals$sigma2_st <- rep(1, length(y))
}
if (missing(starting_vals)){
starting_vals <- default_starting_vals
} else {
starting_vals <- list_merger(default_starting_vals, starting_vals)
}
vec_valued <- c("beta_mean_st",
"theta_sr_st",
"tau2_st",
"xi2_st",
"kappa2_st",
"lambda2_st")
bad_length <- sapply(starting_vals[vec_valued], function(x) length(x) != d)
if (any(bad_length)){
bad_length_names <- vec_valued[bad_length]
stop(paste0(paste(bad_length_names, collapse = ", "),
ifelse(length(bad_length_names) == 1, " has", " have"),
" to be of length ", d))
}
if (sv == TRUE) {
if (length(starting_vals$sigma2_st) != length(y)) {
stop("sigma2_st has to be the same length as y if sv is TRUE")
}
num_input_bad <- sapply(starting_vals$sigma2_st, numeric_input_bad)
if (any(num_input_bad)) {
stop("sigma2_st may only contain real, positive numbers")
}
} else if (numeric_input_bad(starting_vals$sigma2_st)) {
stop("sigma2_st has to be a real, positive number")
}
vec_valued_pos <- vec_valued[vec_valued != "beta_mean_st"]
bad_content <- sapply(starting_vals[vec_valued_pos], function(x) any(sapply(x, numeric_input_bad)))
if (any(bad_content)) {
bad_content_names <- vec_valued_pos[bad_content]
stop(paste0(paste(bad_content_names, collapse = ", "), " may only contain real, positive numbers"))
}
if (any(sapply(starting_vals$beta_mean_st, numeric_input_bad_))) {
stop("beta_mean_st may only contain real numbers")
}
to_check_num_st <- names(starting_vals)[!names(starting_vals) %in% c(vec_valued, "h0_st", "sv_mu_st", "sigma2_st")]
bad_num_st <- sapply(starting_vals[to_check_num_st], numeric_input_bad)
if (any(bad_num_st)) {
bad_num_names <- to_check_num_st[bad_num_st]
stop(paste0(paste(bad_num_names, collapse = ", "),
ifelse(length(bad_num_names) == 1, " has", " have"),
" to be a real, positive number"))
}
if (numeric_input_bad_(starting_vals$h0_st)) {
stop("h0_st has to be a real number")
}
if (numeric_input_bad_(starting_vals$sv_mu_st)) {
stop("sv_mu_st has to be a real number")
}
if (abs(starting_vals$sv_phi_st) >= 1) {
stop("sv_phi_st has to be between -1 and 1")
}
if (starting_vals$c_xi_st >= 0.5) {
warning("c_xi_st is >= 0.5, which means the algorithm will not move away from the starting value", immediate. = TRUE)
}
if (starting_vals$c_tau_st >= 0.5) {
warning("c_tau_st is >= 0.5, which means the algorithm will not move away from the starting value", immediate. = TRUE)
}
runtime <- system.time({
suppressWarnings({
res <- shrinkTVP_cpp(y,
x,
mod_type,
niter,
nburn,
nthin,
hyperprior_param$c0,
hyperprior_param$g0,
hyperprior_param$G0,
hyperprior_param$d1,
hyperprior_param$d2,
hyperprior_param$e1,
hyperprior_param$e2,
learn_lambda2_B,
learn_kappa2_B,
lambda2_B,
kappa2_B,
learn_a_xi,
learn_a_tau,
a_xi,
a_tau,
learn_c_xi,
learn_c_tau,
c_xi,
c_tau,
a_eq_c_xi,
a_eq_c_tau,
MH_tuning$a_xi_tuning_par,
MH_tuning$a_tau_tuning_par,
MH_tuning$c_xi_tuning_par,
MH_tuning$c_tau_tuning_par,
hyperprior_param$beta_a_xi,
hyperprior_param$beta_a_tau,
hyperprior_param$alpha_a_xi,
hyperprior_param$alpha_a_tau,
hyperprior_param$beta_c_xi,
hyperprior_param$beta_c_tau,
hyperprior_param$alpha_c_xi,
hyperprior_param$alpha_c_tau,
display_progress,
sv,
sv_param$Bsigma_sv,
sv_param$a0_sv,
sv_param$b0_sv,
sv_param$bmu,
sv_param$Bmu,
unlist(MH_tuning[grep("adaptive", names(MH_tuning))]),
unlist(MH_tuning[grep("target", names(MH_tuning))]),
unlist(MH_tuning[grep("max", names(MH_tuning))]),
unlist(MH_tuning[grep("size", names(MH_tuning))]),
starting_vals)
})
})
if (res$internals$success_vals$success == FALSE){
stop(paste0("The sampler failed at iteration ",
res$internals$success_vals$fail_iter,
" while trying to ",
res$internals$success_vals$fail, ". ",
"Try rerunning the model. ",
"If the sampler fails again, try changing the prior to be more informative. ",
"If the problem still persists, please contact the maintainer: ",
maintainer("shrinkTVP")))
} else {
res$internals$success_vals <- NULL
}
if (display_progress == TRUE){
cat("Timing (elapsed): ", file = stderr())
cat(runtime["elapsed"], file = stderr())
cat(" seconds.\n", file = stderr())
cat(round( (niter + nburn) / runtime[3]), "iterations per second.\n\n", file = stderr())
cat("Converting results to coda objects and summarizing draws... ", file = stderr())
}
if (sv == FALSE){
res$sigma2 <- matrix(res$sigma2[1, 1, ], ncol = 1)
}
res[sapply(res, function(x) 0 %in% dim(x))] <- NULL
res$MH_diag[sapply(res$MH_diag, function(x) 0 %in% dim(x))] <- NULL
if (a_eq_c_tau == TRUE) {
res$c_tau <- NULL
}
if (a_eq_c_xi == TRUE) {
res$c_xi <- NULL
}
res$priorvals <- c(hyperprior_param,
sv_param,
a_xi = a_xi,
a_tau = a_tau,
c_xi = c_xi,
c_tau = c_tau,
lambda2_B = lambda2_B,
kappa2_B = kappa2_B)
res[["model"]] <- list()
res$model$x <- x
res$model$y <- y
res$model$formula <- formula
res$model$xlevels <- .getXlevels(mt, mf)
res$model$terms <- mt
res$summaries <- list()
nsave <- floor((niter - nburn)/nthin)
for (i in names(res)){
attr(res[[i]], "type") <- ifelse(nsave %in% dim(res[[i]]), "sample", "stat")
if (attr(res[[i]], "type") == "sample"){
if (dim(res[[i]])[2] == d){
colnames(res[[i]]) <- paste0(i, "_", colnames(x))
} else if (dim(res[[i]])[2] == 2 * d){
colnames(res[[i]]) <- paste0(i, "_", rep( colnames(x), 2))
} else {
colnames(res[[i]]) <- i
}
}
if (attr(res[[i]], "type") == "sample"){
if (is.na(dim(res[[i]])[3]) == FALSE){
dat <- res[[i]]
res[[i]] <- list()
for (j in 1:dim(dat)[2]){
res[[i]][[j]] <- as.mcmc(t(dat[, j, ]), start = niter - nburn, end = niter, thin = nthin)
colnames(res[[i]][[j]]) <- paste0(i, "_", j, "_", 1:ncol(res[[i]][[j]]))
class(res[[i]][[j]]) <- c("mcmc.tvp", "mcmc")
attr(res[[i]][[j]], "type") <- "sample"
attr(res[[i]][[j]], "index") <- index
}
if (length(res[[i]]) == 1){
res[[i]] <- res[[i]][[j]]
attr(res[[i]][[j]], "index") <- index
}
attr(res[[i]], "type") <- "sample"
if (dim(dat)[2] > 1){
names(res[[i]]) <- colnames(dat)
}
} else {
res[[i]] <- as.mcmc(res[[i]], start = niter - nburn, end = niter, thin = nthin)
}
}
if (is.list(res[[i]]) == FALSE & attr(res[[i]], "type") == "sample") {
if (i != "theta_sr" & !(i == "sigma2" & sv == TRUE) & i != "beta") {
res$summaries[[i]] <- t(apply(res[[i]], 2, function(x){
obj <- as.mcmc(x, start = niter - nburn, end = niter, thin = nthin)
ESS <- tryCatch(coda::effectiveSize(obj),
error = function(err) {
warning("Calculation of effective sample size failed for one or more variable(s). This can happen if the prior placed on the model induces extreme shrinkage.")
return(NA)
}, silent = TRUE)
return(c("mean" = mean(obj),
"sd" = sd(obj),
"median" = median(obj),
"HPD" = HPDinterval(obj)[c(1, 2)],
"ESS" = round(ESS)))
}))
} else if (i == "theta_sr") {
res$summaries[[i]] <- t(apply(res[[i]], 2, function(x){
obj <- as.mcmc(abs(x), start = niter - nburn, end = niter, thin = nthin)
ESS <- tryCatch(coda::effectiveSize(obj),
error = function(err) {
warning("Calculation of effective sample size failed for one or more variable(s). This can happen if the prior placed on the model induces extreme shrinkage.")
return(NA)
}, silent = TRUE)
return(c("mean" = mean(obj),
"sd" = sd(obj),
"median" = median(obj),
"HPD" = HPDinterval(obj)[c(1, 2)],
"ESS" = round(ESS)))
}))
}
}
}
if (display_progress == TRUE) {
cat("Done!\n", file = stderr())
}
attr(res, "class") <- "shrinkTVP"
attr(res, "learn_a_xi") <- learn_a_xi
attr(res, "learn_a_tau") <- learn_a_tau
attr(res, "learn_c_xi") <- learn_c_xi
attr(res, "learn_c_tau") <- learn_c_tau
attr(res, "learn_kappa2_B") <- learn_kappa2_B
attr(res, "learn_lambda2_B") <- learn_lambda2_B
attr(res, "a_eq_c_xi") <- a_eq_c_xi
attr(res, "a_eq_c_tau") <- a_eq_c_tau
attr(res, "niter") <- niter
attr(res, "nburn") <- nburn
attr(res, "nthin") <- nthin
attr(res, "sv") <- sv
attr(res, "colnames") <- colnames(x)
attr(res, "index") <- index
attr(res, "p") <- p
attr(res, "mod_type") <- mod_type
return(res)
}
updateTVP <- function(y,
x,
curr_draws,
mod_type = "double",
learn_a_xi = TRUE,
learn_a_tau = TRUE,
a_xi = 0.1,
a_tau = 0.1,
learn_c_xi = TRUE,
learn_c_tau = TRUE,
c_xi = 0.1,
c_tau = 0.1,
a_eq_c_xi = FALSE,
a_eq_c_tau = FALSE,
learn_kappa2_B = TRUE,
learn_lambda2_B = TRUE,
kappa2_B = 20,
lambda2_B = 20,
hyperprior_param,
sv = FALSE,
sv_param,
MH_tuning){
default_hyper <- list(c0 = 2.5,
g0 = 5,
G0 = 5 / (2.5 - 1),
e1 = 0.001,
e2 = 0.001,
d1 = 0.001,
d2 = 0.001,
beta_a_xi = 10,
beta_a_tau = 10,
alpha_a_xi = 5,
alpha_a_tau = 5,
beta_c_xi = 2,
beta_c_tau = 2,
alpha_c_xi = 5,
alpha_c_tau = 5)
default_hyper_sv <- list(Bsigma_sv = 1,
a0_sv = 5,
b0_sv = 1.5,
bmu = 0,
Bmu = 1)
default_tuning_par <- list(a_xi_adaptive = TRUE,
a_xi_tuning_par = 1,
a_xi_target_rate = 0.44,
a_xi_max_adapt = 0.01,
a_xi_batch_size = 50,
a_tau_adaptive = TRUE,
a_tau_tuning_par = 1,
a_tau_target_rate = 0.44,
a_tau_max_adapt = 0.01,
a_tau_batch_size = 50,
c_xi_adaptive = TRUE,
c_xi_tuning_par = 1,
c_xi_target_rate = 0.44,
c_xi_max_adapt = 0.01,
c_xi_batch_size = 50,
c_tau_adaptive = TRUE,
c_tau_tuning_par = 1,
c_tau_target_rate = 0.44,
c_tau_max_adapt = 0.01,
c_tau_batch_size = 50)
if (missing(MH_tuning)){
MH_tuning <- default_tuning_par
} else {
MH_tuning <- list_merger(default_tuning_par, MH_tuning)
}
if (missing(hyperprior_param)){
hyperprior_param <- default_hyper
} else {
hyperprior_param <- list_merger(default_hyper, hyperprior_param)
}
if (missing(sv_param) | sv == FALSE){
sv_param <- default_hyper_sv
} else {
sv_param <- list_merger(default_hyper_sv, sv_param)
}
suppressWarnings({
res <- shrinkTVP_cpp(y,
x,
mod_type,
1,
0,
1,
hyperprior_param$c0,
hyperprior_param$g0,
hyperprior_param$G0,
hyperprior_param$d1,
hyperprior_param$d2,
hyperprior_param$e1,
hyperprior_param$e2,
learn_lambda2_B,
learn_kappa2_B,
lambda2_B,
kappa2_B,
learn_a_xi,
learn_a_tau,
a_xi,
a_tau,
learn_c_xi,
learn_c_tau,
c_xi,
c_tau,
a_eq_c_xi,
a_eq_c_tau,
MH_tuning$a_xi_tuning_par,
MH_tuning$a_tau_tuning_par,
MH_tuning$c_xi_tuning_par,
MH_tuning$c_tau_tuning_par,
hyperprior_param$beta_a_xi,
hyperprior_param$beta_a_tau,
hyperprior_param$alpha_a_xi,
hyperprior_param$alpha_a_tau,
hyperprior_param$beta_c_xi,
hyperprior_param$beta_c_tau,
hyperprior_param$alpha_c_xi,
hyperprior_param$alpha_c_tau,
FALSE,
sv,
sv_param$Bsigma_sv,
sv_param$a0_sv,
sv_param$b0_sv,
sv_param$bmu,
sv_param$Bmu,
unlist(MH_tuning[grep("adaptive", names(MH_tuning))]),
unlist(MH_tuning[grep("target", names(MH_tuning))]),
unlist(MH_tuning[grep("max", names(MH_tuning))]),
unlist(MH_tuning[grep("size", names(MH_tuning))]),
curr_draws)
})
if(sv == FALSE){
res$sigma2_st <- res$sigma2_st[1]
}
return(res)
} |
context("Ensuring that the `tab_footnote()` function works as expected")
data <-
mtcars %>%
gt(rownames_to_stub = TRUE) %>%
cols_move_to_start(columns = c("gear", "carb")) %>%
tab_stubhead(label = "cars") %>%
cols_hide(columns = "mpg") %>%
cols_hide(columns = "vs") %>%
tab_row_group(
label = "Mercs",
rows = contains("Merc"),
) %>%
tab_row_group(
label = "Mazdas",
rows = contains("Mazda"),
) %>%
tab_options(row_group.default_label = "Others") %>%
tab_spanner(
label = "gear_carb_cyl",
id = "gcc",
columns = c(gear, carb, cyl)
) %>%
row_group_order(groups = c("Mazdas", "Mercs")) %>%
cols_merge_range(
col_begin = "disp",
col_end = "drat"
) %>%
tab_header(
title = "Title",
subtitle = "Subtitle"
) %>%
tab_source_note(source_note = "this is a source note") %>%
summary_rows(
groups = c("Mazdas", "Mercs"),
columns = c(hp, wt, qsec),
fns = list(
~mean(., na.rm = TRUE),
~sum(., na.rm = TRUE))
) %>%
summary_rows(
columns = c(hp, wt),
fns = list(
~mean(., na.rm = TRUE),
~sum(., na.rm = TRUE))
)
data_2 <-
gtcars %>%
dplyr::filter(ctry_origin == "Germany") %>%
dplyr::group_by(mfr) %>%
dplyr::top_n(2, msrp) %>%
dplyr::ungroup() %>%
dplyr::select(mfr, model, drivetrain, msrp) %>%
gt() %>%
tab_spanner(
label = "make and model",
id = "mm",
columns = c(mfr, model)
) %>%
tab_spanner(
label = "specs and pricing",
id = "sp",
columns = c(drivetrain, msrp)
) %>%
tab_footnote(
footnote = "Prices in USD.",
locations = cells_column_labels(columns = msrp)
) %>%
tab_footnote(
footnote = "AWD = All Wheel Drive, RWD = Rear Wheel Drive.",
locations = cells_column_labels(columns = drivetrain)
) %>%
tab_footnote(
footnote = "The most important details.",
locations = cells_column_spanners(spanners = "sp")
) %>%
tab_footnote(
footnote = "German cars only.",
locations = cells_column_spanners(spanners = "mm")
)
data_3 <-
gtcars %>%
dplyr::filter(ctry_origin == "Germany") %>%
dplyr::group_by(mfr) %>%
dplyr::top_n(3, msrp) %>%
dplyr::ungroup() %>%
dplyr::select(mfr, model, drivetrain, msrp) %>%
gt(rowname_col = "model", groupname_col = "mfr") %>%
summary_rows(
groups = c("BMW", "Audi"),
columns = "msrp",
fns = list(
~mean(., na.rm = TRUE),
~min(., na.rm = TRUE))
) %>%
summary_rows(
columns = "msrp",
fns = list(
~min(., na.rm = TRUE),
~max(., na.rm = TRUE))
) %>%
tab_footnote(
footnote = "Average price for BMW and Audi.",
locations = cells_summary(
groups = c("BMW", "Audi"),
columns = "msrp",
rows = starts_with("me"))
) %>%
tab_footnote(
footnote = "Maximum price across all cars.",
locations = cells_grand_summary(
columns = "msrp",
rows = starts_with("ma"))
) %>%
tab_footnote(
footnote = "Minimum price across all cars.",
locations = cells_grand_summary(
columns = "msrp",
rows = starts_with("mi"))
)
data_4 <-
sp500 %>%
dplyr::filter(
date >= "2015-01-05" &
date <="2015-01-10"
) %>%
dplyr::select(
-c(adj_close, volume, high, low)
) %>%
gt() %>%
tab_header(
title = "S&P 500",
subtitle = "Open and Close Values"
) %>%
tab_footnote(
footnote = "All values in USD.",
locations = list(
cells_title(groups = "subtitle")
)
) %>%
tab_footnote(
footnote = "Standard and Poor 500.",
locations = list(
cells_title(groups = "title")
)
)
check_suggests <- function() {
skip_if_not_installed("rvest")
skip_if_not_installed("xml2")
}
selection_value <- function(html, key) {
selection <- paste0("[", key, "]")
html %>%
rvest::html_nodes(selection) %>%
rvest::html_attr(key)
}
selection_text <- function(html, selection) {
html %>%
rvest::html_nodes(selection) %>%
rvest::html_text()
}
test_that("the `tab_footnote()` function works correctly", {
check_suggests()
tab <-
data %>%
tab_footnote(
footnote = "Column labels and stub footnote.",
locations = list(
cells_column_labels(),
cells_stub(rows = TRUE)
)
)
dt_footnotes_get(data = tab) %>%
dplyr::pull(locname) %>%
unique() %>%
expect_equal(c("columns_columns", "stub"))
dt_footnotes_get(data = tab) %>%
dplyr::pull(footnotes) %>%
unlist() %>%
unique() %>%
expect_equal("Column labels and stub footnote.")
tab <-
data %>%
tab_footnote(
footnote = "Stub cell footnote.",
locations = cells_stub(rows = "Merc 240D")
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("stub", NA_character_, NA_character_, "5", "8",
NA_character_, "Stub cell footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Title footnote.",
locations = cells_title(groups = "title")
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("title", NA_character_, NA_character_, "1", NA_character_,
NA_character_, "Title footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Subtitle footnote.",
locations = cells_title(groups = "subtitle")
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("subtitle", NA_character_, NA_character_, "2", NA_character_,
NA_character_, "Subtitle footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Stubhead label footnote.",
locations = cells_stubhead()
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("stubhead", NA_character_, NA_character_, "2.5", NA_character_,
NA_character_, "Stubhead label footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Summary cell footnote.",
locations = cells_summary(
groups = "Mercs", columns = "hp", rows = 2)
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("summary_cells", "Mercs", "hp", "5", "2", NA_character_,
"Summary cell footnote.")
)
expect_error(
data %>%
tab_footnote(
footnote = "Summary cell footnote.",
locations = cells_summary(
groups = "Mercs", columns = starts_with("x"), rows = 2)
)
)
expect_error(
data %>%
tab_footnote(
footnote = "Summary cell footnote.",
locations = cells_summary(
groups = "Mercs", columns = starts_with("m"), rows = starts_with("x"))
)
)
tab <-
data %>%
tab_footnote(
footnote = "Grand summary cell footnote.",
locations = cells_grand_summary(
columns = wt, rows = starts_with("s")
)
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("grand_summary_cells", "::GRAND_SUMMARY", "wt", "6", "2",
NA_character_, "Grand summary cell footnote.")
)
expect_error(
data %>%
tab_footnote(
footnote = "Grand summary cell footnote.",
locations = cells_grand_summary(
columns = starts_with("x"), rows = 2)
)
)
expect_error(
data %>%
tab_footnote(
footnote = "Grand summary cell footnote.",
locations = cells_grand_summary(
columns = starts_with("m"), rows = starts_with("x"))
)
)
tab <-
data %>%
tab_footnote(
footnote = "Summary cell footnote.",
locations = cells_summary(
groups = "Mercs", columns = "hp", rows = 2)
) %>%
tab_footnote(
footnote = "Grand summary cell footnote.",
locations = cells_grand_summary(
columns = wt, rows = starts_with("s")
)
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(2)
expect_attr_equal(
tab, "_footnotes",
c("summary_cells", "grand_summary_cells",
"Mercs", "::GRAND_SUMMARY", "hp", "wt",
"5", "6", "2", "2", NA_character_, NA_character_,
"Summary cell footnote.",
"Grand summary cell footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Group cell footnote.",
locations = cells_row_groups(groups = "Mazdas"))
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("row_groups", "Mazdas", NA_character_, "5", NA_character_,
NA_character_, "Group cell footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Column group footnote.",
locations = cells_column_spanners(spanners = "gcc")
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("columns_groups", "gcc", NA_character_, "3", NA_character_,
NA_character_, "Column group footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Single column label footnote.",
locations = cells_column_labels(columns = "gear")
)
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("columns_columns", NA_character_, "gear", "4", NA_character_,
NA_character_, "Single column label footnote.")
)
tab <-
data %>%
tab_footnote(
footnote = "Five rows footnote.",
locations = cells_body(columns = "hp", rows = 1:5))
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(5)
dt_footnotes_get(data = tab) %>%
dplyr::pull(rownum) %>%
expect_equal(1:5)
dt_footnotes_get(data = tab) %>%
dplyr::pull(footnotes) %>%
unlist() %>%
unique() %>%
expect_equal("Five rows footnote.")
dt_footnotes_get(data = tab) %>%
dplyr::pull(locname) %>%
unique() %>%
expect_equal("data")
dt_footnotes_get(data = tab) %>%
dplyr::pull(colname) %>%
unique() %>%
expect_equal("hp")
expect_error(
data %>%
tab_footnote(
footnote = "Footnote error.",
locations = cells_body(columns = "disp", rows = "Mazda RX7")))
tab <-
data %>%
tab_footnote(
footnote = "A footnote.",
locations = cells_body(columns = "disp", rows = c("Mazda RX4")))
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(1)
dt_footnotes_get(data = tab) %>%
unlist() %>%
unname() %>%
expect_equal(
c("data", NA_character_, "disp", "5", "1", NA_character_, "A footnote."))
tab <-
data %>%
tab_footnote(
footnote = "A footnote.",
locations = cells_body(columns = c(disp, hp), rows = "Mazda RX4"))
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(2)
dt_footnotes_get(data = tab)[1, ] %>%
unlist() %>%
unname() %>%
expect_equal(c(
"data", NA_character_, "disp", "5", "1", NA_character_, "A footnote."))
dt_footnotes_get(data = tab)[2, ] %>%
unlist() %>%
unname() %>%
expect_equal(c(
"data", NA_character_, "hp", "5", "1", NA_character_, "A footnote."))
tab <- data_2
dt_footnotes_get(data = tab) %>%
nrow() %>%
expect_equal(4)
dt_footnotes_get(data = tab) %>%
dplyr::pull(locname) %>%
unique() %>%
expect_equal(c("columns_columns", "columns_groups"))
tbl_html <-
tab %>%
render_as_html() %>%
xml2::read_html()
tbl_html %>%
selection_text(selection = "[class='gt_footnote']") %>%
tidy_gsub("\n ", "") %>%
expect_equal(
c(
"1 German cars only.",
"2 The most important details.",
"3 AWD = All Wheel Drive, RWD = Rear Wheel Drive.",
"4 Prices in USD.")
)
tbl_html %>%
selection_text(selection = "[class='gt_footnote_marks']") %>%
tidy_gsub("\\s+", "") %>%
expect_equal(rep(as.character(1:4), 2))
})
test_that("the footnotes table is structured correctly", {
footnotes_tbl <- dt_footnotes_get(data = data_3)
expect_is(footnotes_tbl, "tbl_df")
expect_equal(
colnames(footnotes_tbl),
c("locname", "grpname", "colname", "locnum", "rownum",
"colnum", "footnotes")
)
expect_equal(nrow(footnotes_tbl), 4)
expect_equal(
footnotes_tbl$locname,
c("summary_cells", "summary_cells",
"grand_summary_cells", "grand_summary_cells")
)
expect_equal(footnotes_tbl$locnum, c(5, 5, 6, 6))
expect_equal(footnotes_tbl$grpname, c("BMW", "Audi", "::GRAND_SUMMARY", "::GRAND_SUMMARY"))
expect_equal(footnotes_tbl$colname, rep("msrp", 4))
expect_equal(footnotes_tbl$colnum, rep(NA_integer_, 4))
expect_equal(
unlist(footnotes_tbl$footnotes),
c("Average price for BMW and Audi.", "Average price for BMW and Audi.",
"Maximum price across all cars.", "Minimum price across all cars.")
)
footnotes_tbl <- dt_footnotes_get(data = data_4)
expect_is(footnotes_tbl, "tbl_df")
expect_equal(
colnames(footnotes_tbl),
c("locname", "grpname", "colname", "locnum", "rownum",
"colnum", "footnotes")
)
expect_equal(nrow(footnotes_tbl), 2)
expect_equal(footnotes_tbl$locname, c("subtitle", "title"))
expect_equal(footnotes_tbl$grpname, c(NA_character_, NA_character_))
expect_equal(footnotes_tbl$colname, c(NA_character_, NA_character_))
expect_equal(footnotes_tbl$locnum, c(2, 1))
expect_equal(footnotes_tbl$rownum, c(NA_integer_, NA_integer_))
expect_equal(footnotes_tbl$colnum, c(NA_integer_, NA_integer_))
expect_equal(
unlist(footnotes_tbl$footnotes),
c("All values in USD.", "Standard and Poor 500.")
)
tbl_html <-
data_4 %>%
render_as_html() %>%
xml2::read_html()
tbl_html %>%
selection_text(selection = "[class='gt_heading gt_title gt_font_normal']") %>%
expect_equal("S&P 5001")
tbl_html %>%
selection_text(selection = "[class='gt_heading gt_subtitle gt_font_normal gt_bottom_border']") %>%
expect_equal("Open and Close Values2")
})
test_that("the `list_of_summaries` table is structured correctly", {
gtcars_built <-
gtcars %>%
dplyr::filter(ctry_origin == "Germany") %>%
dplyr::group_by(mfr) %>%
dplyr::top_n(3, msrp) %>%
dplyr::ungroup() %>%
dplyr::select(mfr, model, drivetrain, msrp) %>%
gt(rowname_col = "model", groupname_col = "mfr") %>%
summary_rows(
groups = c("BMW", "Audi"),
columns = msrp,
fns = list(
~mean(., na.rm = TRUE),
~min(., na.rm = TRUE))
) %>%
summary_rows(
columns = msrp,
fns = list(
~min(., na.rm = TRUE),
~max(., na.rm = TRUE))
) %>%
build_data(context = "html")
gtcars_built_summary_df <- dt_summary_df_get(data = gtcars_built)
gtcars_built_summary_df_data <- dt_summary_df_data_get(data = gtcars_built)
gtcars_built_summary_df_display <- dt_summary_df_display_get(data = gtcars_built)
expect_equal(length(gtcars_built_summary_df), 2)
expect_equal(
names(gtcars_built_summary_df),
c("summary_df_data_list", "summary_df_display_list")
)
expect_equal(length(gtcars_built_summary_df_data$summary_df_data_list), 3)
expect_equal(length(gtcars_built_summary_df_display$summary_df_display_list), 3)
expect_equal(
names(gtcars_built_summary_df_data$summary_df_data_list),
c("BMW", "Audi", "::GRAND_SUMMARY")
)
expect_equal(
names(gtcars_built_summary_df_display$summary_df_display_list),
c("::GRAND_SUMMARY", "Audi", "BMW")
)
expect_equal(
gtcars_built_summary_df_display$summary_df_display_list$`::GRAND_SUMMARY`$msrp,
c("56,000.00", "140,700.00")
)
expect_equal(
gtcars_built_summary_df_display$summary_df_display_list$Audi$msrp,
c("113,233.33", "108,900.00")
)
expect_equal(
gtcars_built_summary_df_display$summary_df_display_list$BMW$msrp,
c("116,066.67", "94,100.00")
)
}) |
describeRNA = function(counts, biotypes, groups, report = FALSE, verbose = FALSE, filter = 1)
{
table = table(biotypes$gene_biotype, exclude = NA)
target = c("miRNA", "protein_coding", "lincRNA", "pseudogene",
"snoRNA", "snRNA", "ribozyme")
table = data.frame(table)
index <- table$Var1 %in% target
barplot = table[index, ]
if (report) {
File <- tempfile(fileext = ".pdf")
warning("\n Temporary report at ", File, call. = FALSE,
immediate. = TRUE)
dir.create(dirname(File), showWarnings=FALSE)
pdf(File, width = 15, height = 15)
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
par(mfrow = c(2, 2))
plotMDS(counts, main = "Multidimensional Scaling")
sortbar = barplot[order(barplot$Freq, decreasing = TRUE),
]
barplot(sortbar$Freq, names.arg = sortbar$Var1, col = 1:6,
main = "Absolute Quantity")
x = t(counts)
x = hclust(dist(x))
plot(x, main = "Cluster Dendrogram")
wordcloud(table$Var1, table$Freq, colors = brewer.pal(5,"Dark2"), min.freq = 10)
dev.off()
}
if (filter == 1) {
data_filtered = filterByExpr(counts, group = groups)
data_filtered = counts[data_filtered, ]
}
if (filter == 2) {
data_filtered = filterByExpr(counts, group = groups,
min.count = 15, min.total.count = 25)
data_filtered = counts[data_filtered, ]
}
if (filter == 3) {
data_filtered = filterByExpr(counts, group = groups,
min.count = 25, min.total.count = 40)
data_filtered = counts[data_filtered, ]
}
if (verbose) {
print(knitr::kable(table))
cat("\nGENES", sep = "\n")
cat("Total number of genes:", nrow(counts))
cat("\nGenes remaining:", dim(data_filtered)[1])
}
pos <- 1
envir = as.environment(pos)
BioInsight <- assign("BioInsight", data_filtered, envir = envir)
} |
context("Plot annual stats")
test_that("a list of plots is created", {
skip_on_cran()
skip_on_ci()
plots <- plot_annual_stats(station_number = "08NM116",
start_year = 1981,
end_year = 2010,
water_year_start = 10)
expect_true("list" %in% class(plots) & "gg" %in% sapply(plots, class))
})
test_that("multiple plots are created with multiple groups", {
skip_on_cran()
skip_on_ci()
plots <- plot_annual_stats(station_number = c("08NM116","08NM242"),
ignore_missing = TRUE)
expect_true(length(plots) == 2)
}) |
.edgeDataMatrix <- function(g, att) {
if (class(g)[1] != "graphNEL")
stop("'g' has to be a 'graphNEL' object")
stopifnot(.validateGraph(g))
if (is.null(edgeDataDefaults(g)[[att]]))
stop(paste("edge attribute", att, "not set"))
raw <- edgeData(g)
n <- nodes(g)
m <- matrix(0, nrow=length(n), ncol=length(n), dimnames=list(n, n))
for (e in names(raw)) {
vert <- strsplit(e, "\\|")[[1]]
m[[vert[[1]], vert[[2]]]] <- raw[[e]][[att]]
}
m
} |
context("sum of llcont")
test_that("lavaan object", {
if (isTRUE(require("lavaan"))) {
HS.model <- 'visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit1 <- cfa(HS.model, data=HolzingerSwineford1939)
fit2 <- cfa(HS.model, data=HolzingerSwineford1939, group="school")
fit3 <- cfa(HS.model, data = HolzingerSwineford1939, group = "school",
group.equal = c("loadings"))
fit4 <- cfa(HS.model, data = HolzingerSwineford1939, group = "school",
group.equal = c("loadings"),
group.partial = c("visual=~x2", "x7~1"))
expect_equal(sum(llcont(fit1)), as.numeric(logLik(fit1)))
expect_equal(sum(llcont(fit2)), as.numeric(logLik(fit2)))
expect_equal(round(sum(llcont(fit3)) - as.numeric(logLik(fit3)), 8), 0L)
expect_equal(sum(llcont(fit4)), as.numeric(logLik(fit4)))
HS.model2 <- 'visual =~ x1 + 0.5*x2 + c(0.6, 0.8)*x3
textual =~ x4 + start(c(1.2, 0.6))*x5 + a*x6
speed =~ x7 + x8 + x9'
fit5 <- cfa(HS.model2, data=HolzingerSwineford1939, group="school")
expect_equal(round(sum(llcont(fit5)) - as.numeric(logLik(fit5)), 8), 0L)
HS.model3 <- 'visual =~ x1 + x2 + c(v3,v3)*x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9'
fit6 <- cfa(HS.model3, data=HolzingerSwineford1939, group="school")
expect_equal(sum(llcont(fit6)), as.numeric(logLik(fit6)))
set.seed(1234)
pop.model <- ' f =~ 0.7*y1 + 0.7*y2 + 0.7*y3 + 0.7*y4 + 0.7*y5
f ~ (-2.3)*x1 + 0.8*x2
y1 ~ 0.2*x2
y3 ~ 0.7*x1 '
Data <- simulateData(pop.model, sample.nobs=100)
model <- ' f =~ y1 + y2 + y3 + y4 + y5
f ~ x1 + x2
y1 ~ x2
y3 ~ x1 '
obs <- rbinom(prod(dim(Data)), 1, .9)
Data <- Data*obs
Data[Data==0] <- NA
Data[95,] <- NA
fit7 <- sem(model, data=Data, fixed.x=TRUE, meanstructure=TRUE, missing='ml')
expect_equal(sum(llcont(fit7)), as.numeric(logLik(fit7)))
}
})
test_that("glm object", {
if (isTRUE(require("faraway")) && isTRUE(require("MASS"))) {
bin1 <- glm(formula=am ~ hp + wt, data=mtcars, family=binomial)
bin2 <- glm(cbind(Menarche, Total-Menarche) ~ Age,
family=binomial(logit), data=menarche)
expect_equal(sum(llcont(bin1)), as.numeric(logLik(bin1)))
expect_equal(sum(llcont(bin2)), as.numeric(logLik(bin2)))
qbin1 <- glm(formula=am ~ hp + wt, data=mtcars, family=quasibinomial)
qbin2 <- glm(cbind(Menarche, Total-Menarche) ~ Age,
family=quasibinomial, data=menarche)
expect_equal(sum(llcont(qbin1)), as.numeric(logLik(qbin1)))
expect_equal(sum(llcont(qbin2)), as.numeric(logLik(qbin2)))
gau1 <- glm(Species ~ Area + Elevation + Nearest + Scruz + Adjacent,
data=gala, family=gaussian)
gau2 <- glm(Species ~ Area + Elevation + Nearest, data=gala,
family=gaussian)
expect_equal(sum(llcont(gau1)), as.numeric(logLik(gau1)))
expect_equal(sum(llcont(gau2)), as.numeric(logLik(gau2)))
invGau1 <- glm(actual ~ projected-1,
family=inverse.gaussian(link="identity"), cpd)
expect_equal(sum(llcont(invGau1)), as.numeric(logLik(invGau1)))
clotting <- data.frame(u = c(5,10,15,20,30,40,60,80,100),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12))
gam1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma)
expect_equal(sum(llcont(gam1)), as.numeric(logLik(gam1)))
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
d.AD <- data.frame(treatment, outcome, counts)
pois1 <- glm(counts ~ outcome + treatment, family = poisson)
pois2 <- glm(counts ~ outcome, family = poisson)
expect_equal(sum(llcont(pois1)), as.numeric(logLik(pois1)))
expect_equal(sum(llcont(pois2)), as.numeric(logLik(pois2)))
qpois1 <- glm(counts ~ outcome + treatment, family = quasipoisson)
qpois2 <- glm(counts ~ outcome, family = quasipoisson)
expect_equal(sum(llcont(qpois1)), as.numeric(logLik(qpois1)))
expect_equal(sum(llcont(qpois2)), as.numeric(logLik(qpois2)))
nb1 <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
expect_equal(sum(llcont(nb1)), as.numeric(logLik(nb1)))
}
})
test_that("clm object", {
if (isTRUE(require("ordinal")) && isTRUE(require("MASS"))) {
clm1 <- clm(rating ~ temp * contact, data = wine)
clm2 <- update(clm1, ~.-temp:contact)
clm3 <- update(clm1, link = "logit")
clm4 <- update(clm1, link = "probit")
clm5 <- update(clm1, link = "loglog")
clm6 <- update(clm1, link = "cloglog")
clm7 <- update(clm1, link = "cauchit")
clm8 <- update(clm1, threshold = "symmetric")
clm9 <- update(clm1, threshold = "equidistant")
clm10 <- clm(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
expect_equal(sum(llcont(clm1)), as.numeric(logLik(clm1)))
expect_equal(sum(llcont(clm2)), as.numeric(logLik(clm2)))
expect_equal(sum(llcont(clm3)), as.numeric(logLik(clm3)))
expect_equal(sum(llcont(clm4)), as.numeric(logLik(clm4)))
expect_equal(sum(llcont(clm5)), as.numeric(logLik(clm5)))
expect_equal(sum(llcont(clm6)), as.numeric(logLik(clm6)))
expect_equal(sum(llcont(clm7)), as.numeric(logLik(clm7)))
expect_equal(sum(llcont(clm8)), as.numeric(logLik(clm8)))
expect_equal(sum(llcont(clm9)), as.numeric(logLik(clm9)))
expect_equal(sum(llcont(clm10)), as.numeric(logLik(clm10)))
}
})
test_that("hurdle object", {
if (isTRUE(require("pscl"))) {
hurdle1 <- hurdle(formula = art ~ ., data = bioChemists)
hurdle2 <- hurdle(formula = art ~ ., data = bioChemists, separate=FALSE)
hurdle3 <- hurdle(art ~ ., data = bioChemists, zero = "geometric")
hurdle4 <- hurdle(art ~ fem + ment, data = bioChemists,
dist = "negbin", zero = "negbin")
hurdle5 <- hurdle(art ~ ., data = bioChemists, dist = "negbin")
expect_equal(sum(llcont(hurdle1)), as.numeric(logLik(hurdle1)))
expect_equal(sum(llcont(hurdle2)), as.numeric(logLik(hurdle2)))
expect_equal(sum(llcont(hurdle3)), as.numeric(logLik(hurdle3)))
expect_equal(sum(llcont(hurdle4)), as.numeric(logLik(hurdle4)))
expect_equal(sum(llcont(hurdle5)), as.numeric(logLik(hurdle5)))
}
})
test_that("zeroinfl object", {
if (isTRUE(require("pscl"))) {
zi1 <- zeroinfl(art ~ . | 1, data = bioChemists)
zi2 <- zeroinfl(art ~ . | 1, data = bioChemists, dist = "negbin")
zi3 <- zeroinfl(art ~ . | ., data = bioChemists)
zi4 <- zeroinfl(art ~ . | ., data = bioChemists, dist = "negbin")
expect_equal(sum(llcont(zi1)), as.numeric(logLik(zi1)))
expect_equal(sum(llcont(zi2)), as.numeric(logLik(zi2)))
expect_equal(sum(llcont(zi3)), as.numeric(logLik(zi3)))
expect_equal(sum(llcont(zi4)), as.numeric(logLik(zi4)))
}
})
test_that("lm object", {
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm1 <- lm(weight ~ group)
expect_equal(sum(llcont(lm1)), as.numeric(logLik(lm1)))
})
test_that("mlogit object", {
if (isTRUE(require("mlogit")) & isTRUE(require("AER"))) {
data("Fishing", package = "mlogit")
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide",
choice = "mode")
mlog1 <- mlogit(mode ~ price + catch, data = Fish)
mlog2 <- mlogit(mode ~ 0 | income, data = Fish)
mlog3 <- mlogit(mode ~ price+ catch | income, data = Fish)
mlog4 <- mlogit(mode ~ price+ catch | income, data = Fish,
reflevel = "charter")
mlog5 <- mlogit(mode ~ price+ catch | income, data = Fish,
alt.subset = c("charter", "pier", "beach"))
Fishing2 <- Fishing
Fishing2[1, "price.pier"] <- Fishing2[3, "price.beach"] <- NA
mlog6 <- mlogit(mode~price+catch|income, Fishing2, shape="wide",
choice="mode", varying = 2:9)
data("TravelMode", package = "AER")
Tr2 <- TravelMode[-c(2, 7, 9),]
mlog7 <- mlogit(choice~wait+gcost|income+size, Tr2, shape = "long",
chid.var = "individual", alt.var="mode", choice = "choice")
data("TravelMode", package = "AER")
mlog8 <- mlogit(choice ~ wait + travel + vcost, TravelMode,
shape = "long", chid.var = "individual",
alt.var = "mode",
method = "bfgs", heterosc = TRUE, tol = 10)
data("Game", package = "mlogit")
mlog10 <- mlogit(ch~own|hours, Game, choice='ch', varying = 1:12,
ranked=TRUE, shape="wide", reflevel="PC")
expect_equal(sum(llcont(mlog1)), as.numeric(logLik(mlog1)))
expect_equal(sum(llcont(mlog2)), as.numeric(logLik(mlog2)))
expect_equal(sum(llcont(mlog3)), as.numeric(logLik(mlog3)))
expect_equal(sum(llcont(mlog4)), as.numeric(logLik(mlog4)))
expect_equal(sum(llcont(mlog5)), as.numeric(logLik(mlog5)))
expect_equal(sum(llcont(mlog6)), as.numeric(logLik(mlog6)))
expect_equal(sum(llcont(mlog7)), as.numeric(logLik(mlog7)))
expect_equal(sum(llcont(mlog8)), as.numeric(logLik(mlog8)))
expect_equal(sum(llcont(mlog10)), as.numeric(logLik(mlog10)))
}
})
test_that("nls object", {
DNase1 <- subset(DNase, Run == 1)
nls1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1)
nls2 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(xmid = 0, scal = 1),
algorithm = "plinear")
nls3 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(Asym = 3, xmid = 0, scal = 1))
nls4 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(Asym = 3, xmid = 0, scal = 1),
algorithm = "port")
Treated <- Puromycin[Puromycin$state == "treated", ]
weighted.MM <- function(resp, conc, Vm, K) {
pred <- (Vm * conc)/(K + conc)
(resp - pred) / sqrt(pred)
}
nls5 <- nls( ~ weighted.MM(rate, conc, Vm, K), data = Treated,
start = list(Vm = 200, K = 0.1))
lisTreat <- with(Treated,
list(conc1 = conc[1], conc.1 = conc[-1], rate = rate))
weighted.MM1 <- function(resp, conc1, conc.1, Vm, K) {
conc <- c(conc1, conc.1)
pred <- (Vm * conc)/(K + conc)
(resp - pred) / sqrt(pred)
}
nls6 <- nls( ~ weighted.MM1(rate, conc1, conc.1, Vm, K),
data = lisTreat, start = list(Vm = 200, K = 0.1))
weighted.MM.grad <- function(resp, conc1, conc.1, Vm, K) {
conc <- c(conc1, conc.1)
K.conc <- K+conc
dy.dV <- conc/K.conc
dy.dK <- -Vm*dy.dV/K.conc
pred <- Vm*dy.dV
pred.5 <- sqrt(pred)
dev <- (resp - pred) / pred.5
Ddev <- -0.5*(resp+pred)/(pred.5*pred)
attr(dev, "gradient") <- Ddev * cbind(Vm = dy.dV, K = dy.dK)
dev
}
nls7 <- nls( ~ weighted.MM.grad(rate, conc1, conc.1, Vm, K),
data = lisTreat, start = list(Vm = 200, K = 0.1))
if(isTRUE(require("MASS"))){
utils::data(muscle, package = "MASS")
nls9 <- nls(Length ~ cbind(1, exp(-Conc/th)), muscle,
start = list(th = 1), algorithm = "plinear")
b <- coef(nls9)
nls10 <- nls(Length ~ a[Strip] + b[Strip]*exp(-Conc/th), muscle,
start = list(a = rep(b[2], 21), b = rep(b[3], 21),
th = b[1]))
expect_equal(sum(llcont(nls9)), as.numeric(logLik(nls9)))
expect_equal(sum(llcont(nls10)), as.numeric(logLik(nls10)))
}
expect_equal(sum(llcont(nls1)), as.numeric(logLik(nls1)))
expect_equal(sum(llcont(nls2)), as.numeric(logLik(nls2)))
expect_equal(sum(llcont(nls3)), as.numeric(logLik(nls3)))
expect_equal(sum(llcont(nls4)), as.numeric(logLik(nls4)))
expect_equal(sum(llcont(nls5)), as.numeric(logLik(nls5)))
expect_equal(sum(llcont(nls6)), as.numeric(logLik(nls6)))
expect_equal(sum(llcont(nls7)), as.numeric(logLik(nls7)))
})
test_that("polr object", {
if (isTRUE(require("MASS"))) {
options(contrasts = c("contr.treatment", "contr.poly"))
polr1 <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
polr2 <- update(polr1, method = "probit", Hess = TRUE)
polr3 <- update(polr1, method = "loglog", Hess = TRUE)
polr4 <- update(polr1, method = "cloglog", Hess = TRUE)
expect_equal(sum(llcont(polr1)), as.numeric(logLik(polr1)))
expect_equal(sum(llcont(polr2)), as.numeric(logLik(polr2)))
expect_equal(sum(llcont(polr3)), as.numeric(logLik(polr3)))
expect_equal(sum(llcont(polr4)), as.numeric(logLik(polr4)))
}
})
test_that("rlm object", {
if (isTRUE(require("MASS"))) {
rlm1 <- rlm(stack.loss ~ ., stackloss)
rlm2 <- rlm(stack.loss ~ ., stackloss, psi = psi.hampel, init = "lts")
rlm3 <- rlm(stack.loss ~ ., stackloss, psi = psi.bisquare)
expect_equal(sum(llcont(rlm1)), as.numeric(logLik(rlm1)))
expect_equal(sum(llcont(rlm2)), as.numeric(logLik(rlm2)))
expect_equal(sum(llcont(rlm3)), as.numeric(logLik(rlm3)))
}
}) |
library(hamcrest)
expected <- structure(list(breaks = c(0x1.8p+0, 0x1p+1, 0x1.4p+1, 0x1.8p+1,
0x1.cp+1, 0x1p+2, 0x1.2p+2, 0x1.4p+2, 0x1.6p+2), counts = c(51L,
41L, 5L, 7L, 30L, 73L, 61L, 4L), density = c(0x1.8p-2, 0x1.34b4b4b4b4b4bp-2,
0x1.2d2d2d2d2d2d3p-5, 0x1.a5a5a5a5a5a5ap-5, 0x1.c3c3c3c3c3c3cp-3,
0x1.12d2d2d2d2d2dp-1, 0x1.cb4b4b4b4b4b5p-2, 0x1.e1e1e1e1e1e1ep-6
), mids = c(0x1.cp+0, 0x1.2p+1, 0x1.6p+1, 0x1.ap+1, 0x1.ep+1,
0x1.1p+2, 0x1.3p+2, 0x1.5p+2), xname = "c(3.6, 1.8, 3.333, 2.283, 4.533, 2.883, 4.7, 3.6, 1.95, 4.35, 1.833, 3.917, 4.2, 1.75, 4.7, 2.167, 1.75, 4.8, 1.6, 4.25, 1.8, 1.75, 3.45, 3.067, 4.533, 3.6, 1.967, 4.083, 3.85, 4.433, 4.3, 4.467, 3.367, 4.033, 3.833, 2.017, 1.867, 4.833, 1.833, 4.783, 4.35, 1.883, 4.567, 1.75, 4.533, 3.317, 3.833, 2.1, 4.633, 2, 4.8, 4.716, 1.833, 4.833, 1.733, 4.883, 3.717, 1.667, 4.567, 4.317, 2.233, 4.5, 1.75, 4.8, 1.817, 4.4, 4.167, 4.7, 2.067, 4.7, 4.033, 1.967, 4.5, 4, 1.983, 5.067, 2.017, 4.567, 3.883, 3.6, \n 4.133, 4.333, 4.1, 2.633, 4.067, 4.933, 3.95, 4.517, 2.167, 4, 2.2, 4.333, 1.867, 4.817, 1.833, 4.3, 4.667, 3.75, 1.867, 4.9, 2.483, 4.367, 2.1, 4.5, 4.05, 1.867, 4.7, 1.783, 4.85, 3.683, 4.733, 2.3, 4.9, 4.417, 1.7, 4.633, 2.317, 4.6, 1.817, 4.417, 2.617, 4.067, 4.25, 1.967, 4.6, 3.767, 1.917, 4.5, 2.267, 4.65, 1.867, 4.167, 2.8, 4.333, 1.833, 4.383, 1.883, 4.933, 2.033, 3.733, 4.233, 2.233, 4.533, 4.817, 4.333, 1.983, 4.633, 2.017, 5.1, 1.8, 5.033, 4, 2.4, 4.6, 3.567, 4, 4.5, 4.083, 1.8, 3.967, \n 2.2, 4.15, 2, 3.833, 3.5, 4.583, 2.367, 5, 1.933, 4.617, 1.917, 2.083, 4.583, 3.333, 4.167, 4.333, 4.5, 2.417, 4, 4.167, 1.883, 4.583, 4.25, 3.767, 2.033, 4.433, 4.083, 1.833, 4.417, 2.183, 4.8, 1.833, 4.8, 4.1, 3.966, 4.233, 3.5, 4.366, 2.25, 4.667, 2.1, 4.35, 4.133, 1.867, 4.6, 1.783, 4.367, 3.85, 1.933, 4.5, 2.383, 4.7, 1.867, 3.833, 3.417, 4.233, 2.4, 4.8, 2, 4.15, 1.867, 4.267, 1.75, 4.483, 4, 4.117, 4.083, 4.267, 3.917, 4.55, 4.083, 2.417, 4.183, 2.217, 4.45, 1.883, 1.85, 4.283, 3.95, 2.333, \n 4.15, 2.35, 4.933, 2.9, 4.583, 3.833, 2.083, 4.367, 2.133, 4.35, 2.2, 4.45, 3.567, 4.5, 4.15, 3.817, 3.917, 4.45, 2, 4.283, 4.767, 4.533, 1.85, 4.25, 1.983, 2.25, 4.75, 4.117, 2.15, 4.417, 1.817, 4.467)",
equidist = TRUE), .Names = c("breaks", "counts", "density",
"mids", "xname", "equidist"), class = "histogram")
assertThat(graphics:::hist.default(plot=FALSE,right=FALSE,x=c(3.6, 1.8, 3.333, 2.283, 4.533, 2.883, 4.7, 3.6, 1.95, 4.35,
1.833, 3.917, 4.2, 1.75, 4.7, 2.167, 1.75, 4.8, 1.6, 4.25, 1.8,
1.75, 3.45, 3.067, 4.533, 3.6, 1.967, 4.083, 3.85, 4.433, 4.3,
4.467, 3.367, 4.033, 3.833, 2.017, 1.867, 4.833, 1.833, 4.783,
4.35, 1.883, 4.567, 1.75, 4.533, 3.317, 3.833, 2.1, 4.633, 2,
4.8, 4.716, 1.833, 4.833, 1.733, 4.883, 3.717, 1.667, 4.567,
4.317, 2.233, 4.5, 1.75, 4.8, 1.817, 4.4, 4.167, 4.7, 2.067,
4.7, 4.033, 1.967, 4.5, 4, 1.983, 5.067, 2.017, 4.567, 3.883,
3.6, 4.133, 4.333, 4.1, 2.633, 4.067, 4.933, 3.95, 4.517, 2.167,
4, 2.2, 4.333, 1.867, 4.817, 1.833, 4.3, 4.667, 3.75, 1.867,
4.9, 2.483, 4.367, 2.1, 4.5, 4.05, 1.867, 4.7, 1.783, 4.85, 3.683,
4.733, 2.3, 4.9, 4.417, 1.7, 4.633, 2.317, 4.6, 1.817, 4.417,
2.617, 4.067, 4.25, 1.967, 4.6, 3.767, 1.917, 4.5, 2.267, 4.65,
1.867, 4.167, 2.8, 4.333, 1.833, 4.383, 1.883, 4.933, 2.033,
3.733, 4.233, 2.233, 4.533, 4.817, 4.333, 1.983, 4.633, 2.017,
5.1, 1.8, 5.033, 4, 2.4, 4.6, 3.567, 4, 4.5, 4.083, 1.8, 3.967,
2.2, 4.15, 2, 3.833, 3.5, 4.583, 2.367, 5, 1.933, 4.617, 1.917,
2.083, 4.583, 3.333, 4.167, 4.333, 4.5, 2.417, 4, 4.167, 1.883,
4.583, 4.25, 3.767, 2.033, 4.433, 4.083, 1.833, 4.417, 2.183,
4.8, 1.833, 4.8, 4.1, 3.966, 4.233, 3.5, 4.366, 2.25, 4.667,
2.1, 4.35, 4.133, 1.867, 4.6, 1.783, 4.367, 3.85, 1.933, 4.5,
2.383, 4.7, 1.867, 3.833, 3.417, 4.233, 2.4, 4.8, 2, 4.15, 1.867,
4.267, 1.75, 4.483, 4, 4.117, 4.083, 4.267, 3.917, 4.55, 4.083,
2.417, 4.183, 2.217, 4.45, 1.883, 1.85, 4.283, 3.95, 2.333, 4.15,
2.35, 4.933, 2.9, 4.583, 3.833, 2.083, 4.367, 2.133, 4.35, 2.2,
4.45, 3.567, 4.5, 4.15, 3.817, 3.917, 4.45, 2, 4.283, 4.767,
4.533, 1.85, 4.25, 1.983, 2.25, 4.75, 4.117, 2.15, 4.417, 1.817,
4.467))[-5]
, identicalTo( expected[-5] ) ) |
WACSdata=function(data,
mapping=NULL,
bounds=NULL,
from = NULL,
to = NULL,
skip=NULL,
Trange=FALSE,
seasons = c("03-01", "06-01", "09-01","12-01"))
{
for (toskip in skip){
data[[toskip]] <- NULL
}
mapping = wacs.buildMapping(mapping_user=mapping, names(data),Trange);
data = wacs.renameData(data, mapping)
if (! all(data$tmin <= data$tmax)) {
stop ("[WACSdata] found tmin > tmax");
}
if (! all(data$rain >= 0)) {
stop ("[WACSdata] found rain < 0");
}
l = length(seasons);
seasonsRes = data.frame(month=rep(0,l), day=rep(0,l))
for (s in 1:l) {
tmp = strsplit(seasons[s],"-")[[1]]
seasonsRes$month[s] = as.numeric(tmp[1]);
seasonsRes$day[s] = as.numeric(tmp[2]);
}
seasons=seasonsRes;
data$season = unlist(lapply(1:nrow(data), function(x){
return(wacs.season(data$month[x], data$day[x], seasons));
}))
data = wacs.reorderData(data,mapping,Trange);
sel = wacs.selectDates(data, from, to)
data = data[sel,]
bounds = wacs.getBounds(data, bounds, mapping);
data = data[-(which(data$month == 2 & data$day == 29)), ]
data$rain[data$rain <0.05] = 0
res = list(data=data, mapping=mapping,
bounds = bounds, seasons=seasons, Trange=Trange)
class(res) = "WACSdata"
return(res)
} |
ITPimage <-
function(ITP.result,alpha=0.05,abscissa.range=c(0,1),nlevel=20){
if(ITP.result$basis=='paFourier' & ITP.result$test=='2pop'){
par(ask=T)
p <- dim(ITP.result$heatmap.matrix_phase)[1]
min.ascissa <- 1-(p-1)/2
max.ascissa <- p+(p-1)/2
ascissa.grafico <- seq(min.ascissa,max.ascissa,length.out=p*4)
ordinata.grafico <- 1:p
colori=rainbow(nlevel,start=0.15,end=0.67)
colori <- colori[length(colori):1]
layout(rbind(1:2,c(3,0),c(4,0)),widths=c(8,1),heights=c(2,1,1))
par(mar=c(4.1, 4.1, 3, .2),cex.main=1.5,cex.lab=1.1,las=0)
matrice.quad <- ITP.result$heatmap.matrix_phase[,(p+1):(3*p)]
ascissa.quad <- ascissa.grafico[(p+1):(3*p)]
image(ascissa.quad,ordinata.grafico,t(matrice.quad[p:1,]),col=colori,ylab='Interval length',main='p-value heatmap (phase)',xlab='Abscissa',zlim=c(0,1),asp=1)
min.plot <- par("usr")[1]
max.plot <- par("usr")[2]
par(mar=c(4.1, 1, 3, 3),las=1)
image(1,seq(0,1,length.out=nlevel)-0.025*seq(0,1,length.out=nlevel)+0.025*seq(1,0,length.out=nlevel),t(as.matrix(seq(0,1,length.out=nlevel))),col=colori,xaxt='n',yaxt='n',xlab='',ylab='')
axis(4,at=seq(0,1,0.2),padj=0.4)
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
par(mar=c(4.1, 4.1, 3, .2),las=0)
plot(1:p,ITP.result$corrected.pval_phase,pch=16,ylim=c(0,1),xlim=c(min.plot,max.plot),main='Corrected p-values (phase)',ylab='p-value',xlab='Component',xaxs='i')
difference <- which(ITP.result$corrected.pval_phase<alpha)
abscissa.pval <- 1:p
if(length(difference)>0){
for(j in 1:length(difference)){
min.rect <- abscissa.pval[difference[j]] - 0.5
max.rect <- min.rect + 1
rect(min.rect, par("usr")[3], max.rect, par("usr")[4], col = 'gray90',density=-2,border = NA)
}
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
}
for(j in 0:10){
abline(h=j/10,col='lightgray',lty="dotted")
}
points(abscissa.pval,ITP.result$corrected.pval_phase,pch=16)
abscissa.new <- seq(abscissa.range[1],abscissa.range[2],length.out=dim(ITP.result$data.eval)[2])
matplot(abscissa.new,t(ITP.result$data.eval),col=ITP.result$labels,type='l',main='Functional data',xlab='Abscissa',ylab='Value',xaxs='i')
layout(rbind(1:2,c(3,0),c(4,0)),widths=c(8,1),heights=c(2,1,1))
par(mar=c(4.1, 4.1, 3, .2),cex.main=1.5,cex.lab=1.1,las=0)
matrice.quad <- ITP.result$heatmap.matrix_amplitude[,(p+1):(3*p)]
ascissa.quad <- ascissa.grafico[(p+1):(3*p)]
image(ascissa.quad,ordinata.grafico,t(matrice.quad[p:1,]),col=colori,ylab='Interval length',main='p-value heatmap (amplitude)',xlab='Abscissa',zlim=c(0,1),asp=1)
min.plot <- par("usr")[1]
max.plot <- par("usr")[2]
par(mar=c(4.1, 1, 3, 3),las=1)
image(1,seq(0,1,length.out=nlevel)-0.025*seq(0,1,length.out=nlevel)+0.025*seq(1,0,length.out=nlevel),t(as.matrix(seq(0,1,length.out=nlevel))),col=colori,xaxt='n',yaxt='n',xlab='',ylab='')
axis(4,at=seq(0,1,0.2),padj=0.4)
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
par(mar=c(4.1, 4.1, 3, .2),las=0)
plot(1:p,ITP.result$corrected.pval_amplitude,pch=16,ylim=c(0,1),xlim=c(min.plot,max.plot),main='Corrected p-values (amplitude)',ylab='p-value',xlab='Component',xaxs='i')
difference <- which(ITP.result$corrected.pval_amplitude<alpha)
abscissa.pval <- 1:p
if(length(difference)>0){
for(j in 1:length(difference)){
min.rect <- abscissa.pval[difference[j]] - 0.5
max.rect <- min.rect + 1
rect(min.rect, par("usr")[3], max.rect, par("usr")[4], col = 'gray90',density=-2,border = NA)
}
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
}
for(j in 0:10){
abline(h=j/10,col='lightgray',lty="dotted")
}
points(1:p,ITP.result$corrected.pval_amplitude,pch=16)
abscissa.new <- seq(abscissa.range[1],abscissa.range[2],length.out=dim(ITP.result$data.eval)[2])
matplot(abscissa.new,t(ITP.result$data.eval),col=ITP.result$labels,type='l',main='Functional data',xlab='Abscissa',ylab='Value',xaxs='i')
par(ask=FALSE)
}else if(ITP.result$basis=='Fourier'){
p <- dim(ITP.result$heatmap.matrix)[1]
min.ascissa <- 1-(p-1)/2
max.ascissa <- p+(p-1)/2
ascissa.grafico <- seq(min.ascissa,max.ascissa,length.out=p*4)
ordinata.grafico <- 1:p
colori=rainbow(nlevel,start=0.15,end=0.67)
colori <- colori[length(colori):1]
layout(rbind(1:2,c(3,0),c(4,0)),widths=c(8,1),heights=c(2,1,1))
par(mar=c(4.1, 4.1, 3, .2),cex.main=1.5,cex.lab=1.1,las=0)
matrice.quad <- ITP.result$heatmap.matrix[,(p+1):(3*p)]
ascissa.quad <- ascissa.grafico[(p+1):(3*p)]
image(ascissa.quad,ordinata.grafico,t(matrice.quad[p:1,]),col=colori,ylab='Interval length',main='p-value heatmap',xlab='Abscissa',zlim=c(0,1),asp=1)
min.plot <- par("usr")[1]
max.plot <- par("usr")[2]
par(mar=c(4.1, 1, 3, 3),las=1)
image(1,seq(0,1,length.out=nlevel)-0.025*seq(0,1,length.out=nlevel)+0.025*seq(1,0,length.out=nlevel),t(as.matrix(seq(0,1,length.out=nlevel))),col=colori,xaxt='n',yaxt='n',xlab='',ylab='')
axis(4,at=seq(0,1,0.2),padj=0.4)
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
par(mar=c(4.1, 4.1, 3, .2),las=0)
plot(1:p,ITP.result$corrected.pval,pch=16,ylim=c(0,1),xlim=c(min.plot,max.plot),main='Corrected p-values',ylab='p-value',xlab='Component',xaxs='i')
difference <- which(ITP.result$corrected.pval<alpha)
abscissa.pval <- 1:p
if(length(difference)>0){
for(j in 1:length(difference)){
min.rect <- abscissa.pval[difference[j]] - 0.5
max.rect <- min.rect + 1
rect(min.rect, par("usr")[3], max.rect, par("usr")[4], col = 'gray90',density=-2,border = NA)
}
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
}
for(j in 0:10){
abline(h=j/10,col='lightgray',lty="dotted")
}
points(1:p,ITP.result$corrected.pval,pch=16)
abscissa.new <- seq(abscissa.range[1],abscissa.range[2],length.out=dim(ITP.result$data.eval)[2])
matplot(abscissa.new,t(ITP.result$data.eval),col=ITP.result$labels,type='l',main='Functional data',xlab='Abscissa',ylab='Value',xaxs='i')
if(ITP.result$test=='1pop'){
if(length(ITP.result$mu)==1){
abscissa.mu <- abscissa.new
mu <- rep(ITP.result$mu,1000)
}else{
abscissa.mu <- seq(abscissa.range[1],abscissa.range[2],length.out=length(ITP.result$mu))
mu <- ITP.result$mu
}
lines(abscissa.mu,mu,col='gray')
}
}else if(ITP.result$basis=='B-spline'){
min.ascissa <- abscissa.range[1]-(abscissa.range[2]-abscissa.range[1])/2
max.ascissa <- abscissa.range[2]+(abscissa.range[2]-abscissa.range[1])/2
p <- dim(ITP.result$heatmap.matrix)[1]
ordinata.grafico <- seq(abscissa.range[1],abscissa.range[2],length.out=p) - abscissa.range[1]
colori=rainbow(nlevel,start=0.15,end=0.67)
colori <- colori[length(colori):1]
layout(rbind(1:2,c(3,0),c(4,0)),widths=c(8,1),heights=c(2,1,1))
par(mar=c(4.1, 4.1, 3, .2),cex.main=1.5,cex.lab=1.1,las=0)
matrice.quad <- ITP.result$heatmap.matrix[,(p+1):(3*p)]
ascissa.quad <- seq(abscissa.range[1],abscissa.range[2],length.out=p*2)
image(ascissa.quad,ordinata.grafico,t(matrice.quad[p:1,]),col=colori,ylab='Interval length',main='p-value heatmap',xlab='Abscissa',zlim=c(0,1),asp=1)
min.plot <- par("usr")[1]
max.plot <- par("usr")[2]
par(mar=c(4.1, 1, 3, 3),las=1)
image(1,seq(0,1,length.out=nlevel)-0.025*seq(0,1,length.out=nlevel)+0.025*seq(1,0,length.out=nlevel),t(as.matrix(seq(0,1,length.out=nlevel))),col=colori,xaxt='n',yaxt='n',xlab='',ylab='')
axis(4,at=seq(0,1,0.2),padj=0.4)
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
par(mar=c(4.1, 4.1, 3, .2),las=0)
abscissa.pval <- seq(abscissa.range[1],abscissa.range[2],length.out=p)
plot(abscissa.pval,ITP.result$corrected.pval,pch=16,ylim=c(0,1),xlim=c(min.plot,max.plot),main='Corrected p-values',ylab='p-value',xlab='Component',xaxs='i')
difference <- which(ITP.result$corrected.pval<alpha)
if(length(difference) >0){
for(j in 1:length(difference)){
min.rect <- abscissa.pval[difference[j]] - (abscissa.pval[2]-abscissa.pval[1])/2
max.rect <- min.rect + (abscissa.pval[2]-abscissa.pval[1])
rect(min.rect, par("usr")[3], max.rect, par("usr")[4], col = 'gray90',density=-2,border = NA)
}
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
}
for(j in 0:10){
abline(h=j/10,col='lightgray',lty="dotted")
}
points(abscissa.pval,ITP.result$corrected.pval,pch=16)
abscissa.new <- seq(abscissa.range[1],abscissa.range[2],length.out=dim(ITP.result$data.eval)[2])
matplot(abscissa.new,t(ITP.result$data.eval),col=ITP.result$labels,type='l',xlim=c(min.plot,max.plot),main='Functional data',xlab='Abscissa',ylab='Value',xaxs='i')
if(length(difference) >0){
for(j in 1:length(difference)){
min.rect <- abscissa.pval[difference[j]] - (abscissa.pval[2]-abscissa.pval[1])/2
max.rect <- min.rect + (abscissa.pval[2]-abscissa.pval[1])
rect(min.rect, par("usr")[3], max.rect, par("usr")[4], col = 'gray90',density=-2,border = NA)
}
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = NULL,border='black')
}
matplot(abscissa.new,t(ITP.result$data.eval),col=ITP.result$labels,type='l',add=TRUE)
if(ITP.result$test=='1pop'){
if(length(ITP.result$mu)==1){
abscissa.mu <- abscissa.new
mu <- rep(ITP.result$mu,1000)
}else{
abscissa.mu <- seq(abscissa.range[1],abscissa.range[2],length.out=length(ITP.result$mu))
mu <- ITP.result$mu
}
lines(abscissa.mu,mu,col='blue')
}
}
} |
llm <- function(X,Y,threshold_pruning=0.25 , nbr_obs_leaf=100) {
if (threshold_pruning < 0 | threshold_pruning> 1){
stop("Enter a valid threshold for pruning value [0,1] (threshold_pruning)")
}
if (nbr_obs_leaf < 1){
stop("Enter a valid mimimum number of observations per leaf (minimum =1) (nbr_obs_leaf)")
}
if (nrow(X) != length(Y)){
stop("The number of instances in (X) and (Y) should be the same")
}
if (nrow(X) == 0 | length(Y)== 0){
stop("There are no instances in (X) or (Y)")
}
if (length(which(sapply(X, is.numeric)==FALSE))>0){
stop("All variables in the dataframe (X) should be numeric")
}
if (nlevels(Y) != 2){
stop("Only binary classification is supported at the moment")
}
.list.rules.party <- function(x, i = NULL, ...) {
if (is.null(i)) i <- nodeids(x, terminal = TRUE)
if (length(i) > 1) {
ret <- sapply(i, .list.rules.party, x = x)
names(ret) <- if (is.character(i)) i else names(x)[i]
return(ret)
}
if (is.character(i) && !is.null(names(x)))
i <- which(names(x) %in% i)
stopifnot(length(i) == 1 & is.numeric(i))
stopifnot(i <= length(x) & i >= 1)
i <- as.integer(i)
dat <- data_party(x, i)
if (!is.null(x$fitted)) {
findx <- which("(fitted)" == names(dat))[1]
fit <- dat[,findx:ncol(dat), drop = FALSE]
dat <- dat[,-(findx:ncol(dat)), drop = FALSE]
if (ncol(dat) == 0)
dat <- x$data
} else {
fit <- NULL
dat <- x$data
}
rule <- c()
recFun <- function(node) {
if (id_node(node) == i) return(NULL)
kid <- sapply(kids_node(node), id_node)
whichkid <- max(which(kid <= i))
split <- split_node(node)
ivar <- varid_split(split)
svar <- names(dat)[ivar]
index <- index_split(split)
if (is.factor(dat[, svar])) {
if (is.null(index))
index <- ((1:nlevels(dat[, svar])) > breaks_split(split)) + 1
slevels <- levels(dat[, svar])[index == whichkid]
srule <- paste(svar, " %in% c(\"",
paste(slevels, collapse = "\", \"", sep = ""), "\")",
sep = "")
} else {
if (is.null(index)) index <- 1:length(kid)
breaks <- cbind(c(-Inf, breaks_split(split)),
c(breaks_split(split), Inf))
sbreak <- breaks[index == whichkid,]
right <- right_split(split)
srule <- c()
if (is.finite(sbreak[1]))
srule <- c(srule,
paste(svar, ifelse(right, ">", ">="), sbreak[1]))
if (is.finite(sbreak[2]))
srule <- c(srule,
paste(svar, ifelse(right, "<=", "<"), sbreak[2]))
srule <- paste(srule, collapse = " & ")
}
rule <<- c(rule, srule)
return(recFun(node[[whichkid]]))
}
node <- recFun(node_party(x))
paste(rule, collapse = " & ")
}
m1 <- RWeka::J48(as.factor(as.character(Y)) ~ .,
data = X,
control = RWeka::Weka_control(M = nbr_obs_leaf, C= threshold_pruning))
Pm1 = partykit::as.party(m1)
Pm1_rules = .list.rules.party(Pm1)
TrainPred = stats::predict(Pm1, newdata=X, type="node")
listythelist <- vector("list",length(Pm1_rules))
listythelist2 <- vector("list",length(Pm1_rules))
listythelist3 <- vector("list",length(Pm1_rules))
aa <- as.numeric()
for (l in 1:length(Pm1_rules)) {
train_ss <- X[which(TrainPred==names(Pm1_rules)[l]), ]
y_sel <- Y[which(TrainPred==names(Pm1_rules)[l])]
LR <- stats::glm(y_sel ~ ., data=train_ss, family=stats::binomial("logit"))
LR1 <- stats::glm(y_sel ~ 1, data=train_ss, family=stats::binomial("logit"))
listythelist[[l]] <- stats::step(LR1,direction="forward" ,scope = list(lower= LR1, upper = LR), trace = 0)
listythelist2[[l]] <- nrow(train_ss)
listythelist3[[l]] <- 1 - (table(y_sel)[1]/ length(y_sel))
}
myreturn <- list()
myreturn[[1]] <- Pm1_rules
myreturn[[2]] <- listythelist
myreturn[[3]] <- m1
myreturn[[4]] <- listythelist2
myreturn[[5]] <- listythelist3
class(myreturn) <- "logitleafmodel"
names(myreturn)[[1]] <- "Segment Rules"
names(myreturn)[[2]] <- "Coefficients"
names(myreturn)[[3]] <- "Full decision tree for segmentation"
names(myreturn)[[4]] <- "Observations per segment"
names(myreturn)[[5]] <- "Incidence of dependent per segment"
return(myreturn)
} |
threshold_apply = function(threshold = 0.5, roi_name = "test", video_path = 'image826.avi',radians = 0.217604550320612,xlength = 60,ylength = 242,xstart = 696,ystart = 323, image_list = NULL, fps = NULL)
{
starttime = Sys.time()
output_folder = output_dir(dirname(video_path), use_default = TRUE)
scratch_dir(wipe_scratch = TRUE)
scratch = scratch_dir(file_name = video_path)
unlink(gsub("/$", "", scratch), recursive = TRUE, force = TRUE)
dir.create(scratch)
filter_string = paste("rotate = '",radians,":out_w=rotw(",radians,"):out_h=roth(",radians,"):c = red',",
"crop=",xlength,":",ylength,":",xstart,":",ystart,"",
sep = "")
if(!is.null(fps))
{
filter_string = paste("fps = ",fps,",",filter_string, sep = "")
}
av::av_encode_video(video_path, paste(scratch, "/%03d_raw.png", sep = ""), vfilter = filter_string, codec = "png")
cropped_file_list = list.files(scratch, full.names = TRUE, pattern = "\\_raw.png$")
starttime = Sys.time()
options(future.rng.onMisuse = "ignore")
load.image(cropped_file_list[[1]]) %>% plot()
message("Making segmentation")
bundlesize = length(cropped_file_list)/as.numeric(availableCores())
split_file_list = split(cropped_file_list, ceiling(seq_along(cropped_file_list)/bundlesize))
foreach(locallist = split_file_list) %dopar%
{
for(current_frame in locallist)
{
current_frame_threshold = threshold_image(current_frame, threshold)
imager::save.image(current_frame_threshold[[1]], current_frame %>% str_replace("_raw.png", "_threshold.png"))
current_frame_threshold[[2]]$filename = current_frame_threshold[[2]]$filename %>% str_replace("_raw", "")
utils::write.csv(current_frame_threshold[[2]], current_frame %>% str_replace("_raw.png", "_width.csv"))
current_frame_spread = image_intensity_spread(current_frame)
current_frame_spread$frame = current_frame_spread$frame %>% str_replace("_raw", "")
utils::write.csv(current_frame_spread, current_frame %>% str_replace("_raw.png", "_profile.csv"))
}
}
message("Copying results")
output_file_base = paste(output_folder, "/", basename(file_path_sans_ext(video_path)), "_", roi_name, "_", sep = "")
file_list = list.files(scratch, full.names = TRUE, pattern = "_raw.png$")
av::av_encode_video(file_list, output = paste(output_file_base, "raw.avi", sep = ""),codec = "libx264", verbose = 24)
file_list = list.files(scratch, full.names = TRUE, pattern = "_threshold.png$")
load.image(file_list[[1]]) %>% plot()
av::av_encode_video(file_list, output = paste(output_file_base, "threshold.avi", sep = ""),codec = "libx264", verbose = 24)
file_list = list.files(scratch, full.names = TRUE, pattern = "_width.csv$")
all_csv = lapply(file_list, read.csv) %>% bind_rows()
all_csv = all_csv %>% mutate(X = NULL, y_position = y, y = NULL,
y_position_excluded = excluded, excluded = NULL,
frame = filename, filename = NULL,
roi_name = roi_name)
write.csv(all_csv, paste(output_file_base, "width.csv"))
file_list = list.files(scratch, full.names = TRUE, pattern = "_profile.csv$")
all_csv = lapply(file_list, read.csv) %>% bind_rows()
all_csv = all_csv %>% mutate(X = NULL,
x_position = x, x = NULL,
roi_name = roi_name)
write.csv(all_csv, paste(output_file_base, "profile.csv", sep = ""))
unlink(gsub("/$", "", scratch), recursive = TRUE, force = TRUE)
print(Sys.time() - starttime)
} |
quilt_form <- function(input_data,
page_break_every = 0,
question_type,
filename) {
if(question_type=="dropdown"){
question_type = "[[Question:MC:Dropdown]]"
}
if(question_type=="select"){
question_type = "[[Question:MC:Select]]"
}
if(question_type=="multiselect"){
question_type = "[[Question:MC:MultiSelect]]"
}
if(question_type=="singleanswer"){
question_type = "[[Question:MC:SingleAnswer:Horizontal]]"
}
if(question_type=="multianswer"){
question_type = "[[Question:MC:MultipleAnswer:Horizontal]]"
}
if(question_type=="rankorder"){
question_type = "[[Question:RankOrder]]"
}
if(question_type=="singleline"){
question_type = "[[Question:TextEntry:SingleLine]]"
}
if(question_type=="essay"){
question_type = "[[Question:TextEntry:Essay]]"
}
rowID <- NULL
variable <- NULL
input_data <- addformIDs(input_data)
if(page_break_every != 0) {
input_data <- pbreak(input_data, page_break_every)
} else input_data$rowID = 1:nrow(input_data)
quilted_form <- formpaste(input_data, question_type)
writeLines(quilted_form, filename)
} |
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