code
stringlengths 1
13.8M
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"NewOrl"
"NewOrl_5"
"Artificial_3"
"Western"
"Ned"
"Ned_5"
"UKdat"
"UKdat_5"
|
setMethod("leastFavorableRadius", signature(L2Fam = "L2ParamFamily",
neighbor = "UncondNeighborhood",
risk = "asGRisk"),
function(L2Fam, neighbor, risk, rho, upRad = 1, z.start = NULL,
A.start = NULL, upper = 100, maxiter = 100,
tol = .Machine$double.eps^0.4, warn = FALSE){
if(length(rho) != 1)
stop("'rho' is not of length == 1")
if((rho <= 0)||(rho >= 1))
stop("'rho' not in (0,1)")
L2derivDim <- numberOfMaps(L2Fam@L2deriv)
if(L2derivDim == 1){
leastFavFct.1 <- function(r, L2Fam, neighbor, risk, rho,
upper.b, MaxIter, eps, warn){
loRad <- r*rho
upRad <- r/rho
lower <- ifelse(identical(all.equal(loRad, 0), TRUE), 1e-4, loRad)
upper <- ifelse(upRad == Inf, 10, upRad)
ow <- options("warn")
options(warn = -1)
if(identical(all.equal(loRad, 0), TRUE)){
loRad <- 0
loRisk <- 1/as.vector(L2Fam@FisherInfo)
}else{
neighbor@radius <- loRad
resLo <- getInfRobIC(L2deriv = L2Fam@L2derivDistr[[1]], neighbor = neighbor,
risk = risk, symm = L2Fam@L2derivDistrSymm[[1]],
Finfo = L2Fam@FisherInfo, upper = upper.b,
trafo = L2Fam@param@trafo, maxiter = MaxIter, tol = eps, warn = warn)
loRisk <- getAsRisk(risk = risk, L2deriv = L2Fam@L2derivDistr[[1]],
neighbor = neighbor, clip = resLo$b, cent = resLo$a,
stand = resLo$A, trafo = L2Fam@param@trafo)[[1]]
}
if(upRad == Inf){
bmin <- getAsRisk(risk = asBias(), L2deriv = L2Fam@L2derivDistr[[1]],
neighbor = neighbor, trafo = L2Fam@param@trafo, symm = L2Fam@L2derivSymm[[1]])
upRisk <- bmin^2
}else{
neighbor@radius <- upRad
resUp <- getInfRobIC(L2deriv = L2Fam@L2derivDistr[[1]], neighbor = neighbor,
risk = risk, symm = L2Fam@L2derivDistrSymm[[1]],
Finfo = L2Fam@FisherInfo, upper = upper.b,
trafo = L2Fam@param@trafo, maxiter = MaxIter, tol = eps, warn = warn)
upRisk <- getAsRisk(risk = risk, L2deriv = L2Fam@L2derivDistr[[1]],
neighbor = neighbor, clip = resUp$b, cent = resUp$a,
stand = resUp$A, trafo = L2Fam@param@trafo)[[1]]
}
ineff <- NULL
getIneffDiff.1 <- function(x){
res <- getIneffDiff(x, L2Fam = L2Fam, neighbor = neighbor,
upper.b = upper.b, risk = risk, loRad = loRad, upRad = upRad,
loRisk = loRisk, upRisk = upRisk, eps = .Machine$double.eps^0.25,
MaxIter = MaxIter, warn = warn)
ineff <<- res["ineff"]
return(res["ineffDiff"])
}
leastFavR <- uniroot(getIneffDiff.1, lower = lower, upper = upper,
tol = .Machine$double.eps^0.25)$root
options(ow)
cat("current radius:\t", r, "\tinefficiency:\t", ineff, "\n")
return(ineff)
}
leastFavR <- optimize(leastFavFct.1, lower = 1e-4, upper = upRad,
tol = .Machine$double.eps^0.25, maximum = TRUE,
L2Fam = L2Fam, neighbor = neighbor, risk = risk,
rho = rho, upper.b = upper, MaxIter = maxiter,
eps = tol, warn = warn)
res <- list(rho = rho, leastFavorableRadius = leastFavR$maximum,
ineff = leastFavR$objective)
names(res)[3] <- paste(class(risk)[1], "-inefficiency", sep="")
return(res)
}else{
if(is(L2Fam@distribution, "UnivariateDistribution")){
if((length(L2Fam@L2deriv) == 1) & is(L2Fam@L2deriv[[1]], "RealRandVariable")){
L2deriv <- L2Fam@L2deriv[[1]]
L2derivSymm <- L2Fam@L2derivSymm
L2derivDistrSymm <- L2Fam@L2derivDistrSymm
}else{
L2deriv <- diag(dimension(L2Fam@L2deriv)) %*% L2Fam@L2deriv
L2deriv <- RealRandVariable(Map = L2deriv@Map, Domain = L2deriv@Domain)
nrvalues <- numberOfMaps(L2deriv)
if(numberOfMaps(L2Fam@L2deriv) != nrvalues){
L1 <- vector("list", nrvalues)
L2 <- vector("list", nrvalues)
for(i in 1:nrvalues){
L1[[i]] <- NonSymmetric()
L2[[i]] <- NoSymmetry()
}
L2derivSymm <- new("FunSymmList", L1)
L2derivDistrSymm <- new("DistrSymmList", L2)
}
}
leastFavFct.p <- function(r, L2Fam, neighbor, risk, rho,
z.start, A.start, upper.b, MaxIter, eps, warn){
loRad <- r*rho
upRad <- r/rho
lower <- ifelse(identical(all.equal(loRad, 0), TRUE), 1e-4, loRad)
upper <- ifelse(upRad == Inf, 10, upRad)
ow <- options("warn")
options(warn = -1)
trafo <- L2Fam@param@trafo
if(identical(all.equal(loRad, 0), TRUE)){
loRad <- 0
loRisk <- sum(diag(distr::solve(L2Fam@FisherInfo)))
}else{
neighbor@radius <- loRad
resLo <- getInfRobIC(L2deriv = L2deriv, neighbor = neighbor, risk = risk,
Distr = L2Fam@distribution, DistrSymm = L2Fam@distrSymm,
L2derivSymm = L2derivSymm, L2derivDistrSymm = L2derivDistrSymm,
Finfo = L2Fam@FisherInfo, trafo = trafo, z.start = z.start,
A.start = A.start, upper = upper.b, maxiter = MaxIter,
tol = eps, warn = warn)
loRisk <- getAsRisk(risk = risk, L2deriv = L2deriv, neighbor = neighbor,
clip = resLo$b, cent = resLo$a, stand = resLo$A, trafo = trafo)[[1]]
}
if(upRad == Inf){
bmin <- getAsRisk(risk = asBias(), L2deriv = L2deriv, neighbor = neighbor,
Distr = L2Fam@distribution, L2derivDistrSymm = L2Fam@L2derivDistrSymm,
trafo = trafo, z.start = z.start, A.start = A.start,
maxiter = maxiter, tol = tol)$asBias
upRisk <- bmin^2
}else{
neighbor@radius <- upRad
resUp <- getInfRobIC(L2deriv = L2deriv, neighbor = neighbor, risk = risk,
Distr = L2Fam@distribution, DistrSymm = L2Fam@distrSymm,
L2derivSymm = L2derivSymm, L2derivDistrSymm = L2derivDistrSymm,
Finfo = L2Fam@FisherInfo, trafo = trafo, z.start = z.start,
A.start = A.start, upper = upper.b, maxiter = maxiter,
tol = tol, warn = warn)
upRisk <- getAsRisk(risk = risk, L2deriv = L2deriv, neighbor = neighbor,
clip = resUp$b, cent = resUp$a, stand = resUp$A, trafo = trafo)[[1]]
}
ineff <- NULL
getIneffDiff.p <- function(x){
res <- getIneffDiff(x, L2Fam = L2Fam, neighbor = neighbor,
z.start = z.start, A.start = A.start, upper.b = upper.b, risk = risk,
loRad = loRad, upRad = upRad, loRisk = loRisk, upRisk = upRisk,
eps = .Machine$double.eps^0.25, MaxIter = MaxIter, warn = warn)
ineff <<- res["ineff"]
return(res["ineffDiff"])
}
leastFavR <- uniroot(getIneffDiff.p, lower = lower, upper = upper,
tol = .Machine$double.eps^0.25)$root
options(ow)
cat("current radius:\t", r, "\tinefficiency:\t", ineff, "\n")
return(ineff)
}
if(is.null(z.start)) z.start <- numeric(L2derivDim)
if(is.null(A.start)) A.start <- L2Fam@param@trafo
leastFavR <- optimize(leastFavFct.p, lower = 1e-4, upper = upRad,
tol = .Machine$double.eps^0.25, maximum = TRUE,
L2Fam = L2Fam, neighbor = neighbor, risk = risk,
rho = rho, z.start = z.start, A.start = A.start,
upper.b = upper, MaxIter = maxiter, eps = tol, warn = warn)
res <- list(rho = rho, leastFavorableRadius = leastFavR$maximum,
ineff = leastFavR$objective)
names(res)[3] <- paste(class(risk)[1], "-inefficiency", sep="")
return(res)
}else{
stop("not yet implemented")
}
}
})
|
"gss2010"
|
context("feature_specs")
skip_if_not_tf <- function() {
skip_if_no_tensorflow(required_version = "2.0")
}
df <- list(
a = letters,
b = 1:length(letters),
c = runif(length(letters)),
d = LETTERS,
y = runif(length(letters))
)
dataset <- df %>%
tensor_slices_dataset() %>%
dataset_batch(2)
get_features <- function(df, feature_columns) {
if (tensorflow::tf$executing_eagerly())
example <- reticulate::iter_next(reticulate::as_iterator(df))
else {
example <- make_iterator_one_shot(df)
example <- iterator_get_next(example)
}
k <- keras::layer_dense_features(feature_columns = feature_columns)
if (tensorflow::tf$executing_eagerly())
return(k(example))
else {
res <- k(example)
sess <- tf$Session()
return(sess$run(res))
}
}
test_that("Can create a feature_spec", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d)
expect_equal(sort(spec$feature_names()), sort(names(df)[-which(names(df) == "y")]))
})
test_that("Can create numeric columns", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_numeric_column(b, c)
spec$fit()
expect_length(spec$features(), 2)
expect_named(spec$features(), c("b", "c"))
expect_s3_class(spec$features()[[1]], "tensorflow.python.feature_column.feature_column._DenseColumn")
expect_s3_class(spec$features()[[2]], "tensorflow.python.feature_column.feature_column._DenseColumn")
})
test_that("Can create categorical columns with vocabulary list", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_categorical_column_with_vocabulary_list(a, d)
spec$fit()
expect_length(spec$features(), 2)
expect_named(spec$features(), c("a", "d"))
expect_s3_class(spec$features()[[1]], "tensorflow.python.feature_column.feature_column._CategoricalColumn")
expect_s3_class(spec$features()[[2]], "tensorflow.python.feature_column.feature_column._CategoricalColumn")
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_categorical_column_with_vocabulary_list(a, vocabulary_list = letters[1:5])
spec$fit()
expect_length(spec$features(), 1)
expect_length(spec$dense_features(), 0)
})
test_that("Can create categorical columns with hash_bucket", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_categorical_column_with_hash_bucket(a, d, hash_bucket_size = 10)
spec$fit()
expect_length(spec$features(), 2)
expect_named(spec$features(), c("a", "d"))
expect_s3_class(spec$features()[[1]], "tensorflow.python.feature_column.feature_column._CategoricalColumn")
expect_s3_class(spec$features()[[2]], "tensorflow.python.feature_column.feature_column._CategoricalColumn")
})
test_that("Can create categorical columns with identity", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_categorical_column_with_identity(a, num_buckets = 10)
spec$fit()
expect_length(spec$features(), 1)
expect_named(spec$features(), c("a"))
expect_s3_class(spec$features()[[1]], "tensorflow.python.feature_column.feature_column._CategoricalColumn")
})
test_that("Can create categorical columns with vocabulary file", {
skip_if_not_tf()
tmp <- tempfile()
writeLines(tmp, text = letters)
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_categorical_column_with_vocabulary_file(a, vocabulary_file = tmp)
spec$fit()
expect_length(spec$features(), 1)
expect_named(spec$features(), c("a"))
expect_s3_class(spec$features()[[1]], "tensorflow.python.feature_column.feature_column._CategoricalColumn")
})
test_that("Can create indicator variables", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_indicator_column(a, d)
spec$fit()
expect_length(spec$dense_features(), 2)
expect_named(spec$dense_features(), c("indicator_a", "indicator_d"))
expect_s3_class(spec$dense_features()[[1]], "tensorflow.python.feature_column.feature_column_v2.IndicatorColumn")
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_indicator_column(ind_a = a)
spec$fit()
expect_named(spec$dense_features(), c("ind_a"))
})
test_that("Can create embedding columns", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_embedding_column(a, d, dimension = 5)
spec$fit()
expect_length(spec$dense_features(), 2)
expect_named(spec$dense_features(), c("embedding_a", "embedding_d"))
expect_s3_class(spec$dense_features()[[1]], "tensorflow.python.feature_column.feature_column_v2.EmbeddingColumn")
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_embedding_column(emb_a = a, dimension = 5)
spec$fit()
expect_named(spec$dense_features(), c("emb_a"))
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_embedding_column(a, d)
spec$fit()
expect_length(spec$dense_features(), 2)
expect_named(spec$dense_features(), c("embedding_a", "embedding_d"))
expect_s3_class(spec$dense_features()[[1]], "tensorflow.python.feature_column.feature_column_v2.EmbeddingColumn")
})
test_that("Can create crossed columns", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_crossed_column(c(a, d), hash_bucket_size = 100) %>%
step_indicator_column(crossed_a_d)
spec$fit()
expect_named(spec$dense_features(), "indicator_crossed_a_d")
expect_s3_class(spec$dense_features()[[1]], "tensorflow.python.feature_column.feature_column_v2.IndicatorColumn")
expect_s3_class(spec$features()$crossed_a_d, "tensorflow.python.feature_column.feature_column_v2.CrossedColumn")
})
test_that("Can create bucketized columns", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_numeric_column(b) %>%
step_bucketized_column(b, boundaries = c(5, 10, 15))
spec$fit()
expect_s3_class(spec$dense_features()$bucketized_b, "tensorflow.python.feature_column.feature_column_v2.BucketizedColumn")
})
test_that("Can remove columns", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_numeric_column(b) %>%
step_bucketized_column(b, boundaries = c(5, 10, 15)) %>%
step_remove_column(b)
spec$fit()
expect_length(spec$features(), 1)
})
test_that("Using with layer_dense_features", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_numeric_column(b, c) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_indicator_column(a, d)
spec$fit()
lyr <- keras::layer_dense_features(feature_columns = spec$dense_features())
ds <- reticulate::as_iterator(dataset)
x <- lyr(reticulate::iter_next(ds))
if (tensorflow::tf$executing_eagerly())
expect_equal(x$shape$as_list(), c(2, 2 + 2*26))
else
expect_equal(x$shape$as_list()[[2]], 2 + 2*26)
})
test_that("Recipes are correctly cloned/imutable", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_numeric_column(b, c) %>%
step_categorical_column_with_vocabulary_list(a, d)
spec1 <- spec %>%
step_indicator_column(a, d)
spec2 <- spec %>%
step_indicator_column(a, d)
spec1$fit()
expect_length(spec1$features(), 6)
expect_error(spec2$features())
expect_error(spec$features())
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_numeric_column(b, c) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_indicator_column(a, d)
spec_prep <- fit(spec)
expect_length(spec_prep$features(), 6)
expect_error(spec$features())
})
test_that("Recipes column types", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a+b+c+d) %>%
step_numeric_column(b) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_indicator_column(a, d)
expect_equal(
spec$feature_types(),
c("float32", "string", "string", "float32", "float32", "float32")
)
})
test_that("Fit feature_spec", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(b) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_indicator_column(a, d)
spec_prep <- fit(spec)
expect_error(dataset_use_spec(dataset, spec))
expect_s3_class(dataset_use_spec(dataset, spec_prep), "tensorflow.python.data.ops.dataset_ops.DatasetV2")
})
test_that("Prep with different dataset", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(b) %>%
step_categorical_column_with_vocabulary_list(a, d) %>%
step_indicator_column(a, d)
ds <- df %>%
tensor_slices_dataset() %>%
dataset_take(10)
spec_prep <- fit(spec, ds)
expect_s3_class(dataset_use_spec(ds, spec_prep), "tensorflow.python.data.ops.dataset_ops.DatasetV2")
})
test_that("Can select with has_type", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(has_type("float32")) %>%
step_numeric_column(has_type("int32"))
expect_length(spec$steps, 2)
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(has_type("float32")) %>%
step_numeric_column(has_type("int32")) %>%
step_categorical_column_with_vocabulary_list(has_type("string")) %>%
step_indicator_column(has_type("string"))
expect_length(spec$steps, 6)
expect_error(spec %>% step_indicator_column(a = has_type("string")))
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(all_numeric()) %>%
step_categorical_column_with_vocabulary_list(has_type("string")) %>%
step_indicator_column(all_nominal())
expect_length(spec$steps, 6)
})
test_that("Can remove variables using -", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(all_numeric(), - b) %>%
step_categorical_column_with_vocabulary_list(all_nominal()) %>%
step_indicator_column(all_nominal(), - a)
spec <- fit(spec)
expect_length(spec$dense_features(), 2)
expect_named(spec$dense_features(), c("c", "indicator_d"))
})
test_that("StandardScaler works as expected", {
x <- runif(100)
sc <- StandardScaler$new()
splited <- split(x, rep(1:10, each = 10))
a <- lapply(splited, sc$fit_batch)
sc$fit_resume()
expect_equal(sc$mean, mean(x))
expect_equal(sc$sd, sd(x))
})
test_that("Can use a scaler_standard", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(all_numeric(), normalizer_fn = scaler_standard())
spec <- fit(spec)
value <- as.matrix(get_features(dataset, spec$dense_features()))
normalized_c <- (df$c - mean(df$c))/sd(df$c)
normalized_b <- (df$b - mean(df$b))/sd(df$b)
expect_equal(as.numeric(value[,2]), normalized_c[1:2], tolerance = 1e-6)
expect_equal(as.numeric(value[,1]), normalized_b[1:2], tolerance = 1e-6)
})
test_that("MinMaxScaler works as expected", {
x <- runif(100)
sc <- MinMaxScaler$new()
splited <- split(x, rep(1:10, each = 10))
a <- lapply(splited, sc$fit_batch)
sc$fit_resume()
expect_equal(sc$min, min(x))
expect_equal(sc$max, max(x))
})
test_that("Can use a scaler_min_max", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(all_numeric(), normalizer_fn = scaler_min_max())
spec <- fit(spec)
value <- as.matrix(get_features(dataset, spec$dense_features()))
normalized_c <- (df$c - min(df$c))/(max(df$c) - min(df$c))
normalized_b <- (df$b - min(df$b))/(max(df$b) - min(df$b))
expect_equal(as.numeric(value[,2]), normalized_c[1:2], tolerance = 1e-6)
expect_equal(as.numeric(value[,1]), normalized_b[1:2], tolerance = 1e-6)
})
test_that("Can use layer_input_from_dataset with TF datasets", {
skip_if_not_tf()
spec <- feature_spec(dataset, y ~ a + b + c + d) %>%
step_numeric_column(all_numeric(), normalizer_fn = scaler_min_max())
spec <- fit(spec)
ds <- dataset_use_spec(dataset, spec)
input <- layer_input_from_dataset(ds)
output <- input %>%
keras::layer_dense_features(spec$dense_features())
model <- keras::keras_model(inputs = input, outputs = output)
expect_length(input, 4)
if (tf$executing_eagerly())
expect_equal(dim(as.matrix(model(next_batch(ds)[[1]]))), c(2,2))
})
test_that("Can use layer_input_from_dataset with TF data frames", {
skip_if_not_tf()
spec <- feature_spec(as.data.frame(df), y ~ a + b + c + d) %>%
step_numeric_column(all_numeric(), normalizer_fn = scaler_min_max())
spec <- fit(spec)
input <- layer_input_from_dataset(as.data.frame(df)[, 1:4])
output <- input %>%
keras::layer_dense_features(spec$dense_features()) %>%
keras::layer_dense(units = 1)
model <- keras::keras_model(inputs = input, outputs = output)
keras::compile(model, loss = "mse", optimizer = "adam")
hist <- keras::fit(model, x = df, y = df$y, verbose = 0)
expect_s3_class(hist, "keras_training_history")
})
test_that("Can use data.frames", {
skip_if_not_tf()
spec <- feature_spec(hearts, target ~ .) %>%
step_numeric_column(
all_numeric(), -cp, -restecg, -exang, -sex, -fbs,
normalizer_fn = scaler_standard()
) %>%
step_categorical_column_with_vocabulary_list(thal) %>%
step_bucketized_column(age, boundaries = c(18, 25, 30, 35, 40, 45, 50, 55, 60, 65)) %>%
step_indicator_column(thal) %>%
step_embedding_column(thal, dimension = 2) %>%
step_crossed_column(c(thal, bucketized_age), hash_bucket_size = 10) %>%
step_indicator_column(crossed_thal_bucketized_age) %>%
fit()
expect_length(spec$dense_features(), 11)
})
test_that("Correctly creates indicator vars", {
skip_if_not_tf()
x <- data.frame(
y = runif(5),
x = c("a", "aã", "b", "c", "d"),
b = runif(5),
stringsAsFactors = FALSE
)
spec <- feature_spec(x, y ~ x) %>%
step_categorical_column_with_vocabulary_list(x) %>%
step_indicator_column(x)
spec <- fit(spec)
k <- keras::layer_dense_features(feature_columns = spec$dense_features())
res <- as.matrix(k(list(x = x$x)))
expect_equal(
res,
diag(nrow(res))
)
})
test_that("feature_spec works with make_csv_dataset", {
skip_if_not_tf()
TRAIN_DATA_URL <- "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
train_file_path <- keras::get_file("train_csv", TRAIN_DATA_URL)
train_dataset <- make_csv_dataset(
train_file_path,
field_delim = ",",
batch_size = 5,
num_epochs = 1
)
spec <- feature_spec(train_dataset, survived ~ .)
expect_s3_class(spec, class = "FeatureSpec")
})
test_that("can create image embedding steps", {
skip_if_not_tf()
if (tensorflow::tf$executing_eagerly())
skip("Needs non-eager execution.")
df <- list(img = array(0, dim = c(1, 192, 192, 3)))
df <- tensor_slices_dataset(df)
spec <- feature_spec(df, x = c(img)) %>%
step_image_embedding_column(
img,
module_spec = "https://tfhub.dev/google/imagenet/mobilenet_v1_075_192/quantops/feature_vector/3"
)
spec <- spec %>% fit()
layer <- keras::layer_dense_features(feature_columns = spec$dense_features())
x <- layer(list(img = array(0, dim = c(1, 192, 192, 3))))
expect_equal(x$get_shape()$as_list(), c(1L, 768L))
})
test_that("can create text embedding columns", {
skip_if_not_tf()
if (tensorflow::tf$executing_eagerly())
skip("Needs non-eager execution.")
df <- list(txt = c("hello world", "hello world"))
df <- tensor_slices_dataset(df)
spec <- feature_spec(df, x = c(txt)) %>%
step_text_embedding_column(txt, module_spec = "https://tfhub.dev/google/nnlm-en-dim50/1")
spec <- spec %>% fit()
layer <- keras::layer_dense_features(feature_columns = spec$dense_features())
x <- layer(list(txt = c("hello world", "hello world")))
expect_equal(x$get_shape()$as_list(), list(NULL, 50L))
})
test_that("can save and reload models that use a normalizer_fn", {
data <- data.frame(
y = runif(5),
x = runif(5),
b = runif(5)
)
spec <- feature_spec(data, y ~ .) %>%
step_numeric_column(x, normalizer_fn = scaler_standard()) %>%
fit()
input <- layer_input_from_dataset(data[-1])
output <- input %>%
layer_dense_features(dense_features(spec)) %>%
layer_dense(units = 1, activation = "sigmoid")
model <- keras_model(input, output)
model %>% compile(
loss = "binary_crossentropy",
optimizer = "adam",
metrics = "binary_accuracy"
)
tmp <- tempfile("model")
rds <- tempfile("rds")
save_model_weights_tf(model, tmp)
saveRDS(spec, rds)
reloaded_spec <- readRDS(rds)
input <- layer_input_from_dataset(data[-1])
output <- input %>%
layer_dense_features(dense_features(reloaded_spec)) %>%
layer_dense(units = 1, activation = "sigmoid")
new_model <- keras_model(input, output)
load_model_weights_tf(new_model, tmp)
expect_equal(
predict(model, data[-1]),
predict(new_model, data[-1])
)
})
|
targets::tar_test("tar_files_input() writes the correct targets", {
targets::tar_script({
list(
tarchetypes::tar_files_input(x, c("a.txt", "b.txt"))
)
})
out <- targets::tar_manifest(x, callr_function = NULL)
expect_equal(out$name, "x")
expect_equal(out$command, "x_files")
expect_equal(out$pattern, "map(x_files)")
out <- targets::tar_manifest(x_files, callr_function = NULL)
expect_equal(out$name, "x_files")
expect_equal(out$command, "list(\"a.txt\", \"b.txt\")")
expect_equal(out$pattern, NA_character_)
})
targets::tar_test("tar_files_input() writes the correct targets (batched)", {
targets::tar_script({
list(
tarchetypes::tar_files_input(
x,
files = c("a.txt", "b.txt", "c.txt", "d.txt"),
batches = 2
)
)
})
out <- targets::tar_manifest(x, callr_function = NULL)
expect_equal(out$name, "x")
expect_equal(out$command, "x_files")
expect_equal(out$pattern, "map(x_files)")
out <- targets::tar_manifest(x_files, callr_function = NULL)
expect_equal(out$name, "x_files")
expect_equal(
out$command,
"list(c(\"a.txt\", \"b.txt\"), c(\"c.txt\", \"d.txt\"))"
)
expect_equal(out$pattern, NA_character_)
})
targets::tar_test("tar_files_input() correctly responds to changes in files", {
file.create(c("a.txt", "b.txt", "c.txt", "d.txt"))
targets::tar_script({
list(
tarchetypes::tar_files_input(
x,
files = c("a.txt", "b.txt", "c.txt", "d.txt"),
batches = 2
)
)
})
targets::tar_make(callr_function = NULL)
meta <- targets::tar_meta(starts_with("x_"))
meta <- meta[meta$name != "x_files", ]
expect_equal(nrow(meta), 2L)
expect_equal(length(unique(meta$data)), 1L)
progress <- targets::tar_progress(starts_with("x_"))
progress <- progress[progress$name != "x_files", ]
expect_equal(nrow(progress), 2L)
expect_equal(sum(grepl("built", progress$progress)), 2L)
writeLines("b", "b.txt")
tar_make(callr_function = NULL)
meta <- targets::tar_meta(starts_with("x_"))
meta <- meta[meta$name != "x_files", ]
expect_equal(nrow(meta), 2L)
expect_equal(length(unique(meta$data)), 2L)
progress <- targets::tar_progress(starts_with("x_"))
progress <- progress[progress$name != "x_files", ]
progress <- progress[progress$progress != "skipped", ]
expect_equal(nrow(progress), 1L)
expect_equal(sum(grepl("built", progress$progress)), 1L)
out <- targets::tar_read(x_files)
exp <- list(
c("a.txt", "b.txt"),
c("c.txt", "d.txt")
)
expect_equal(out, exp)
out <- targets::tar_read(x, branches = 1)
expect_equal(unname(out), c("a.txt", "b.txt"))
out <- targets::tar_read(x, branches = 2)
expect_equal(unname(out), c("c.txt", "d.txt"))
})
|
CRPS <-
function(fit, ensembleData, dates=NULL, nSamples=NULL, seed=NULL, ...) {
UseMethod("CRPS")
}
|
context("keys")
test_that("key options returned", {
x <- google_keys()
expect_true(inherits(x, "googleway_api"))
})
test_that("keys are cleared", {
set_key("my_key")
clear_keys()
x <- google_keys()
v <- unname(sapply(x[[1]], `[`))
expect_true(sum(is.na(v)) == 14)
})
test_that("default key set", {
clear_keys()
set_key("my_key")
x <- google_keys()
expect_true(x[[1]][['default']] == "my_key")
v <- unname(sapply(x[[1]], `[`))
expect_true(sum(is.na(v)) == 13)
expect_true(v[1] == "my_key")
})
test_that("specific key set", {
clear_keys()
set_key("my_key", "directions")
x <- google_keys()
expect_true(x[[1]][['directions']] == "my_key")
v <- unname(sapply(x[[1]], `[`))
expect_true(sum(is.na(v)) == 13)
expect_true(v[3] == "my_key")
})
test_that("get_api_key retrieves api key", {
clear_keys()
set_key("my_key", "map")
expect_true(googleway:::get_api_key("map") == "my_key")
})
test_that("default api key returned", {
clear_keys()
set_key("my_key")
expect_true(googleway:::get_default_key() == "my_key")
expect_true(googleway:::get_api_key("directions") == "my_key")
})
test_that("api keys printed", {
expect_silent(google_keys())
})
|
library(randomForest);
library(unbalanced);
smote_and_undersample <- function(data, y, fp=1, ratio=1, k=5) {
perc.over <- fp * 100;
perc.under <- (100.0 * ratio * (perc.over+100))/perc.over;
return (ubSMOTE(as.data.frame(data), y, perc.over = perc.over, k = k, perc.under = perc.under));
}
smote <- function(data, fp=1, k=5) {
if (fp<1)
return(data);
perc.over <- fp * 100;
y <- as.factor(rep(1,nrow(data)));
return (ubSMOTE(as.data.frame(data), y, perc.over = perc.over, k = k, perc.under = 0)$X);
}
do.random.partition <- function(n.ex, n.partitions, seed=0) {
if (seed!=0)
set.seed(seed);
part <- vector(mode="list", length=n.partitions);
m.per.part <- round(n.ex/n.partitions);
shuffled <- sample(1:n.ex);
start=1;
end=m.per.part;
for (i in 1:(n.partitions-1)) {
part[[i]] <- shuffled[start:end];
start <- start+m.per.part;
end <- end+m.per.part;
}
part[[n.partitions]] <- shuffled[start:n.ex];
return(part);
}
hyperSMURF.train <- function (data, y, n.part=10, fp=1, ratio=1, k=5, ntree=10, mtry=5, cutoff=c(0.5,0.5), seed = 0, file="") {
data.min <- as.data.frame(data[y==1,]);
data <- data[-which(y==1),];
gc();
n.data <- nrow(data);
rf.list <- vector(mode="list", n.part);
part <- do.random.partition(n.data, n.partitions=n.part, seed=seed);
for (i in 1:n.part) {
data.min.over <- smote(data.min, fp=fp, k=k);
n.data.min.over <- nrow(data.min.over);
n.maj <- ratio*n.data.min.over;
ind.part <- part[[i]];
if (length(ind.part) >= n.maj)
indices.maj <- ind.part[1:n.maj]
else
indices.maj <- ind.part;
data.train <- rbind(data.min.over, data[indices.maj,]);
y <- as.factor(c(rep(1,n.data.min.over), rep(0, length(indices.maj))));
rf.list [[i]] <-randomForest(data.train, y, ntree = ntree, mtry = mtry, cutoff = cutoff);
rm(data.train, data.min.over); gc();
cat("Training of ensemble ", i, "done.\n");
}
if (file!="")
save(rf.list, file=file);
return(rf.list);
}
hyperSMURF.test <- function (data, HSmodel) {
n.models <- length(HSmodel);
n.ex <- nrow(data);
prob <- numeric(n.ex);
for (i in 1:n.models) {
prob <- prob + predict(HSmodel[[i]], data, type="prob")[,2];
gc();
}
prob <- prob/n.models;
names(prob) <- rownames(data);
return(prob);
}
hyperSMURF.test.thresh <- function (data, HSmodel) {
n.models <- length(HSmodel);
n.ex <- nrow(data);
prob <- numeric(n.ex);
for (i in 1:n.models) {
lab <- predict(HSmodel[[i]], data, type="response");
p <- ifelse(lab==1, 1, 0);
prob <- prob + p;
gc();
}
prob <- prob/n.models;
names(prob) <- rownames(data);
return(prob);
}
hyperSMURF.cv <- function (data, y, kk=5, n.part=10, fp=1, ratio=1, k=5, ntree=10, mtry=5, cutoff=c(0.5,0.5), thresh=FALSE, seed = 0, fold.partition=NULL, file="") {
n.data <- nrow(data);
indices.positives <- which(y == 1) ;
scores <- numeric(n.data);
names(scores) <- rownames(data);
if (is.null(fold.partition)) {
cat("Creating new folds\n")
folds <- do.stratified.cv.data(1:n.data, indices.positives, k=kk, seed=seed);
} else {
cat("Using given folds\n")
folds <- do.stratified.cv.data.from.folds(1:n.data,indices.positives,fold.partition,k=kk);
}
for (i in 1:kk) {
ind.test <- c(folds$fold.positives[[i]], folds$fold.non.positives[[i]]);
ind.pool.pos <- integer(0);
ind.pool.neg <- integer(0);
for (j in 1:kk)
if (j!=i) {
ind.pool.pos <- c(ind.pool.pos, folds$fold.positives[[j]]);
ind.pool.neg <- c(ind.pool.neg, folds$fold.non.positives[[j]]);
}
data.train <- data[c(ind.pool.pos, ind.pool.neg),];
y.train <- as.factor(c(rep(1, length(ind.pool.pos)), rep (0, length(ind.pool.neg))));
cat("Starting training on Fold ", i, "...\n");
HS <- hyperSMURF.train (data.train, y.train, n.part=n.part, fp=fp, ratio=ratio, k=k, ntree=ntree, mtry=mtry, cutoff=cutoff, seed = seed);
rm(data.train); gc();
data.test <- data[ind.test,];
cat("Starting test on Fold ", i, "...\n");
if (thresh)
scores[ind.test] <- hyperSMURF.test.thresh(data.test, HS)
else
scores[ind.test] <- hyperSMURF.test(data.test, HS);
cat("End test on Fold ", i, ".\n");
rm(data.test);
if (file=="")
rm(HS)
else
HS.list <- c(HS.list, HS);
gc();
cat("Fold ", i, " done -----\n");
}
if (file != "")
save(HS.list, file);
return(scores);
}
do.stratified.cv.data <- function(examples, positives, k=10, seed=0) {
if (seed!=0)
set.seed(seed);
fold.non.positives <- fold.positives <- list();
for (i in 1:k) {
fold.non.positives[[i]] <- integer(0);
fold.positives[[i]] <- integer(0);
}
non.positives <- setdiff(examples,positives);
non.positives <- sample(non.positives);
positives <- sample(positives);
n.positives <- length(positives);
resto.positives <- n.positives%%k;
n.pos.per.fold <- (n.positives - resto.positives)/k;
n.non.positives <- length(non.positives);
resto.non.positives <- n.non.positives%%k;
n.non.pos.per.fold <- (n.non.positives - resto.non.positives)/k;
j=1;
if (n.non.pos.per.fold > 0)
for (i in 1:k) {
fold.non.positives[[i]] <- non.positives[j:(j+n.non.pos.per.fold-1)];
j <- j + n.non.pos.per.fold;
}
j.pos=1;
if (n.pos.per.fold > 0)
for (i in 1:k) {
fold.positives[[i]] <- positives[j.pos:(j.pos+n.pos.per.fold-1)];
j.pos <- j.pos + n.pos.per.fold;
}
if (resto.non.positives > 0)
for (i in k:(k-resto.non.positives+1)) {
fold.non.positives[[i]] <- c(fold.non.positives[[i]], non.positives[j]);
j <- j + 1;
}
if (resto.positives > 0)
for (i in 1:resto.positives) {
fold.positives[[i]] <- c(fold.positives[[i]], positives[j.pos]);
j.pos <- j.pos + 1;
}
return(list(fold.non.positives=fold.non.positives, fold.positives=fold.positives));
}
do.stratified.cv.data.from.folds <- function(examples, positives, folds, k=10) {
non.positives <- setdiff(examples,positives);
fold.non.positives <- fold.positives <- list();
for (i in 1:k) {
fold.non.positives[[i]] <- integer(0);
fold.positives[[i]] <- integer(0);
fold.positives[[i]] <- positives[folds[positives]==i-1];
fold.non.positives[[i]] <- non.positives[folds[non.positives]==i-1];
}
return(list(fold.non.positives=fold.non.positives, fold.positives=fold.positives));
}
imbalanced.data.generator <- function(n.pos=100, n.neg=2000, n.features=10, n.inf.features=2, sd=1, seed=0) {
if (seed!=0)
set.seed(seed);
class0 <- matrix(rnorm(n.neg*n.features, mean=0, sd=1), nrow=n.neg);
class1 <-matrix(rnorm(n.pos*n.inf.features, mean=1, sd=sd), nrow=n.pos);
classr1<-matrix(rnorm(n.pos*(n.features-n.inf.features), mean=0, sd=1), nrow=n.pos);
class1 <- cbind(class1,classr1);
data <- rbind(class1,class0);
labels<-factor(c(rep(1,n.pos),rep(0,n.neg)), levels=c("1","0"));
return (list(data=data, labels=labels));
}
|
base_palette_set <- function(theme = .globals$theme) {
base_palette_restore()
codes <- theme$qualitative
.globals$base_palette <- if (isTRUE(is.na(codes))) {
attempt_palette()
} else {
attempt_palette(codes)
}
}
base_palette_restore <- function() {
if (is.null(.globals$base_palette)) return()
attempt_palette(.globals$base_palette)
rm("base_palette", envir = .globals)
}
base_params_set <- function(theme = .globals$theme) {
base_params_restore()
params <- list()
bg <- theme$bg
if (length(bg)) {
params <- c(params, attempt_par(bg = bg))
}
fg <- theme$fg
if (length(fg)) {
params <- c(params, attempt_par(
fg = fg,
col.axis = fg,
col.lab = fg,
col.main = fg,
col.sub = fg
))
}
font <- theme$font
if (length(font$family)) {
params <- c(params, attempt_par(
family = font$family,
cex.axis = font$scale,
cex.lab = font$scale,
cex.main = font$scale * 1.2,
cex.sub = font$scale
))
}
.globals$base_params <- params
}
base_params_restore <- function() {
if (is.null(.globals$base_params)) return()
do.call(attempt_par, .globals$base_params)
rm("base_params", envir = .globals)
}
attempt_par <- function(...) {
attempt_(par(...))
}
attempt_palette <- function(...) {
attempt_(palette(...))
}
attempt_ <- function(expr) {
if (is_null_device()) {
attempt_with_new_device(expr)
} else {
force(expr)
}
}
|
bins <- function(breaks, closed = c("right", "left"),
fuzz = 1e-08 * stats::median(diff(breaks))) {
if (!is.numeric(breaks)) abort("`breaks` must be a numeric vector")
closed <- match.arg(closed)
breaks <- sort(breaks)
if (closed == "right") {
fuzzes <- c(-fuzz, rep.int(fuzz, length(breaks) - 1))
} else {
fuzzes <- c(rep.int(-fuzz, length(breaks) - 1), fuzz)
}
structure(
list(
breaks = breaks,
fuzzy = breaks + fuzzes,
right_closed = closed == "right"
),
class = "ggplot2_bins"
)
}
is_bins <- function(x) inherits(x, "ggplot2_bins")
print.ggplot2_bins <- function(x, ...) {
n <- length(x$breaks)
cat("<Bins>\n")
if (x$right_closed) {
left <- c("[", rep("(", n - 2))
right <- rep("]", n - 1)
} else {
left <- rep("[", n - 1)
right <- c(rep(")", n - 2), "]")
}
breaks <- format(x$breaks)
bins <- paste0("* ", left, breaks[-n], ",", breaks[-1], right)
cat(bins, sep = "\n")
cat("\n")
}
bin_breaks <- function(breaks, closed = c("right", "left")) {
bins(breaks, closed)
}
bin_breaks_width <- function(x_range, width = NULL, center = NULL,
boundary = NULL, closed = c("right", "left")) {
if (length(x_range) != 2) abort("`x_range` must have two elements")
if (!(is.numeric(width) && length(width) == 1)) abort("`width` must be a numeric scalar")
if (width <= 0) {
abort("`binwidth` must be positive")
}
if (!is.null(boundary) && !is.null(center)) {
abort("Only one of 'boundary' and 'center' may be specified.")
} else if (is.null(boundary)) {
if (is.null(center)) {
boundary <- width / 2
} else {
boundary <- center - width / 2
}
}
x_range <- as.numeric(x_range)
width <- as.numeric(width)
boundary <- as.numeric(boundary)
shift <- floor((x_range[1] - boundary) / width)
origin <- boundary + shift * width
max_x <- x_range[2] + (1 - 1e-08) * width
if (isTRUE((max_x - origin) / width > 1e6)) {
abort("The number of histogram bins must be less than 1,000,000.\nDid you make `binwidth` too small?")
}
breaks <- seq(origin, max_x, width)
if (length(breaks) == 1) {
breaks <- c(breaks, breaks + width)
}
bin_breaks(breaks, closed = closed)
}
bin_breaks_bins <- function(x_range, bins = 30, center = NULL,
boundary = NULL, closed = c("right", "left")) {
if (length(x_range) != 2) abort("`x_range` must have two elements")
bins <- as.integer(bins)
if (bins < 1) {
abort("Need at least one bin.")
} else if (zero_range(x_range)) {
width <- 0.1
} else if (bins == 1) {
width <- diff(x_range)
boundary <- x_range[1]
} else {
width <- (x_range[2] - x_range[1]) / (bins - 1)
}
bin_breaks_width(x_range, width, boundary = boundary, center = center,
closed = closed)
}
bin_vector <- function(x, bins, weight = NULL, pad = FALSE) {
if (!is_bins(bins)) abort("`bins` must be a ggplot2_bins object")
if (all(is.na(x))) {
return(bin_out(length(x), NA, NA, xmin = NA, xmax = NA))
}
if (is.null(weight)) {
weight <- rep(1, length(x))
} else {
weight[is.na(weight)] <- 0
}
bin_idx <- cut(x, bins$fuzzy, right = bins$right_closed,
include.lowest = TRUE)
bin_count <- as.numeric(tapply(weight, bin_idx, sum, na.rm = TRUE))
bin_count[is.na(bin_count)] <- 0
bin_x <- (bins$breaks[-length(bins$breaks)] + bins$breaks[-1]) / 2
bin_widths <- diff(bins$breaks)
if (pad) {
bin_count <- c(0, bin_count, 0)
width1 <- bin_widths[1]
widthn <- bin_widths[length(bin_widths)]
bin_widths <- c(width1, bin_widths, widthn)
bin_x <- c(bin_x[1] - width1, bin_x, bin_x[length(bin_x)] + widthn)
}
if (any(is.na(bins))) {
bin_count <- c(bin_count, sum(is.na(bins)))
bin_widths <- c(bin_widths, NA)
bin_x <- c(bin_x, NA)
}
bin_out(bin_count, bin_x, bin_widths)
}
bin_out <- function(count = integer(0), x = numeric(0), width = numeric(0),
xmin = x - width / 2, xmax = x + width / 2) {
density <- count / width / sum(abs(count))
new_data_frame(list(
count = count,
x = x,
xmin = xmin,
xmax = xmax,
width = width,
density = density,
ncount = count / max(abs(count)),
ndensity = density / max(abs(density))
), n = length(count))
}
|
oe_get_network = function(
place,
mode = c("cycling", "driving", "walking"),
...
) {
mode = match.arg(mode)
oe_get_options = switch(
mode,
cycling = load_options_cycling(place),
driving = load_options_driving(place),
walking = load_options_walking(place)
)
dots_args = list(...)
oe_get_options = check_args_network(dots_args, oe_get_options)
do.call(oe_get, oe_get_options)
}
load_options_cycling = function(place) {
list(
place = place,
layer = "lines",
extra_tags = c("access", "bicycle", "service"),
vectortranslate_options = c(
"-where", "
(highway IS NOT NULL)
AND
(highway NOT IN (
'abandonded', 'bus_guideway', 'byway', 'construction', 'corridor', 'elevator',
'fixme', 'escalator', 'gallop', 'historic', 'no', 'planned', 'platform',
'proposed', 'raceway', 'steps'
))
AND
(highway NOT IN ('motorway', 'motorway_link', 'footway', 'bridleway',
'pedestrian') OR bicycle IN ('yes', 'designated', 'permissive', 'destination')
)
AND
(access NOT IN ('private', 'no'))
AND
(bicycle NOT IN ('private', 'no', 'use_sidepath', 'restricted'))
AND
(service NOT ILIKE 'private%')
"
)
)
}
load_options_walking = function(place) {
list(
place = place,
layer = "lines",
extra_tags = c("access", "foot", "service"),
vectortranslate_options = c(
"-where", "
(highway IS NOT NULL)
AND
(highway NOT IN ('abandonded', 'bus_guideway', 'byway', 'construction', 'corridor', 'elevator',
'fixme', 'escalator', 'gallop', 'historic', 'no', 'planned', 'platform', 'proposed', 'raceway',
'motorway', 'motorway_link'))
AND
(highway <> 'cycleway' OR foot IN ('yes', 'designated', 'permissive', 'destination'))
AND
(access NOT IN ('private', 'no'))
AND
(foot NOT IN ('private', 'no', 'use_sidepath', 'restricted'))
AND
(service NOT ILIKE 'private%')
"
)
)
}
load_options_driving = function(place) {
list(
place = place,
layer = "lines",
extra_tags = c("access", "service"),
vectortranslate_options = c(
"-where", "
(highway IS NOT NULL)
AND
(highway NOT IN (
'abandonded', 'bus_guideway', 'byway', 'construction', 'corridor', 'elevator',
'fixme', 'escalator', 'gallop', 'historic', 'no', 'planned', 'platform',
'proposed', 'cycleway', 'pedestrian', 'bridleway', 'path', 'footway',
'steps'
))
AND
(access NOT IN ('private', 'no'))
AND
(service NOT ILIKE 'private%')
"
)
)
}
check_args_network = function(dots_args, oe_get_options) {
if (length(dots_args) == 0L) {
return(oe_get_options)
}
if (!is.null(dots_args[["layer"]]) && dots_args[["layer"]] != "lines") {
warning(
"oe_get_network() works only with lines layer. Ignoring other values",
call. = FALSE
)
dots_args[["layer"]] = NULL
}
if (!is.null(dots_args[["extra_tags"]])) {
oe_get_options[["extra_tags"]] = unique(
c(oe_get_options[["extra_tags"]], dots_args[["extra_tags"]])
)
dots_args[["extra_tags"]] = NULL
}
if (!is.null(dots_args[["vectortranslate_options"]])) {
if ("-where" %in% dots_args[["vectortranslate_options"]]) {
stop(
"The vectortranslate_options inside oe_get_network() cannot be used",
"to define a query with the -where argument. Use the query argument",
call. = FALSE
)
}
oe_get_options[["vectortranslate_options"]] = c(
oe_get_options[["vectortranslate_options"]], dots_args[["vectortranslate_options"]]
)
dots_args[["vectortranslate_options"]] = NULL
}
oe_get_options = c(oe_get_options, dots_args)
oe_get_options
}
|
NULL
jovanovicplot <- function(Ce,Qe)
{
.Deprecated("jovanovicanalysis")
x5<-Ce
y5<-log10(Qe)
plot(x5, y5, main = "Jovanovic Isotherm", xlab = "Ce", ylab= "ln(Qe)", type = "p", col ="blue", pch =10)
fit52 <- lm(y5~x5)
abline(fit52, col = "green")
}
|
estimate_dataset_bmr <- function(d, p1 = NULL, p2 = NULL, plot = TRUE) {
d <- .check_bmr_input(d)
base_cols <- get_base_cols(d$result)
act_set <- make_act_na_highconc_in(d$result$act_set)
pvar_datasets <- cal_dataset_pvar(act_set, base_cols)
knees <- cal_dataset_knee(pvard = pvar_datasets, base_cols = base_cols, p1 = p1, p2 = p2)
resultl <- purrr::map(c("stats", "outcome"), unnest_knee_data, kneed = knees)
result <- list(stats = resultl[[1]], outcome = resultl[[2]])
class(result) <- c("rcurvep_bmr", class(result))
if (plot) {
p <- plot.rcurvep_bmr(result)
print(p)
}
return(result)
}
cal_dataset_pvar <- function(act_set, base_cols) {
base_colsq <- rlang::syms(base_cols)
base_cols_nochemq <- rlang::syms(base_cols[!base_cols %in% "chemical"])
sdd <- act_set %>%
dplyr::group_by(!!!base_colsq) %>%
dplyr::summarise(
sd_pod = sd(.data$POD), n_rep = dplyr::n()
) %>%
dplyr::ungroup()
result <- sdd %>%
dplyr::group_by(!!!base_cols_nochemq) %>%
dplyr::summarize(
pvar = sum((.data$sd_pod^2)*(.data$n_rep - 1))/(sum(.data$n_rep) - dplyr::n())) %>%
dplyr::ungroup()
return(result)
}
cal_dataset_knee <- function(pvard, base_cols, p1, p2) {
base_cols_f <- base_cols[!base_cols %in% c("chemical", "TRSH")]
knees <- pvard %>%
tidyr::nest(input = -c(base_cols_f)) %>%
dplyr::mutate(
knee_out = purrr::map(
.data$input,
cal_knee_point, xaxis = "TRSH", yaxis = "pvar", p1 = p1, p2 = p2, plot = FALSE)
)
return(knees)
}
unnest_knee_data <- function(kneed, type = c("stats", "outcome")) {
result <- kneed %>%
dplyr::mutate(temp = purrr::map(.data$knee_out, ~ .x[[type]])) %>%
dplyr::select(-.data$knee_out, -.data$input) %>%
tidyr::unnest(cols = c("temp"))
return(result)
}
cal_knee_point <- function(d, xaxis, yaxis, p1 = NULL, p2 = NULL, plot = TRUE) {
xvar_c <- as.character(xaxis)
yvar_c <- as.character(yaxis)
d1 <- d %>% dplyr::select(dplyr::one_of(c(xvar_c, yvar_c)))
res_stat <- add_fitted_y(d = d1, xvar = xvar_c, yvar = yvar_c)
res_stat <- add_dist_2_line(res_stat, xvar = xvar_c, yvar = yvar_c, p1 = p1, p2 = p2)
res_stat <- add_curvature(res_stat, xvar = xvar_c, yvar = yvar_c)
res_out <- create_bmr_report(res_stat, xvar = xvar_c, yvar = yvar_c)
result <- list(stats = res_stat, outcome = res_out)
class(result) <- c("rcurvep_bmr", class(result))
if (plot) {
p <- plot.rcurvep_bmr(result)
print(p)
}
return(result)
}
create_bmr_report <- function(distd, xvar, yvar) {
result <- tibble::tibble(
bmr_ori = get_thres_at_max_dist2l(distd[[xvar]], distd[['dist2l_ori']]),
p1_ori = unique(distd$p1_ori),
p2_ori = unique(distd$p2_ori),
bmr_exp = get_thres_at_max_dist2l(distd[[xvar]],distd[['dist2l_exp']]),
p1_exp = unique(distd$p1_exp),
p2_exp = unique(distd$p2_exp),
cor_exp_fit = cal_cor_ori_fitted(distd[[yvar]], distd[['y_exp_fit']]),
cor_lm_fit = cal_cor_ori_fitted(distd[[yvar]], distd[['y_lm_fit']])
)
result[['qc']] <- qc_data(result[['cor_exp_fit']], result[['cor_lm_fit']])
return(result)
}
qc_data <- function(exp_cor, linear_cor) {
if (is.na(exp_cor) | is.na(linear_cor) )
{
return("check")
}
if (abs(linear_cor - exp_cor) <= 0.2) {
if (exp_cor >= 0.90) {
comment <- "cautionary"
} else {
comment <- "check"
}
} else {
if (exp_cor >= 0.95) {
comment <- "OK"
} else {
comment <- "cautionary"
}
}
return(comment)
}
add_fitted_y <- function(d, xvar, yvar) {
result <- d %>%
dplyr::mutate(
y_exp_fit = try(fitted(cal_exponential_fit(.data[[xvar]], .data[[yvar]])), silent = TRUE),
y_lm_fit = fitted(cal_linear_fit(.data[[xvar]], .data[[yvar]]))
) %>%
dplyr::mutate_at("y_exp_fit", as.numeric)
return(result)
}
add_dist_2_line <- function(fitd, xvar, yvar, p1, p2) {
exp_dist <- cal_dist2l(fitd[[xvar]], fitd[['y_exp_fit']], p1 = p1, p2 = p2)
ori_dist <- cal_dist2l(fitd[[xvar]], fitd[[yvar]], p1 = p1, p2 = p2)
result <- fitd %>%
dplyr::mutate(
dist2l_ori = ori_dist$dist2l,
p1_ori = ori_dist$p1,
p2_ori = ori_dist$p2,
dist2l_exp = exp_dist$dist2l,
p1_exp = exp_dist$p1,
p2_exp = exp_dist$p2
)
return(result)
}
add_curvature <- function(d, xvar, yvar) {
result <- d %>%
dplyr::mutate(
curva = cal_curvature(.data[[xvar]], .data[[yvar]])
)
return(result)
}
cal_exponential_fit <- function(thres, vars) {
dd <- data.frame(x = thres, y = vars)
mod_nls <- nls(y ~ SSasymp(x, yf, y0, log_alpha), data = dd)
return(mod_nls)
}
cal_linear_fit <- function(thres, vars) {
dd <- data.frame(x = thres, y = vars)
mod_lm <- lm(y ~ x , data = dd)
return(mod_lm)
}
cal_curvature <- function(thres, vars) {
spl <- smooth.spline(thres, vars)
spl_der1 <- predict(spl, deriv = 1)
spl_der2 <- predict(spl, deriv = 2)
curvature <- spl_der2$y/(((1 + (spl_der1$y)^2))^3/2)
return(curvature)
}
cal_dist2l <- function(thres, vars, p1 = NULL, p2 = NULL) {
if (is.null(p1)) { p1 <- which.max(vars); if (length(p1) == 0) p1 <- 0}
if (is.null(p2)) { p2 <- which.min(vars); if (length(p2) == 0) p2 <- 0 }
try(
if ( p1 >= p2 | p1 > length(thres) | p2 > length(thres)) {
warning("p1 has to be smaller than p2, p1 and p2 do not in the range of the threshold index")
dists <- rep(as.numeric(NA), length(thres))
}
)
d <- data.frame(x = thres, y = vars)
while (p1 < p2)
{
l1 <- d[p1, ] %>% purrr::as_vector()
l2 <- d[p2, ] %>% purrr::as_vector()
dists <- apply(d, 1, function(x) {
x <- purrr::as_vector(x)
v1 <- l1 - l2
v2 <- x - l1
m <- cbind(v1,v2)
d <- abs(det(m))/sqrt(sum(v1*v1))
return(d)
})
dists[!(seq(1:length(dists)) %in% p1:p2)] <- NA
l3 <- d[which.max(dists),] %>% purrr::as_vector()
if (abs(l3[2] - l2[2]) < abs(l3[2] - l1[2])) {
break
} else
{
p1 <- p1 + 1
}
}
result <- list(dist2l = dists, p1 = p1, p2 = p2)
return(result)
}
get_thres_at_max_dist2l <- function(thres, dist2l) {
ind_max <- thres[which.max(dist2l)]
if ( length(ind_max) == 0 ) ind_max <- as.numeric(NA)
return(ind_max)
}
cal_cor_ori_fitted <- function(ori_vars, fitted_vars) {
return(cor(ori_vars, fitted_vars))
}
|
ChiES<-function(phi, df, nlow, nhigh, by = 1, alpha=.05)
{
result <- data.frame(matrix(ncol = 2))
colnames(result) <- c( "n","Power")
{if(phi<0|phi>1.0){stop("Invalid effect size, phi must be between 0 and 1.0")
}
else
for(n in seq(nlow,nhigh, by)){
lambda<-n*phi^2
tabled<-stats::qchisq(1-alpha, df=df)
power<-round(1-stats::pchisq(tabled, df=df, lambda),4)
result[n, 1]<-n
result[n, 2]<-power}
output<-na.omit(result)
rownames(output)<- c()
output
}}
|
coor <-
function (a, b)
{
out = list(net = a, coord = b)
class(out) = c("GML")
out
}
|
write.term.info <- function(file, terms, data, object = NULL, contrasts.arg = NULL,
xlev = NULL, intcpt = TRUE, rdafile)
{
warn <- getOption("warn")
options(warn = -1L)
nt <- length(terms)
reclass <- function(x) {
if(x == "ps.smooth.spec")
x <- "sm.bayesx"
if(x == "rw2.smooth.spec")
x <- "sm.bayesx"
if(x == "rw1.smooth.spec")
x <- "sm.bayesx"
if(x == "bl.smooth.spec")
x <- "sm.bayesx"
if(x == "cs.smooth.spec")
x <- "sm.bayesx"
if(x == "gk.smooth.spec")
x <- "geo.bayesx"
if(x == "gs.smooth.spec")
x <- "geo.bayesx"
if(x == "mrf.smooth.spec")
x <- "mrf.bayesx"
if(x == "re.smooth.spec")
x <- "random.bayesx"
if(x == "random.smooth.spec")
x <- "random.bayesx"
x
}
if(nt > 0L) {
for(k in 1L:nt) {
if(is.sm(terms[k])) {
a <- parse(text = terms[[k]])[[1L]]
a_has_xt <- "xt" %in% names(a)
map <- paste("NULL")
if(a_has_xt) {
if("map" %in% names(a$xt))
map <- paste("\'", a$xt$map, "\'", sep = "")
if("polys" %in% names(a$xt))
map <- paste("\'", a$xt$polys, "\'", sep = "")
}
te <- eval(parse(text = terms[k]))
if(!is.null(te$map.name))
map <- paste("\'", te$map.name, "\'", sep = "")
if(!is.null(te$xt$map.name))
map <- paste("\'", te$xt$map.name, "\'", sep = "")
fby <- FALSE
if(te$by != "NA") {
if(!is.character(data)) {
by <- data[[te$by]]
if(is.factor(by)) {
fby <- TRUE
fnv <- paste("c(", paste("\'", te$by, levels(by), "\'", sep = "", collapse = ","),
")", sep = "")
}
}
}
israndom <- FALSE
if(class(te) == "ra.smooth.spec" || class(te) == "re.smooth.spec")
israndom <- TRUE
if(fby) {
te$label <- gsub(")", paste(",by=", te$by, ")", sep = ""), te$label)
info <- paste("list(term=\'", te$label, "\',pos=", k, ",by=\'", te$by,
"\',isFactor=FALSE", ",isFactorBy=", fby, ",isFactorByNames=", fnv,
",map=", map, ",israndom=", israndom, ",class=\'", reclass(class(te)),
"\',bs=\'", gsub(".smooth.spec", "", class(te)),
"\',call=\'", terms[k], "\')", sep = "")
} else {
info <- paste("list(term=\'", te$label, "\',pos=", k, ",by=\'", te$by,
"\',isFactor=FALSE", ",isFactorBy=", fby, ",map=", map,
",israndom=", israndom, ",class=\'", reclass(class(te)),
"\',bs=\'", gsub(".smooth.spec", "", class(te)),
"\',call=\'", terms[k], "\')", sep = "")
}
} else {
sp <- FALSE
if(grepl(":", terms[k]))
sp <- TRUE
if(!is.character(data) && !sp)
x <- data[[terms[k]]]
if(is.factor(x) && !sp) {
m <- model.matrix(as.formula(paste("~ -1 +", terms[k])), data, contrasts.arg, xlev)
realname <- colnames(m)
fn <- rmf(realname)
realname <- paste("c(\'", paste(realname, sep = "", collapse = "\', \'"), "\')", sep = "")
fnv <- "c("
nf <- length(fn)
if(nf > 1L)
for(i in 1L:(nf - 1L))
fnv <- paste(fnv, "\'", fn[i], "\',", sep = "")
fnv <- paste(fnv, "\'", fn[nf], "\')", sep = "")
xl <- paste(levels(x), collapse = "\',\'")
xl <- paste("c(\'", xl , "\')", sep = "")
info <- paste("list(term=\'", terms[k], "\',pos=" , k,
",isFactor=TRUE", ",names=", fnv, ",levels=", xl, ",realname=", realname,
",class=\'linear.bayesx\'", ")", sep = "")
} else {
info <- paste("list(term=\'", rmf(terms[k]), "\',pos=", k, ",isFactor=FALSE, realname=",
paste("\'", terms[k], "\'", sep = ""), ",class=\'linear.bayesx\'", ")", sep = "")
}
}
info <- paste(info,"\n")
if(k < 2L)
cat(info, file = file, append = FALSE)
else
cat(info, file = file, append = TRUE)
}
if(!is.null(object)) {
if(!is.null(object$YLevels)) {
YLevels <- paste(object$YLevels, collapse = "\\',\\'")
YLevels <- paste("c(\\'", YLevels, "\\')", sep = "")
}
if(!is.null(object$nYLevels)) {
nYLevels <- paste(object$nYLevels, collapse = "\\',\\'")
nYLevels <- paste("c(\\'", nYLevels, "\\')", sep = "")
}
if(!is.null(object$order)) {
ooo <- paste(object$order, collapse = ",")
ooo <- paste("\'c(", ooo, ")\'", sep = "")
}
f <- object$oformula
save(f, file = rdafile)
f <- paste(f[[2L]], " ~ ", paste(attr(terms(f),
"term.labels"), collapse= " + "), sep = "")
info <- paste("list(formula=\'", f, "\',", sep = "")
if(!is.null(object$hlevel))
object$method <- "HMCMC"
info <- paste(info, "method=\'", object$method, "\',", sep = "")
info <- paste(info, "family=\'", object$family, "\',", sep = "")
info <- paste(info, "iterations=\'", object$iterations, "\',", sep = "")
info <- paste(info, "step=\'", object$step, "\',", sep = "")
if(!is.null(object$YLevels))
info <- paste(info, "YLevels=\'", YLevels, "\',", sep = "")
if(!is.null(object$nYLevels))
info <- paste(info, "nYLevels=\'", nYLevels, "\',", sep = "")
if(!is.null(object$order))
info <- paste(info, "order=", ooo, ",", sep = "")
info <- paste(info, "model.name=\'", object$model.name, "\')\n", sep = "")
cat(info, file = file, append = TRUE)
}
}
options(warn = warn)
return(invisible(NULL))
}
|
drop_metadata_cols <- function(tbl) {
to_be_dropped <- c('..resource', '..timestamp', '..page')
dplyr::select(tbl, -to_be_dropped)
}
|
context("collapse and data.table integration")
bmean <- base::mean
options(warn = -1L)
library(data.table)
library(magrittr)
mtcDT <- qDT(roworderv(mtcars))
irisDT <- qDT(ss(iris, 1:100))
n <- 5L
assignInNamespace("cedta.override", "collapse", "data.table")
options(warn = 1L)
test_that("creating columns and printing works after passing a data.table through collapse functions", {
expect_true(is.data.table(mtcDT))
expect_true(is.data.table(irisDT))
expect_output(print(mtcDT))
expect_identical(names(mtcDT), names(mtcars))
expect_silent(mtcDT[, col := 1])
expect_output(print(mtcDT))
expect_silent(mtcDT[, col := NULL])
expect_identical(names(mtcDT), names(mtcars))
expect_output(print(mtcDT))
expect_silent(irisDT[, col := 1])
expect_silent(irisDT[, col := NULL])
dt <- fscale(copy(mtcDT))
expect_warning(dt[, new := 1])
expect_output(print(dt))
dt <- fsum(copy(mtcDT), TRA = 1)
expect_warning(dt[, new := 1])
expect_output(print(dt))
dt <- fsum(copy(mtcDT), drop = FALSE)
expect_warning(dt[, new := 1])
expect_output(print(dt))
for(i in 1:n) {
if(!identical(copy, identity)) mtcDT <- qDT(mtcDT)
expect_silent(mtcDT[, col := 1])
expect_silent(mtcDT[, col := NULL])
expect_identical(names(mtcDT), names(mtcars))
expect_identical(length(mtcDT), length(mtcars))
}
for(i in 1:n) {
dt <- fgroup_by(mtcDT, cyl)
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt2 <- fgroup_vars(dt)
expect_silent(dt2[, new := 1])
expect_output(print(dt2))
}
for(i in 1:n) {
dt <- fungroup(fgroup_by(mtcDT, c(2,8:9)))
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- funique(copy(mtcDT))
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- funique(copy(mtcDT), cols = "cyl")
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- fselect(copy(mtcDT), -mpg, -hp)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- fselect(copy(mtcDT), col2 = disp, wt:carb)
expect_silent(dt[, new := 1])
expect_output(print(dt))
fselect(dt, col2, new) <- NULL
expect_silent(dt[, ncol := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- fsubset(copy(mtcDT), cyl == 4)
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- fsubset(copy(mtcDT), cyl == 4, bla = mpg, vs:am)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% smr(mean_mpg = fmean(mpg))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% smr(mean_mpg = bmean(mpg))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% gby(cyl) %>% smr(mean_mpg = fmean(mpg))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% gby(cyl) %>% smr(mean_mpg = bmean(mpg))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- ftransform(copy(mtcDT), bla = 1)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
settransform(dt, bla2 = 1)
expect_silent(dt[, new2 := 1])
expect_output(print(dt))
}
for(i in 1:n) {
ftransform(dt) <- list(sds = mtcDT$qsec)
expect_silent(dt[, new3 := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- fcompute(copy(mtcDT), bla = mpg + cyl, df = 1, keep = 7:10)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- roworderv(copy(mtcDT))
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- roworder(copy(mtcDT), cyl, -vs)
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- roworderv(copy(mtcDT), cols = 1:2)
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- colorderv(copy(mtcDT))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- colorder(copy(mtcDT), vs, cyl, am)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- frename(copy(mtcDT), carb = bla, mpg = x)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- frename(copy(mtcDT), toupper)
expect_silent(dt[, new := 1])
expect_output(print(dt))
setrename(dt, MPG = ABC, new = NEW)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- get_vars(copy(irisDT), 1:3)
expect_silent(dt[, new := 1])
expect_output(print(dt))
get_vars(dt, 1) <- irisDT$Species
expect_silent(dt[, new2 := 1])
expect_output(print(dt))
}
for(i in 1:n) {
get_vars(dt, 1) <- NULL
expect_silent(dt[, new3 := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- get_vars(irisDT, 1:3) %>% add_vars(gv(irisDT, 4))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
add_vars(dt) <- list(Sp = irisDT$Species)
expect_silent(dt[, new2 := 1])
expect_output(print(dt))
}
wldDT <- qDT(wlddev)
for(i in .c(num_vars, nv, cat_vars, char_vars, fact_vars, logi_vars, date_vars)) {
FUN <- match.fun(i)
dt <- FUN(irisDT)
expect_identical(names(dt), FUN(iris, "names"))
expect_silent(dt[, new := 1])
expect_output(print(dt))
rm(dt)
dt <- irisDT
eval(substitute(FUN(dt) <- NULL, list(FUN = as.name(i))))
expect_silent(dt[, new := 1])
expect_output(print(dt))
rm(dt)
dt <- FUN(wldDT)
expect_identical(names(dt), FUN(wlddev, "names"))
expect_silent(dt[, new := 1])
expect_output(print(dt))
rm(dt)
dt <- wldDT
eval(substitute(FUN(dt) <- NULL, list(FUN = as.name(i))))
expect_silent(dt[, new := 1])
expect_output(print(dt))
rm(dt)
}
for(i in 1:n) {
dt <- relabel(copy(wldDT), toupper)
expect_silent(dt[, new := 1])
expect_output(print(dt))
setrelabel(dt, PCGDP = "GRP per cap", LIFEEX = "LE")
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- qDT(qTBL(qDF(qDT(GGDC10S))))
expect_identical(names(dt), names(GGDC10S))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- fdroplevels(copy(wldDT))
expect_identical(names(dt), names(wlddev))
expect_true(!anyNA(vlabels(dt)))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
m <- qM(mtcars)
dt <- qDT(m)
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
expect_output(print(mtcDT[, qDT(pwcor(.SD)), by = cyl, .SDcols = c("mpg", "hp", "carb")]))
expect_output(print(melt(qDT(GGDC10S)[, qDT(pwcor(.SD)), by = .(Variable, Country), .SDcols = 6:15], 1:2)))
for(i in 1:n) {
dt <- as_character_factor(wldDT)
expect_identical(names(dt), names(wlddev))
expect_true(!anyNA(vlabels(dt)))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- as_character_factor(wldDT, keep.attr = FALSE)
expect_identical(names(dt), names(wlddev))
expect_true(anyNA(vlabels(dt)))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
options(warn = -1L)
for(i in 1:n) {
dt <- as_numeric_factor(wldDT)
expect_identical(names(dt), names(wlddev))
expect_true(!anyNA(vlabels(dt)))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- as_numeric_factor(wldDT, keep.attr = FALSE)
expect_identical(names(dt), names(wlddev))
expect_true(anyNA(vlabels(dt)))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
options(warn = 1L)
for(i in 1:n) {
dt <- collap(wldDT, ~ iso3c)
expect_identical(names(dt), names(wlddev))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- collapv(wldDT, 1)
expect_identical(names(dt), names(wlddev))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- collapg(gby(wldDT, 1))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- dapply(copy(mtcDT), log)
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- dapply(copy(mtcDT), log, return = "data.frame")
expect_identical(names(dt), names(mtcars))
expect_error(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
l <- rsplit(copy(mtcDT), ~cyl)
expect_silent(for(i in seq_along(l)) l[[i]][, new := 1])
expect_output(print(l))
expect_output(print(l[[1]]))
}
for(i in 1:n) {
dt <- unlist2d(l, DT = TRUE)
expect_silent(dt[, new45 := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- na_omit(copy(mtcDT), cols = 1:2)
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- na_omit(copy(mtcDT))
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- na_insert(copy(mtcDT))
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(wldDT)
vlabels(wldDT) <- NULL
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% add_stub("B")
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% add_stub("B") %>% rm_stub("B")
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% setRownames
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- copy(mtcDT) %>% frename(toupper) %>% setColnames(names(mtcars))
expect_identical(names(dt), names(mtcars))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- replace_NA(copy(wldDT), cols = is.numeric)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- replace_NA(copy(mtcDT), set = TRUE, cols = is.numeric)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- replace_Inf(copy(wldDT))
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- replace_outliers(copy(wldDT), 3)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- recode_num(copy(wldDT), `1` = 2)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- recode_char(copy(wldDT), Uganda = "UGA")
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
for(i in 1:n) {
dt <- pad(copy(mtcDT), 1:3)
expect_silent(dt[, new := 1])
expect_output(print(dt))
}
})
|
collateDaily <- function(directory = DAILY_DIR){
dateTime <- as.character(Sys.Date())
fname <- paste("CRN_Daily_", dateTime, ".dat", sep = "")
invname <- sub("dat","inv",fname)
filenames <- list.files(path = directory, full.names = TRUE, pattern = ".txt")
files <- basename(filenames)
directory <- data.frame(years = substr(files,10,13),stations = substr(files,15,100),stringsAsFactors=FALSE)
StationNames <- unique(directory$stations)
Metadata <- data.frame(WBANNO = rep(NA,length(StationNames)),Lat = rep(NA,length(StationNames)),
Lon = rep(NA,length(StationNames)),
Name = rep(NA,length(StationNames)))
for ( thisStation in 1:length(StationNames)){
dex <- which(directory$stations == StationNames[thisStation])
fileList <- filenames[dex]
for (thisFile in 1: length(fileList)){
X <- read.table(fileList[thisFile], na.strings = -9999.0 )
X <- apply(X,MARGIN = 2, FUN = function(x) ifelse(x == -9999,NA,x))
write.table(X,file = fname,col.names = FALSE,row.names = FALSE,append = TRUE)
Metadata$WBANNO[thisStation] <- X[1,1]
Metadata$Lat[thisStation] <- X[1,5]
Metadata$Lon[thisStation] <- X[1,4]
Metadata$Name[thisStation] <- sub(".txt","",StationNames[thisStation])
}
}
write.table(Metadata,file = invname,col.names = TRUE,row.names = FALSE, quote = FALSE, sep = ",")
}
|
ic_bayes <- function(formula, data, logPriorFxn = function(x) return(0),
model = 'ph', dist = 'weibull', weights = NULL,
controls = bayesControls(), useMCores = F){
if(missing(data)) data <- environment(formula)
checkFor_cluster(formula)
reg_items = make_xy(formula, data)
x = reg_items$x
yMat = reg_items$y
xNames = reg_items$xNames
testMat <- cbind(x, 1)
invertResult <- try(diag(solve(t(testMat) %*% testMat )), silent = TRUE)
if(is(invertResult, 'try-error') & controls$useMLE_start){
errorMsg <- 'covariate matrix is computationally singular!'
errorMsg <- paste0(errorMsg, '\nic_bayes can still work with informative priors if useMLE_start = F in controls.')
errorMsg <- paste0(errorMsg, '\nSee ?bayesControls for more details')
stop( errorMsg )
}
modelName <- paste(dist, model, 'bayes')
callText <- match.call()
if(is.null(weights)) weights = rep(1, nrow(yMat))
if(length(weights) != nrow(yMat)) stop('weights improper length!')
if(min(weights) < 0) stop('negative weights not allowed!')
if(sum(is.na(weights)) > 0) stop('cannot have weights = NA')
covarOffset <- icColMeans(x)
x <- t(t(x) - covarOffset)
ans <- fit_bayes(yMat, x, parFam = dist, link = model,
leftCen = 0, rightCen = Inf, uncenTol = 10^-6,
regnames = xNames, weights = weights,
callText = callText, logPriorFxn = logPriorFxn,
bayesList = controls, modelName = modelName,
chains = controls$chains, use_m_cores = useMCores)
ans$model = model
call_base = match.call(expand.dots = FALSE)
call_info = readingCall(call_base)
ans$terms <- call_info$mt
ans$xlevels <- .getXlevels(call_info$mt, call_info$mf)
ans$formula <- formula
dataEnv <- new.env()
dataEnv$data <- data
ans$.dataEnv <- dataEnv
ans$covarOffset <- matrix(covarOffset, nrow = 1)
return(ans)
}
bayesControls <- function(samples = 4000, chains = 4,
useMLE_start = TRUE, burnIn = 2000,
samplesPerUpdate = 1000, initSD = 0.1,
updateChol = TRUE, acceptRate = 0.25,
thin = 5){
ans <- list(useMLE_start = useMLE_start,
chains = chains,
samples = samples,
thin = thin,
samplesPerUpdate = samplesPerUpdate,
updateChol = updateChol,
initSD = initSD,
burnIn = burnIn,
acceptRate = acceptRate
)
return(ans)
}
fit_bayes <- function(y_mat, x_mat, parFam, link,
leftCen = 0, rightCen = Inf,
uncenTol = 10^-6, regnames,
weights, callText, logPriorFxn,
bayesList, modelName,
chains = 4, use_m_cores = T){
parList<- make_par_fitList(y_mat, x_mat, parFam = parFam,
link = link, leftCen = 0, rightCen = Inf,
uncenTol = 10^-6, regnames = regnames,
weights = weights, callText = modelName)
bayesList$samples <- ceiling(bayesList$samples / chains)
`%myDo%` <- `%do%`
if(use_m_cores) `%myDo%` <- `%dopar%`
seeds = runif(chains, 1, 10000)
this_seed = NULL
c_fit_list <- foreach(this_seed = seeds) %myDo% {
set.seed(this_seed)
R_ic_bayes(bayesList, logPriorFxn, parList)
}
c_fit = c_fit_list[[1]]
mcmc_samples = mcmc.list()
allParNames = c(parList$bnames, parList$regnames)
logPostDens <- list()
for(i in 1:chains){
these_samps <- c_fit_list[[i]]$samples
colnames(these_samps) = allParNames
these_samps <- coda::mcmc(these_samps,
thin = bayesList$thin,
start = bayesList$burnIn + 1)
mcmc_samples[[i]] = these_samps
logPostDens[[i]] = c_fit_list[[i]]$logPosteriorDensity
}
nBase <- length(parList$bnames)
nRegPar <- length(parList$regnames)
fit <- new(modelName)
fit$par <- parFam
mat_samples <- as.matrix(mcmc_samples)
fit$baseline <- icr_colMeans(mat_samples[ ,1:nBase])
regParVec <- NULL
covMat <- NULL
if(nRegPar > 0) {
regParVec <- icr_colMeans(mat_samples[ ,nBase + 1:nRegPar])
}
fit$reg_pars <- regParVec
fit$nSamples <- nrow(mat_samples)
fit$var <- cov(mat_samples)
fit$mcmcList <- mcmc_samples
fit$logPosteriorDensities <- logPostDens
fit$ess <- coda::effectiveSize(mcmc_samples)
fit$call <- callText
fit$logPrior <- logPriorFxn
fit$finalChol <- c_fit$finalChol
MAP_info <- getMaxPostDensInfo(logPostDens)
MAP_dens <- MAP_info[1]
MAP_ind <- MAP_info[2]
fit$MAP_ind <- MAP_ind
fit$MAP_dens <- MAP_dens
fit$MAP_reg_pars <- mat_samples[fit$MAP_ind, nBase + seq_len(nRegPar)]
fit$MAP_baseline <- mat_samples[fit$MAP_ind, 1:nBase]
fit$samples <- mat_samples
if(nRegPar > 0) {
names(fit$reg_pars) <- parList$regnames
names(fit$MAP_reg_pars) <- parList$regnames
}
names(fit$baseline) <- parList$bnames
names(fit$MAP_baseline) <- parList$bnames
fit$coefficients <- c(fit$baseline, fit$reg_pars)
return(fit)
}
getMaxPostDensInfo <- function(maxDensList){
vals <- NULL
for(i in seq_along(maxDensList)){
these_dens <- maxDensList[[i]]
vals <- c(vals, these_dens)
}
maxVal <- max(vals)
maxValInd <- which(maxVal == maxVal)[1]
ans <- c(maxVal, maxValInd)
names(ans) <- c('maxVal', 'maxValInd')
return(ans)
}
postDens_at_mle <- function(mle_fit, priorFxn){
mle_vals <- coef(mle_fit)
llk <- mle_fit$llk
prior_dens <- priorFxn(mle_vals)
post_dens <- llk + prior_dens
ans <- c(post_dens, mle_vals)
return(ans)
}
|
loadBESymbols <- function(d, be="Gene", dbname){
beid <- paste0(be, "ID")
dColNames <- c("id", "symbol")
if(any(!dColNames %in% colnames(d))){
stop(paste(
"The following columns are missing:",
paste(setdiff(dColNames, colnames(d)), collapse=", ")
))
}
cql <- c(
'MERGE (s:BESymbol {value:row.symbol, value_up:upper(row.symbol)})'
)
bedImport(cql, unique(d[,"symbol", drop=FALSE]))
cql <- c(
sprintf(
'MATCH (beid:%s {value:row.id, database:"%s"}) USING INDEX beid:%s(value)',
beid, dbname, beid
),
'MATCH (s:BESymbol {value:row.symbol})',
'MERGE (beid)-[r:is_known_as]->(s)'
)
if("canonical" %in% colnames(d)){
cql <- c(
cql,
sprintf(
'SET r.canonical=%s',
'(case row.canonical when "TRUE" then true else false end)'
)
)
}else{
cql <- c(
cql,
'ON CREATE SET r.canonical=false'
)
}
bedImport(cql, d)
}
|
z <- zlognorm$new()
test.lognorm <- z$mcunit(minx=0, ci=0.99, nsim=1000, plot=FALSE)
expect_true(test.lognorm)
|
reduceResults = function(fun, ids = NULL, init, ..., reg = getDefaultRegistry()) {
assertRegistry(reg, sync = TRUE)
ids = convertIds(reg, ids, default = .findDone(reg = reg), keep.order = TRUE)
fun = match.fun(fun)
if (nrow(.findNotDone(reg, ids)))
stop("All jobs must be have been successfully computed")
if (nrow(ids) == 0L)
return(if (missing(init)) NULL else init)
fns = getResultFiles(reg, ids)
if (missing(init)) {
init = readRDS(fns[1L])
fns = fns[-1L]
if (length(fns) == 0L)
return(init)
}
pb = makeProgressBar(total = length(fns), format = "Reduce [:bar] :percent eta: :eta")
if ("job" %chin% names(formals(fun))) {
for (i in seq_along(fns)) {
init = fun(init, readRDS(fns[i]), job = makeJob(ids[i], reg = reg), ...)
pb$tick()
}
} else {
for (i in seq_along(fns)) {
init = fun(init, readRDS(fns[i]), ...)
pb$tick()
}
}
return(init)
}
reduceResultsList = function(ids = NULL, fun = NULL, ..., missing.val, reg = getDefaultRegistry()) {
assertRegistry(reg, sync = TRUE)
assertFunction(fun, null.ok = TRUE)
ids = convertIds(reg, ids, default = .findDone(reg = reg), keep.order = TRUE)
.reduceResultsList(ids, fun, ..., missing.val = missing.val, reg = reg)
}
reduceResultsDataTable = function(ids = NULL, fun = NULL, ..., missing.val, reg = getDefaultRegistry()) {
assertRegistry(reg, sync = TRUE)
ids = convertIds(reg, ids, default = .findDone(reg = reg))
assertFunction(fun, null.ok = TRUE)
results = .reduceResultsList(ids = ids, fun = fun, ..., missing.val = missing.val, reg = reg)
if (length(results) == 0L)
return(noIds())
ids[, "result" := results][]
}
.reduceResultsList = function(ids, fun = NULL, ..., missing.val, reg = getDefaultRegistry()) {
if (is.null(fun)) {
worker = function(.res, .job, ...) .res
} else {
fun = match.fun(fun)
if ("job" %chin% names(formals(fun)))
worker = function(.res, .job, ...) fun(.res, job = .job, ...)
else
worker = function(.res, .job, ...) fun(.res, ...)
}
results = vector("list", nrow(ids))
done = ids[.findDone(reg, ids), nomatch = 0L, which = TRUE, on = "job.id"]
if (missing(missing.val)) {
if (length(done) != nrow(ids))
stop("All jobs must be have been successfully computed")
} else {
results[setdiff(seq_row(ids), done)] = list(missing.val)
}
if (length(done) > 0L) {
fns = getResultFiles(reg, ids)
pb = makeProgressBar(total = length(fns), format = "Reducing [:bar] :percent eta: :eta")
reader = RDSReader$new(TRUE)
for (i in done) {
res = worker(readRDS(fns[i]), makeJob(ids$job.id[i], reader = reader, reg = reg), ...)
if (!is.null(res))
results[[i]] = res
rm(res)
pb$tick()
}
}
return(results)
}
|
LAScluster <- function(center, width, height, buffer, shape, files, name, proj4string = sp::CRS(), index = LIDRDEFAULTINDEX)
{
return(new("LAScluster", center, width, height, buffer, shape, files, name, "", proj4string, index))
}
|
cat("\n-------------------- Testing rprior --------------------")
rm(list = ls())
p.prior <- BuildPrior(
dists = c("tnorm", "tnorm", "beta", "tnorm", "beta", "beta"),
p1 = c(a = 1, v = 0, z = 1, sz = 1, sv = 1, t0 = 1),
p2 = c(a = 1, v = 2, z = 1, sz = 1, sv = 1, t0 = 1),
lower = c(0,-5, NA, NA, 0, NA),
upper = c(2, 5, NA, NA, 2, NA))
ggdmc:::rprior_mat(p.prior, 1)
ggdmc:::rprior_mat(p.prior, 2)
rprior(p.prior, 9)
|
line_ids <- c(1,1,1,1,1)
unique_ids <- sort( unique( line_ids ) )
expected <- matrix(c(0,4), ncol = 2)
res <- geometries:::rcpp_id_positions( line_ids, unique_ids )
expect_equal( res, expected )
line_ids <- c(1.1, 1.1)
unique_ids <- sort( unique( line_ids ) )
expected <- matrix(c(0,1), ncol = 2)
res <- geometries:::rcpp_id_positions( line_ids, unique_ids )
expect_equal( res, expected )
line_ids <- c("a")
unique_ids <- sort( unique( line_ids ) )
expected <- matrix(c(0,0), ncol = 2)
res <- geometries:::rcpp_id_positions( line_ids, unique_ids )
expect_equal( res, expected )
line_ids <- c("a","a")
unique_ids <- sort( unique( line_ids ) )
expected <- matrix(c(0,1), ncol = 2)
res <- geometries:::rcpp_id_positions( line_ids, unique_ids )
expect_equal( res, expected )
line_ids <- c(1,1,1,1,2,2,3,3,3,3)
unique_ids <- sort( unique( line_ids ) )
expected <- matrix(c(0,3,4,5,6,9), ncol = 2, byrow = T)
res <- geometries:::rcpp_id_positions( line_ids, unique_ids )
expect_equal( res, expected )
line_ids <- as.integer( c(1,1,1,1,2,2,1,3) )
unique_ids <- as.integer( sort( unique( line_ids ) ) )
expect_error( geometries:::rcpp_id_positions( line_ids, unique_ids ), "geometries - error indexing lines, perhaps caused by un-ordered data?")
line_ids <- c(1.1,1.1,1.1,2,2,1.1,3.1)
unique_ids <- sort( unique( line_ids ) )
expect_error( geometries:::rcpp_id_positions( line_ids, unique_ids ), "geometries - error indexing lines, perhaps caused by un-ordered data?")
|
getIsoEffD <-
function(D1=NULL, D2=NULL, fd1=NULL, fd2=NULL, ab=NULL) {
UseMethod("getIsoEffD")
}
getIsoEffD.default <-
function(D1=NULL, D2=NULL, fd1=NULL, fd2=NULL, ab=NULL) {
stopifnot(!is.null(D1), !is.null(fd1), !is.null(ab))
argL <- suppressWarnings(recycle(D1, D2, fd1, fd2, ab))
maxL <- max(sapply(argL, length))
D1 <- argL[[1]]
D2 <- argL[[2]]
fd1 <- argL[[3]]
fd2 <- argL[[4]]
ab <- argL[[5]]
keepD1 <- D1 > 0
keepFD1 <- fd1 > 0
keepAB <- ab > 0
if(any(!keepD1)) { warning("'D1' must be > 0") }
if(any(!keepFD1)) { warning("'fd1' must be > 0") }
if(any(!keepAB)) { warning("'ab' must be > 0") }
if(is.null(D2)) {
if(is.null(fd2)) { stop("'fd2' must be given to calculate 'D2'") }
keepFD2 <- fd2 > 0
if(any(!keepFD2)) { warning("'fd2' must be > 0") }
keep <- keepD1 & keepFD1 & keepFD2 & keepAB
D2 <- rep(NA_real_, times=maxL)
D2[keep] <- D1[keep] * (fd1[keep] + ab[keep]) / (fd2[keep] + ab[keep])
D2
} else if(is.null(fd2)) {
if(is.null(D2)) { stop("'D2' must be given to calculate 'fd2'") }
keepD2 <- D2 > 0
if(any(!keepD2)) { warning("'D2' must be > 0") }
keep <- keepD1 & keepD2 & keepFD1 & keepAB
fd2 <- rep(NA_real_, times=maxL)
fd2[keep] <- (D1[keep] / D2[keep]) * (fd1[keep] + ab[keep]) - ab[keep]
fd2
} else {
warning("either 'D2' or 'fd2' must be NULL")
rep(NA_real_, times=maxL)
}
}
getIsoEffD.DVHs <-
function(D1=NULL, D2=NULL, fd1=NULL, fd2=NULL, ab=NULL) {
stopifnot(!is.null(D1), !is.null(fd1), !is.null(fd2))
if(!is.null(D2)) { warning("'D2' is ignored for the 'DVHs' method") }
if(length(fd1) > 1L) {
warning("Only first element of 'fd1' will be used")
fd1 <- fd1[1]
}
if(length(fd2) > 1L) {
warning("Only first element of 'fd2' will be used")
fd2 <- fd2[1]
}
if(length(ab) > 1L) {
warning("Only first element of 'ab' will be used")
ab <- ab[1]
}
D1$dvh[ , "dose"] <- getIsoEffD(D1=D1$dvh[ , "dose"], fd1=fd1, fd2=fd2, ab=ab)
D1
}
getIsoEffD.DVHLst <-
function(D1=NULL, D2=NULL, fd1=NULL, fd2=NULL, ab=NULL) {
stopifnot(!is.null(D1))
IsoEDl <- Map(getIsoEffD, D1, fd1=list(fd1), fd2=list(fd2), ab=list(ab))
class(IsoEDl) <- "DVHLst"
attr(IsoEDl, which="byPat") <- attributes(D1)$byPat
IsoEDl
}
getIsoEffD.DVHLstLst <-
function(D1=NULL, D2=NULL, fd1=NULL, fd2=NULL, ab=NULL) {
stopifnot(!is.null(D1))
IsoEDll <- Map(getIsoEffD, D1, fd1=list(fd1), fd2=list(fd2), ab=list(ab))
class(IsoEDll) <- "DVHLstLst"
attr(IsoEDll, which="byPat") <- attributes(D1)$byPat
IsoEDll
}
|
data(blaTum)
fm <- PanelSurv(id, time, count) ~ num + size + treatment
fit1 <- panelReg(fm, data=blaTum, method = "AEE", se = "Sandwich")
plot(fit1)
fit2 <- panelReg(fm, data=blaTum, method = "MLs", se = "NULL")
plot(fit2)
|
rbind <- function(..., deparse.level = 1)
{
has.dl <- !missing(deparse.level)
deparse.level <- as.integer(deparse.level)
if(identical(deparse.level, -1L)) deparse.level <- 0L
stopifnot(0 <= deparse.level, deparse.level <= 2)
argl <- list(...)
na <- nargs() - has.dl
while(na > 0L && is.null(argl[[na]])) { argl <- argl[-na]; na <- na - 1L }
if(na == 0) return(NULL)
symarg <- as.list(substitute(list(...)))[-1L]
nmsym <- names(symarg)
nm <- c(
function(i) NULL,
function(i) if(is.symbol(s <- symarg[[i]])) deparse(s) else NULL,
function(i) deparse(symarg[[i]])[[1L]])[[ 1L + deparse.level ]]
Nms <- function(i) { if(!is.null(s <- nmsym[i]) && nzchar(s)) s else nm(i) }
if(na == 1) {
if(isS4(..1)) {
r <- rbind2(..1)
if(length(dim(..1)) < 2L && length(dim(r)) == 2L)
rownames(r) <- Nms(1)
return(r)
}
else return(.__H__.rbind(..., deparse.level = deparse.level))
}
if(na == 2) {
r <- ..2
fix.na <- FALSE
}
else {
nrs <- unname(lapply(argl, ncol))
iV <- vapply(nrs, is.null, NA)
fix.na <- identical(nrs[(na-1L):na], list(NULL,NULL))
if(fix.na) {
nr <- max(if(all(iV)) lengths(argl) else unlist(nrs[!iV]))
argl[[na]] <- rbind(rep(argl[[na]], length.out = nr),
deparse.level = 0)
}
if(fix.na)
fix.na <- !is.null(Nna <- Nms(na))
if(!is.null(nmi <- names(argl))) iV <- iV & (nmi == "")
ii <- if(fix.na)
2:(na-1) else 2:na
if(any(iV[ii])) {
for(i in ii[iV[ii]])
if (!is.null(nmi <- Nms(i))) names(argl)[i] <- nmi
}
r <- do.call(rbind, c(argl[-1L], list(deparse.level=deparse.level)))
}
d2 <- dim(r)
r <- rbind2(..1, r)
if(deparse.level == 0)
return(r)
ism1 <- !is.null(d1 <- dim(..1)) && length(d1) == 2L
ism2 <- !is.null(d2) && length(d2) == 2L && !fix.na
if(ism1 && ism2)
return(r)
Nrow <- function(x) {
d <- dim(x); if(length(d) == 2L) d[1L] else as.integer(length(x) > 0L) }
nn1 <- !is.null(N1 <- if((l1 <- Nrow(..1)) && !ism1) Nms(1))
nn2 <- !is.null(N2 <- if(na == 2 && Nrow(..2) && !ism2) Nms(2))
if(nn1 || nn2 || fix.na) {
if(is.null(rownames(r)))
rownames(r) <- rep.int("", nrow(r))
setN <- function(i, nams)
rownames(r)[i] <<- if(is.null(nams)) "" else nams
if(nn1) setN(1, N1)
if(nn2) setN(1+l1, N2)
if(fix.na) setN(nrow(r), Nna)
}
r
}
|
context("test Projectile with loop")
source(paste(system.file("examples", package = "rODE"),
"Projectile.R", sep ="/"))
x <- 0; vx <- 10; y <- 0; vy <- 10
state <- c(x, vx, y, vy, 0)
dt <- 0.01
projectile <- Projectile()
expect_equal(getStepSize(projectile@odeSolver), 0.1)
expect_true(length(projectile@state) == 0)
expect_true(length(projectile@rate) == 0)
expect_true(all(projectile@odeSolver@ode@state == state))
expect_true(length(projectile@odeSolver@ode@rate) == 0)
projectile <- setState(projectile, x, vx, y, vy)
expect_equal(projectile@odeSolver@ode@state, state)
expect_equal(projectile@odeSolver@numEqn, 0)
expect_true(length(projectile@odeSolver@ode@rate) == 0)
projectile@odeSolver <- init(projectile@odeSolver, 0.123)
test_that("match these values after init", {
expect_equal(getStepSize(projectile@odeSolver), 0.123)
expect_equal(projectile@odeSolver@ode@state, state)
expect_equal(projectile@odeSolver@numEqn, 5)
})
projectile@odeSolver <- setStepSize(projectile@odeSolver, dt)
expect_equal(getStepSize(projectile@odeSolver), dt)
while (projectile@state[3] >= 0) {
projectile <- step(projectile)
expect_equal(projectile@odeSolver@numEqn, 5)
expect_equal(getStepSize(projectile@odeSolver), 0.01)
}
expect_equal(c(projectile@state[5], projectile@state[1], projectile@state[3]),
c( 2.050000, 20.500000, -0.0922500 ) )
|
context("Conversion Test")
mag <- maxample("pop")
test_that("conversions do not affect content", {
expect_identical(as.magpie(mag), mag)
expect_equivalent(as.magpie(as.array(mag)), mag)
expect_equivalent(as.magpie(as.data.frame(mag)), mag)
expect_null(as.magpie(NULL))
empty <- new("magpie", .Data = structure(logical(0), .Dim = c(0L, 1L, 1L),
.Dimnames = list(region = NULL, year = NULL,
data = NULL)))
expect_identical(as.magpie(data.frame()), empty)
df <- data.frame(x = 1:3, y = 4:6, row.names = c("a", "b", "c"))
expect_identical(as.magpie(df), as.magpie(as.matrix(df)))
ref <- new("magpie", .Data = structure(TRUE, .Dim = c(1L, 1L, 1L),
.Dimnames = structure(list("GLO", NULL, "1.a"),
.Names = c("fake", NA, NA))))
expect_identical(as.magpie(data.frame(a = TRUE)), ref)
skip_if_not_installed("quitte")
expect_equivalent(as.magpie(quitte::as.quitte(mag)), mag)
})
test_that("special cases work", {
a <- as.array(mag)
m2 <- as.magpie(a, spatial = 0, temporal = 0)
expect_equal(dim(m2), c(1, 1, 320))
names(dimnames(a)) <- NULL
attr(a, "sets") <- c("spatial", "temporal", "data")
m2 <- as.magpie(a)
expect_equal(unname(getSets(m2)), attr(a, "sets"))
bla <- data.frame(from = c("bla", "blub"), to = c("ble", "blo"), stringsAsFactors = FALSE)
bla2 <- data.frame(from = c("bla", "blub"), to = c("ble", "blo"), stringsAsFactors = TRUE)
blaExpect <- new("magpie",
.Data = structure(c("ble", "blo"),
.Dim = c(1L, 1L, 2L),
.Dimnames = list(region = "GLO", year = NULL,
from = c("bla", "blub"))))
expect_identical(as.magpie(bla), blaExpect)
expect_identical(as.magpie(bla2), blaExpect)
})
test_that("underscores are preserved", {
mag2 <- maxample("pop")
getCells(mag2) <- paste(getCells(mag2), 1:ncells(mag2), sep = "_")
arr <- as.array(mag2)
mag3 <- as.magpie(arr, spatial = 1)
expect_identical(getCells(mag3), getCells(mag2))
})
test_that("error detection works", {
expect_warning(m <- as.magpie(array(1, c(2, 2), list(c("ABC", "DEF"), c("GHI", "JKL")))), "No clear mapping")
ref <- new("magpie", .Data = structure(c(1, 1, 1, 1), .Dim = c(2L, 1L, 2L),
.Dimnames = structure(list(c("ABC", "DEF"), NULL, c("GHI", "JKL")),
.Names = c("fake", NA, NA))))
expect_identical(m, ref)
skip_if_not_installed("quitte")
qi <- quitte::as.quitte(mag)
qi$unit <- NULL
expect_warning(as.magpie(qi), "does not follow the full quitte class definition")
})
|
context("Results Manager")
test_that("initialization and parameter setting", {
TEST_DIRECTORY <- test_path("test_inputs")
results_manager <- ResultsManager$new()
expect_error(results_manager$simulation_results <- "dummy",
"The simulation results must be a object of a class inherited from the SimulationResults class")
expect_silent(results_manager$simulation_results <- NULL)
expect_error(results_manager$summary_functions <- "dummy", "Summary metric functions should be a list")
expect_error(results_manager$summary_functions <- list(time_steps = "all$time_steps"),
"Could not assign function all$time_steps", fixed = TRUE)
expect_error(results_manager$summary_functions <- list(test = "dummy"), "Could not assign function dummy")
expect_error(results_manager$summary_functions <- list(test = file.path(TEST_DIRECTORY, "test_not_a_function.R")),
paste("Could not assign function", file.path(TEST_DIRECTORY, "test_not_a_function.R")))
expect_error(results_manager$summary_functions <- list(test = file.path(TEST_DIRECTORY, "no_such_file.R")),
paste("Could not assign function", file.path(TEST_DIRECTORY, "no_such_file.R")))
expect_silent(results_manager$summary_functions <- NULL)
expect_error(results_manager$result_attachment_functions <- "dummy", "Result attachment functions should be a list")
expect_error(results_manager$result_attachment_functions <- list(test = "dummy"), "Could not assign function dummy")
expect_error(results_manager$result_attachment_functions <- list(test = file.path(TEST_DIRECTORY, "test_not_a_function.R")),
paste("Could not assign function", file.path(TEST_DIRECTORY, "test_not_a_function.R")))
expect_error(results_manager$result_attachment_functions <- list(test = file.path(TEST_DIRECTORY, "no_such_file.R")),
paste("Could not assign function", file.path(TEST_DIRECTORY, "no_such_file.R")))
expect_silent(results_manager$result_attachment_functions <- NULL)
results_manager$simulation_results <- SimulationResults$new(time_steps = 10)
results_manager$summary_metrics <- "dummy"
results_manager$summary_functions <- list(constant = 22, time_steps = "all$time_steps",
bad_test = file.path(TEST_DIRECTORY, "test_function_2.R"),
total_n = file.path(TEST_DIRECTORY, "test_metrics_function.R"),
min_results = "min", max_results = max,
abundance = function(results) return(results$all$get_attribute("abundance")))
expect_named(results_manager$summary_functions, c("constant", "time_steps", "bad_test", "total_n", "min_results",
"max_results", "abundance"))
expect_equal(results_manager$summary_functions$constant, 22)
expect_equal(results_manager$summary_functions[c("time_steps", "min_results", "max_results")],
list(time_steps = "all$time_steps", min_results = "min", max_results = "max"))
expect_is(results_manager$summary_functions$bad_test, "function")
expect_is(results_manager$summary_functions$total_n, "function")
expect_is(results_manager$summary_functions$abundance, "function")
expect_equal(results_manager$summary_metrics, c("dummy", "constant", "time_steps", "bad_test", "total_n", "min_results",
"max_results", "abundance"))
expect_error(results_manager <- ResultsManager$new(simulation_manager = "dummy"),
"Simulation manager must be a GenericManager or inherited class object")
sim_manager <- SimulationManager$new(sample_data = data.frame(a = 1:3, b = 4:6),
generators = list(Generator$new()),
parallel_cores = 4,
results_dir = test_path("test_results"),
results_filename_attributes = "test")
results_manager <- ResultsManager$new(simulation_manager = sim_manager)
expect_equal(results_manager$sample_data, data.frame(a = 1:3, b = 4:6))
expect_is(results_manager$generators[[1]], "Generator")
expect_equal(results_manager$parallel_cores, 4)
expect_equal(results_manager$results_dir, test_path("test_results"))
expect_equal(results_manager$results_filename_attributes, "test")
})
test_that("attempt generation with incomplete/invalid attributes", {
TEST_DIRECTORY <- test_path("test_results")
results_manager <- ResultsManager$new()
expect_error(results_manager$generate(),
paste("Summary metrics generation requires parameters to be set first:",
"sample_data, simulation_results, summary_metrics, summary_matrices, summary_functions"))
results_manager$sample_data <- data.frame(a = 1:3, b = 4:6)
results_manager$simulation_results <- SimulationResults$new(time_steps = 10)
results_manager$summary_functions <- list(constant = 22, time_steps = "all$time_steps")
expect_error(results_manager$generate(), "No directory set for reading results")
expect_error(results_manager$generate(results_dir = "G:\\no_such_drive\\"),
"Could find the results directory G:\\no_such_drive\\", fixed = TRUE)
results_manager$results_dir <- TEST_DIRECTORY
results_manager$results_filename_attributes <- c("dummy")
expect_error(results_manager$generate(),
paste0("Could not find (first) results file ", file.path(TEST_DIRECTORY, "dummy_1_results.RData"),
". Ensure results_filename_attributes is set to match the filename pattern"), fixed = TRUE)
})
test_that("calculate summaries", {
simulation_results <- SimulationResults$new(time_steps = 10, default = "all$abundance",
results = file.path(test_path("test_results"), "sample_1_results.RData"))
results_manager <- ResultsManager$new(simulation_results = simulation_results)
results_manager$summary_functions <- list(constant = 22, time_steps = "all$time_steps",
bad_test = file.path(test_path("test_inputs"), "test_function_2.R"),
total_n = file.path(test_path("test_inputs"), "test_metrics_function.R"),
min_results = "min", max_results = max,
abundance = function(results) return(results$all$get_attribute("abundance")),
warn = function(results) { warning("test"); 11 })
calculated_summaries <- results_manager$calculate_summaries(simulation_results, 1)
expect_named(calculated_summaries, c("successful", "message", "errors", "warnings", "summary_metric_data"))
expect_false(calculated_summaries$successful)
expect_equal(calculated_summaries$message, "Summaries calculated with errors and warnings for sample 1")
expect_match(calculated_summaries$errors[1], "Error encountered setting metric bad_test")
expect_match(calculated_summaries$errors[2], "Metric function defined for abundance produces multiple values")
expect_match(calculated_summaries$warnings[1], "Warning encountered setting metric warn")
expect_equal(calculated_summaries$summary_metric_data,
data.frame(index = 1, constant = 22, time_steps = 10, bad_test = NA,
total_n = sum(simulation_results$attached$abundance),
min_results = min(simulation_results$all$attached$abundance),
max_results = max(simulation_results$all$attached$abundance),
abundance = NA, warn = 11))
simulation_results$error_messages <- NULL
simulation_results$warning_messages <- NULL
results_manager$summary_metrics <- c("constant", "time_steps", "total_n", "min_results", "max_results")
calculated_summaries <- results_manager$calculate_summaries(simulation_results, 1)
expect_equal(calculated_summaries,
list(successful = TRUE, message = "Summaries calculated for sample 1",
summary_metric_data = data.frame(index = 1, constant = 22, time_steps = 10,
total_n = sum(simulation_results$attached$abundance),
min_results = min(simulation_results$all$attached$abundance),
max_results = max(simulation_results$all$attached$abundance))))
results_manager$summary_metrics <- c("constant", "time_steps")
results_manager$summary_matrices <- "abundance"
calculated_summaries <- results_manager$calculate_summaries(simulation_results, 1)
expect_equal(calculated_summaries,
list(successful = TRUE, message = "Summaries calculated for sample 1",
summary_metric_data = data.frame(index = 1, constant = 22, time_steps = 10),
summary_matrix_list = list(abundance = simulation_results$all$attached$abundance)))
})
test_that("calculate result attachments", {
simulation_results <- SimulationResults$new(time_steps = 10, default = "all$abundance",
results = file.path(test_path("test_results"), "sample_1_results.RData"))
results_manager <- ResultsManager$new(simulation_results = simulation_results)
results_manager$result_attachment_functions <- list(bad_test = file.path(test_path("test_inputs"), "test_function_2.R"),
total_n = file.path(test_path("test_inputs"), "test_metrics_function.R"),
min_results = "min", max_results = max,
abund_all = function(results) return(results$all$get_attribute("abundance")),
warn = function(results) { warning("test"); 11 })
results_manager$calculate_result_attachments(simulation_results)
expect_named(simulation_results$attached, c("abundance", "total_n", "min_results", "max_results", "abund_all", "warn"))
expect_equal(simulation_results$attached[c("total_n", "min_results", "max_results", "abund_all", "warn")],
list(total_n = sum(simulation_results$attached$abundance),
min_results = min(simulation_results$all$attached$abundance),
max_results = max(simulation_results$all$attached$abundance),
abund_all = simulation_results$all$attached$abundance,
warn = 11))
expect_match(simulation_results$error_messages[1], "Error encountered attaching bad_test to results")
expect_match(simulation_results$warning_messages[1], "Warning encountered attaching warn to results")
})
test_that("summary generation", {
invisible(file.copy(test_path("test_results"), tempdir(), recursive = TRUE))
TEST_DIRECTORY <- file.path(tempdir(), "test_results")
abundance <- list(SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_1_results.RData"))$attached$abundance,
SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_2_results.RData"))$attached$abundance,
SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_3_results.RData"))$attached$abundance)
abundance_all <- lapply(abundance, colSums)
results_manager <- ResultsManager$new(simulation_results = SimulationResults$new(time_steps = 10, default = "all$abundance"),
results_dir = TEST_DIRECTORY)
results_manager$summary_matrices <- "abundance"
results_manager$summary_functions <- list(constant = 22, time_steps = "all$time_steps",
total_n = file.path(test_path("test_inputs"), "test_metrics_function.R"),
min_results = "min", max_results = max,
abundance = function(results) return(results$all$get_attribute("abundance")))
results_manager$sample_data <- data.frame(sample = 1:4)
generation_output <- results_manager$generate()
expect_named(generation_output, c("summary", "failed_indices", "warning_indices", "full_log"))
expect_equal(generation_output[c("summary", "failed_indices", "warning_indices")],
list(summary = "3 of 4 summary metrics/matrices generated from sample results successfully",
failed_indices = 4, warning_indices = NULL))
expect_length(generation_output[["full_log"]], 4)
for (i in 1:3) {
expect_true(generation_output$full_log[[i]][["successful"]])
expect_equal(generation_output$full_log[[i]][["message"]], sprintf("Summaries calculated for sample %s", i))
expect_named(generation_output$full_log[[i]][["summary_metric_data"]],
c("index", "constant", "time_steps", "total_n", "min_results", "max_results"))
}
expect_false(generation_output$full_log[[4]][["successful"]])
expect_equal(generation_output$full_log[[4]][["message"]], "Could not find result file for sample 4")
expect_equal(generation_output$full_log[[4]][["errors"]], "Result file not found")
expect_equal(generation_output$full_log[[4]][["summary_metric_data"]],
data.frame(index = 4, constant = NA, time_steps = NA, total_n = NA, min_results = NA, max_results = NA))
expect_equal(results_manager$summary_metric_data,
data.frame(index = 1:4, constant = c(22, 22, 22, NA), time_steps = c(10, 10, 10, NA),
total_n = c(unlist(lapply(abundance_all, sum)), NA),
min_results = c(unlist(lapply(abundance_all, min)), NA),
max_results = c(unlist(lapply(abundance_all, max)), NA)))
expect_equal(results_manager$summary_matrix_list,
list(abundance = matrix(c(unlist(abundance_all), rep(NA, 10)), nrow = 4, ncol = 10, byrow = TRUE)))
expect_true(file.exists(file.path(results_manager$results_dir, "generation_log.txt")))
generator <- Generator$new(generative_requirements = list(occup_mask = "function"),
inputs = c("a"), outputs = c("occup_mask"))
generator$function_templates <- list(occup_mask = list(function_def = function(params) { m = rep(1, 5); m[params$a] <- 0; m },
call_params = c("a")))
results_manager$generators <- list(gen1 = generator)
results_manager$sample_data <- data.frame(sample = 1:4, a = 1:4)
results_manager$simulation_results$attribute_aliases <- list(occup_mask = "occupancy_mask")
results_manager$result_attachment_functions <- list(gen_mask = function(r) return(r$get_attribute("occup_mask")))
results_manager$summary_matrices <- c("abundance", "occupancy", "mask")
results_manager$summary_functions$occupancy <- function(r) return(colSums(+(r$get_attribute("abundance") > 0)))
results_manager$summary_functions$mask <- function(r) return(r$get_attribute("gen_mask"))
generation_output <- results_manager$generate()
expect_equal(generation_output[c("summary", "failed_indices", "warning_indices")],
list(summary = "3 of 4 summary metrics/matrices generated from sample results successfully",
failed_indices = 4, warning_indices = NULL))
abundance_masked <- list()
masks <- list()
for (i in 1:3) {
masks[[i]] <- generator$function_templates$occup_mask$function_def(list(a = i))
abundance_masked[[i]] <- abundance[[i]]*masks[[i]]
}
abundance_all_masked <- lapply(abundance_masked, colSums)
expect_equal(results_manager$summary_metric_data,
data.frame(index = 1:4, constant = c(22, 22, 22, NA), time_steps = c(10, 10, 10, NA),
total_n = c(unlist(lapply(abundance_all_masked, sum)), NA),
min_results = c(unlist(lapply(abundance_all_masked, min)), NA),
max_results = c(unlist(lapply(abundance_all_masked, max)), NA)))
expect_equal(results_manager$summary_matrix_list,
list(abundance = matrix(c(unlist(abundance_all_masked), rep(NA, 10)), nrow = 4, ncol = 10, byrow = TRUE),
occupancy = matrix(c(unlist(lapply(abundance_masked, function(a) colSums(a > 0))), rep(NA, 10)),
nrow = 4, ncol = 10, byrow = TRUE),
mask = matrix(c(unlist(masks), rep(NA, 5)), nrow = 4, ncol = 5, byrow = TRUE)))
})
test_that("calculate summary weighted averages", {
TEST_DIRECTORY <- test_path("test_results")
abundance <- list(SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_1_results.RData"))$attached$abundance,
SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_2_results.RData"))$attached$abundance,
SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_3_results.RData"))$attached$abundance)
abundance_all <- lapply(abundance, colSums)
results_manager <- ResultsManager$new()
expect_error(results_manager$calculate_summary_weighted_averages(), "No summary matrices have been generated")
results_manager$summary_matrix_list <- list(abundance = matrix(c(unlist(abundance_all), rep(NA, 10)),
nrow = 4, ncol = 10, byrow = TRUE))
expect_error(results_manager$calculate_summary_weighted_averages(), "Sample data with a weight column is required")
results_manager$sample_data <- data.frame(sample = 1:4, a = 1:4)
expect_error(results_manager$calculate_summary_weighted_averages(), "Sample data with a weight column is required")
results_manager$sample_data$weight <- c(3, 4, 5, 6)
expect_silent(results_manager$calculate_summary_weighted_averages())
expect_equal(results_manager$summary_matrix_weighted_averages,
list(abundance = colSums(results_manager$summary_matrix_list$abundance*results_manager$sample_data$weight/
sum(results_manager$sample_data$weight), na.rm = TRUE)))
expect_error(results_manager$calculate_summary_weighted_averages(na_replacements = "dummy"),
"NA replacements must be a list of values or functions")
expect_silent(results_manager$calculate_summary_weighted_averages(na_replacements = list(abundance = 100)))
expect_equal(results_manager$summary_matrix_weighted_averages,
list(abundance = colSums(rbind(results_manager$summary_matrix_list$abundance[1:3, ], rep(100, 10))
*results_manager$sample_data$weight/sum(results_manager$sample_data$weight))))
na_function <- function(a) { a[which(is.na(a))] <- apply(a, 2, min, na.rm = NA); a }
expect_silent(results_manager$calculate_summary_weighted_averages(na_replacements = list(abundance = na_function)))
expect_equal(results_manager$summary_matrix_weighted_averages,
list(abundance = colSums(results_manager$summary_matrix_list$abundance[c(1:3, 1), ]
*results_manager$sample_data$weight/sum(results_manager$sample_data$weight))))
})
test_that("summary generation via raster results", {
invisible(file.copy(test_path("test_results"), tempdir(), recursive = TRUE))
TEST_DIRECTORY <- file.path(tempdir(), "test_results", "raster")
finite_indices <- c(1, 3, 5, 8, 9)
abundance <- list(SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_1_raster_results.RData"))$attached$abundance[finite_indices],
SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_2_raster_results.RData"))$attached$abundance[finite_indices],
SimulationResults$new(results = file.path(TEST_DIRECTORY, "sample_3_raster_results.RData"))$attached$abundance[finite_indices])
abundance_all <- lapply(abundance, colSums)
results_manager <- ResultsManager$new(simulation_results = SimulationResults$new(time_steps = 10),
sample_data = data.frame(sample = 1:3),
results_filename_attributes = c("sample", "raster_results"),
results_dir = TEST_DIRECTORY)
results_manager$summary_matrices <- "abundance"
results_manager$summary_functions <- list(abundance = function(results) return(results$all$get_attribute("abundance")))
generation_output <- results_manager$generate()
expect_equal(generation_output[c("summary", "failed_indices", "warning_indices")],
list(summary = "3 of 3 summary metrics/matrices generated from sample results successfully",
failed_indices = integer(0), warning_indices = NULL))
expect_length(generation_output[["full_log"]], 3)
expect_equal(results_manager$summary_metric_data, data.frame(index = 1:3))
expect_equal(results_manager$summary_matrix_list,
list(abundance = matrix(unlist(abundance_all), nrow = 3, ncol = 10, byrow = TRUE)))
})
|
var1stock <-
function(){
my.draw <- function(panel) {
price <-as.numeric(panel$price)
mu <- as.numeric(panel$mu)
sigma <- as.numeric(panel$sigma)
conf <- as.numeric(panel$conf)
horizon <- as.numeric(panel$horizon)/12
if (panel$distribution=="normal"){
var= (qnorm(conf)*sigma*sqrt(horizon)-mu)*price
}
else{
var= (1-(exp(mu-qnorm(conf)*sigma*sqrt(horizon)))) *price
}
var<-round(var,2)
plot(1:20, 1:20, type="n", xlab="", ylab="",
axes=FALSE, frame = TRUE)
text(10, 10, paste("VaR= ", var, sep=""),cex=1.5)
panel
}
my.redraw <- function(panel) {
rp.tkrreplot(panel, my.tkrplot)
panel
}
my.panel <- rp.control(title = "Value at Risk- single stock/Portfolio")
rp.textentry(panel=my.panel,variable=price,labels="Value: ",action=my.redraw,initval=120)
rp.textentry(panel=my.panel,variable=mu,labels="mu: ",action=my.redraw,initval=0.10)
rp.textentry(panel=my.panel,variable=sigma,labels="sigma: ",action=my.redraw,initval=0.30)
rp.textentry(panel=my.panel,variable=conf,labels="Conf level: ",action=my.redraw,initval=0.95)
rp.doublebutton(my.panel,variable=horizon,step=1,title="Horizon (Months)",initval=12,showvalue=T,range=c(1,12),action=my.redraw)
rp.radiogroup(panel = my.panel, variable= distribution,
vals = c("normal", "log-normal"),
action = my.redraw, title = "Distribution")
rp.tkrplot(panel = my.panel, name = my.tkrplot, plotfun = my.draw)
}
|
drake_context("keras")
if (FALSE) {
test_with_dir("custom keras format", {
skip_on_cran()
skip_if_not_installed("keras")
keras_model <- function() {
model <- keras_model_sequential() %>%
layer_conv_2d(
filters = 32,
kernel_size = c(3, 3),
activation = "relu",
input_shape = c(28, 28, 1)
) %>%
layer_conv_2d(
filters = 64,
kernel_size = c(3, 3),
activation = "relu"
) %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_dropout(rate = 0.25) %>%
layer_flatten() %>%
layer_dense(units = 128, activation = "relu") %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 10, activation = "softmax")
compile(
model,
loss = "categorical_crossentropy",
optimizer = optimizer_adadelta(),
metrics = c("accuracy")
)
model
}
plan <- drake_plan(x = target(keras_model(), format = "keras"))
make(plan, packages = "keras")
out <- readd(x)
expect_true(inherits(out, "keras.engine.training.Model"))
cache <- drake_cache()
expect_true(
inherits(
cache$get_value(cache$get_hash("x")),
"keras.engine.training.Model"
)
)
ref <- cache$storr$get("x")
expect_true(inherits(ref, "drake_format_keras"))
expect_equal(length(ref), 1L)
expect_true(nchar(ref) < 100)
expect_false(is.list(ref))
})
test_with_dir("keras + clustermq", {
skip_on_cran()
skip_if_not_installed("clustermq")
skip_if_not_installed("keras")
skip_on_os("windows")
if ("package:clustermq" %in% search()) {
detach("package:clustermq", unload = TRUE)
}
options(clustermq.scheduler = "multicore")
keras_model <- function() {
model <- keras_model_sequential() %>%
layer_conv_2d(
filters = 32,
kernel_size = c(3, 3),
activation = "relu",
input_shape = c(28, 28, 1)
) %>%
layer_conv_2d(
filters = 64,
kernel_size = c(3, 3),
activation = "relu"
) %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_dropout(rate = 0.25) %>%
layer_flatten() %>%
layer_dense(units = 128, activation = "relu") %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 10, activation = "softmax")
compile(
model,
loss = "categorical_crossentropy",
optimizer = optimizer_adadelta(),
metrics = c("accuracy")
)
model
}
plan <- drake_plan(x = target(keras_model(), format = "keras"))
make(
plan,
packages = "keras",
parallelism = "clustermq",
caching = "main"
)
out <- readd(x)
expect_true(inherits(out, "keras.engine.training.Model"))
cache <- drake_cache()
expect_true(
inherits(
cache$get_value(cache$get_hash("x")),
"keras.engine.training.Model"
)
)
ref <- cache$storr$get("x")
expect_true(inherits(ref, "drake_format_keras"))
expect_equal(length(ref), 1L)
expect_true(nchar(ref) < 100)
expect_false(is.list(ref))
if ("package:clustermq" %in% search()) {
detach("package:clustermq", unload = TRUE)
}
})
test_with_dir("keras + future", {
skip_on_cran()
skip_if_not_installed("future")
skip_if_not_installed("keras")
skip_on_os("windows")
future::plan(future::sequential)
keras_model <- function() {
model <- keras_model_sequential() %>%
layer_conv_2d(
filters = 32,
kernel_size = c(3, 3),
activation = "relu",
input_shape = c(28, 28, 1)
) %>%
layer_conv_2d(
filters = 64,
kernel_size = c(3, 3),
activation = "relu"
) %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_dropout(rate = 0.25) %>%
layer_flatten() %>%
layer_dense(units = 128, activation = "relu") %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 10, activation = "softmax")
compile(
model,
loss = "categorical_crossentropy",
optimizer = optimizer_adadelta(),
metrics = c("accuracy")
)
model
}
plan <- drake_plan(x = target(keras_model(), format = "keras"))
make(
plan,
packages = "keras",
parallelism = "future",
caching = "worker"
)
out <- readd(x)
expect_true(inherits(out, "keras.engine.training.Model"))
cache <- drake_cache()
expect_true(
inherits(
cache$get_value(cache$get_hash("x")),
"keras.engine.training.Model"
)
)
ref <- cache$storr$get("x")
expect_true(inherits(ref, "drake_format_keras"))
expect_equal(length(ref), 1L)
expect_true(nchar(ref) < 100)
expect_false(is.list(ref))
})
}
|
library(hamcrest)
expected <- structure(c(0x1.388p+13 + 0x0p+0i, 0x1.d7f56950b1ffp+11 + -0x1.181403e53cfc6p+13i,
-0x1.86abcee3ac1dap+12 + -0x1.8ffb44f3cfba2p+12i, -0x1.c48bdb55a1b75p+12 + 0x1.64f4a7ddfebf7p+11i,
-0x1.1498b5f5dbb35p+7 + 0x1.905deb9bb3326p+12i, 0x1.1d402fa7ba6e2p+12 + 0x1.fac9eecc5c67p+10i,
0x1.4f61e7f3145ecp+11 + -0x1.3ce68ddcced64p+11i, -0x1.cab940aec81a3p+9 + -0x1.2f94c8a59ce22p+11i,
-0x1.b49e9563b403ap+10 + -0x1.00c78141c5cfep+6i, -0x1.e445cc85a75bbp+8 + 0x1.01288f3a90c73p+10i,
0x1.dcb588d2d0442p+8 + 0x1.10dcbb1f4b2b4p+9i, 0x1.b847a360f120ap+8 + -0x1.0931adad71715p+7i,
0x1.57632774cd756p+5 + -0x1.237e295de8b34p+8i, -0x1.3d6c56ee8e08fp+7 + -0x1.b25355852b2aep+6i,
-0x1.c8d4b65519506p+6 + 0x1.e530e654de01ap+5i, -0x1.1b5eee2fb494p+2 + 0x1.7559a6bc8740fp+6i,
0x1.0177baf476c98p+6 + 0x1.6b5faf0191dd7p+5i, 0x1.1b3da5c4571a5p+6 + -0x1.1b8541dadfb6bp+5i,
-0x1.3bb2166e5cb1ap+3 + -0x1.5aa545af18674p+6i, -0x1.87a8c6b498e56p+6 + -0x1.753085c21fa94p+3i,
-0x1.fd33de3e36fc8p+4 + 0x1.b02fd9b11ec7cp+6i, 0x1.cf3d007f790c4p+6 + 0x1.bc683e911694fp+5i,
0x1.514a6b440196ep+6 + -0x1.c826d90f686f8p+6i, -0x1.7d4be5f377be9p+6 + -0x1.bb32a399fd91p+6i,
-0x1.e532a8417874ep+6 + 0x1.ea380c80b70efp+5i, 0x1.9d65d48f556b8p+4 + 0x1.a9e60e4d56c64p+6i,
0x1.1bfdbe9ea125ap+6 + -0x1.da85baf5f969cp+2i, -0x1.f46fd2c6ecda6p+3 + -0x1.0532a587e4af9p+5i,
-0x1.5ccd2b08e1482p+3 + 0x1.61163325c38a7p+5i, 0x1.2c554377d84c4p+6 + 0x1.d083ec333fc58p+3i,
0x1.2c384a42bb923p+5 + -0x1.6bc6dc9d8bce4p+6i, -0x1.50de162a670f6p+6 + -0x1.0337a0b060144p+6i,
-0x1.46b9f129764b4p+6 + 0x1.de780adb6d786p+5i, 0x1.ea8731dc4f862p+4 + 0x1.4676700e7454ep+6i,
0x1.13963eda25d26p+6 + -0x1.3cd9a1b407076p+3i, -0x1.827c1d9450ba7p+1 + -0x1.bc271f975bfc6p+5i,
-0x1.a6915354cef21p+5 + 0x1.ffd724b912beep+1i, -0x1.23e64546342fp-1 + 0x1.efef498f50554p+5i,
0x1.1c1d74fc3c47ap+6 + 0x1.4174d9af2e21cp+4i, 0x1.8b680483375p+5 + -0x1.f9b1b883b0d42p+5i,
-0x1.0313092dd70b1p+5 + -0x1.1750a5cbbcdc8p+6i, -0x1.fc017787e3128p+5 + -0x1.0897778635958p+3i,
-0x1.103de4f021918p+5 + 0x1.e70fcef2679b8p+4i, -0x1.0bbebd132dbbcp+3 + 0x1.c6b91dc827233p+4i,
-0x1.22247f1998c84p+1 + 0x1.a4b3c49d9d69dp+4i, 0x1.884c73a3e41dcp+3 + 0x1.0c5539d5e6224p+5i,
0x1.572b0bf8f73f5p+5 + 0x1.54320dec1f373p+4i, 0x1.941876760fff2p+5 + -0x1.b3fd71aab1ff6p+4i,
-0x1.0d70b2c3a33e8p+1 + -0x1.e855552155eccp+5i, -0x1.cb1ad773aa667p+5 + -0x1.ee2b8ecacb35cp+3i,
-0x1.6a83cac6e739p+4 + 0x1.afc49bc60b214p+5i, 0x1.cd88c9d354f63p+5 + 0x1.f08527aa51af8p+4i,
0x1.9bf45e7907d76p+5 + -0x1.df38d40bde656p+5i, -0x1.69eaa51a349bp+5 + -0x1.4170a6416338bp+6i,
-0x1.8fb0bd05a3a85p+6 + 0x1.05cc5cc289ac6p+3i, -0x1.359a0fd34d3ffp+5 + 0x1.7417c69e5cab4p+6i,
0x1.d0f312e04160ep+5 + 0x1.21af7bb1d185dp+6i, 0x1.30d4d47f2ddaap+6 + -0x1.761a0d6031682p+3i,
0x1.7532f6c6f5b9ep+4 + -0x1.9778b02f02832p+5i, -0x1.9e0bb5045b3fp+3 + -0x1.063757576eeadp+5i,
-0x1.255b403d7634ep+4 + -0x1.25082ab8a13ffp+4i, -0x1.00057c224a747p+5 + -0x1.d148d1967fca9p+2i,
-0x1.eb30190cba462p+4 + 0x1.cf048843f44dep+4i, 0x1.02a7419d6e4b9p+4 + 0x1.68cbe9000c856p+5i,
0x1.8d922d432465ep+5 + 0x0p+0i, 0x1.02a7419d6e4f4p+4 + -0x1.68cbe9000c83p+5i,
-0x1.eb30190cba418p+4 + -0x1.cf048843f4536p+4i, -0x1.00057c224a74dp+5 + 0x1.d148d1967fb56p+2i,
-0x1.255b403d7638bp+4 + 0x1.25082ab8a13cap+4i, -0x1.9e0bb5045b445p+3 + 0x1.063757576eed4p+5i,
0x1.7532f6c6f5bc8p+4 + 0x1.9778b02f02846p+5i, 0x1.30d4d47f2ddbbp+6 + 0x1.761a0d6031673p+3i,
0x1.d0f312e04161p+5 + -0x1.21af7bb1d186p+6i, -0x1.359a0fd34d433p+5 + -0x1.7417c69e5cab6p+6i,
-0x1.8fb0bd05a3a8ap+6 + -0x1.05cc5cc289a1ap+3i, -0x1.69eaa51a3498ep+5 + 0x1.4170a6416339fp+6i,
0x1.9bf45e7907d9fp+5 + 0x1.df38d40bde64cp+5i, 0x1.cd88c9d354f89p+5 + -0x1.f08527aa51b49p+4i,
-0x1.6a83cac6e73dcp+4 + -0x1.afc49bc60b248p+5i, -0x1.cb1ad773aa6b4p+5 + 0x1.ee2b8ecacb39bp+3i,
-0x1.0d70b2c3a33e8p+1 + 0x1.e855552155eccp+5i, 0x1.9418767610012p+5 + 0x1.b3fd71aab1fb9p+4i,
0x1.572b0bf8f73ebp+5 + -0x1.54320dec1f3ap+4i, 0x1.884c73a3e41p+3 + -0x1.0c5539d5e6256p+5i,
-0x1.22247f1998ep+1 + -0x1.a4b3c49d9d691p+4i, -0x1.0bbebd132db23p+3 + -0x1.c6b91dc827221p+4i,
-0x1.103de4f0218f4p+5 + -0x1.e70fcef2679dfp+4i, -0x1.fc017787e3114p+5 + 0x1.08977786358bap+3i,
-0x1.0313092dd70bap+5 + 0x1.1750a5cbbcdcap+6i, 0x1.8b680483374c9p+5 + 0x1.f9b1b883b0d9fp+5i,
0x1.1c1d74fc3c488p+6 + -0x1.4174d9af2e186p+4i, -0x1.23e64546335a8p-1 + -0x1.efef498f5053ep+5i,
-0x1.a6915354cef16p+5 + -0x1.ffd724b912fcp+1i, -0x1.827c1d9451355p+1 + 0x1.bc271f975bf68p+5i,
0x1.13963eda25ceap+6 + 0x1.3cd9a1b4070e4p+3i, 0x1.ea8731dc4f85ep+4 + -0x1.4676700e74524p+6i,
-0x1.46b9f129764b4p+6 + -0x1.de780adb6d786p+5i, -0x1.50de162a670d5p+6 + 0x1.0337a0b060143p+6i,
0x1.2c384a42bb972p+5 + 0x1.6bc6dc9d8bccbp+6i, 0x1.2c554377d84e3p+6 + -0x1.d083ec333fd6cp+3i,
-0x1.5ccd2b08e1516p+3 + -0x1.61163325c38c3p+5i, -0x1.f46fd2c6ecd38p+3 + 0x1.0532a587e4b62p+5i,
0x1.1bfdbe9ea1286p+6 + 0x1.da85baf5f97cp+2i, 0x1.9d65d48f55738p+4 + -0x1.a9e60e4d56c54p+6i,
-0x1.e532a84178751p+6 + -0x1.ea380c80b70f1p+5i, -0x1.7d4be5f377c1cp+6 + 0x1.bb32a399fd932p+6i,
0x1.514a6b4401964p+6 + 0x1.c826d90f6872cp+6i, 0x1.cf3d007f790d6p+6 + -0x1.bc683e9116935p+5i,
-0x1.fd33de3e36f65p+4 + -0x1.b02fd9b11eca8p+6i, -0x1.87a8c6b498e76p+6 + 0x1.753085c21f94bp+3i,
-0x1.3bb2166e5cce2p+3 + 0x1.5aa545af18668p+6i, 0x1.1b3da5c45717dp+6 + 0x1.1b8541dadfb8cp+5i,
0x1.0177baf476c98p+6 + -0x1.6b5faf0191dd7p+5i, -0x1.1b5eee2fb4748p+2 + -0x1.7559a6bc8744cp+6i,
-0x1.c8d4b6551950ep+6 + -0x1.e530e654de084p+5i, -0x1.3d6c56ee8e0aap+7 + 0x1.b25355852b296p+6i,
0x1.57632774cd731p+5 + 0x1.237e295de8b48p+8i, 0x1.b847a360f1216p+8 + 0x1.0931adad71712p+7i,
0x1.dcb588d2d0443p+8 + -0x1.10dcbb1f4b2b5p+9i, -0x1.e445cc85a75bcp+8 + -0x1.01288f3a90c72p+10i,
-0x1.b49e9563b403ap+10 + 0x1.00c78141c5cfcp+6i, -0x1.cab940aec819ep+9 + 0x1.2f94c8a59ce24p+11i,
0x1.4f61e7f3145eep+11 + 0x1.3ce68ddcced64p+11i, 0x1.1d402fa7ba6e2p+12 + -0x1.fac9eecc5c674p+10i,
-0x1.1498b5f5dbb2fp+7 + -0x1.905deb9bb3326p+12i, -0x1.c48bdb55a1b75p+12 + -0x1.64f4a7ddfebf2p+11i,
-0x1.86abcee3ac1d7p+12 + 0x1.8ffb44f3cfba2p+12i, 0x1.d7f56950b1ff3p+11 + 0x1.181403e53cfc5p+13i,
0x1.5dec3e9d6f50bp+11 + -0x1.24788ad8552e4p+13i, -0x1.be98eb71cfb51p+12 + -0x1.7032fc2fcd6bp+12i,
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-0x1.3e104289a9346p+6 + 0x1.0e4901287772p+2i, -0x1.1c99add2b3b97p+5 + 0x1.30888886de0a5p+6i,
0x1.a7dd4d3420402p+5 + 0x1.df1a4e1ac2762p+5i, 0x1.0107184dbfcb6p+6 + -0x1.c6b11fb86f5a8p+4i,
-0x1.dcee9b7b3e798p+3 + -0x1.db95ca8caa004p+5i, -0x1.dd4644a7d7f7ap+5 + 0x1.613be0542bcc9p+3i,
0x1.7730762905114p+2 + 0x1.1ad7782fe978ep+6i, 0x1.5b716d472d348p+6 + 0x1.55dae9ef1903cp+3i,
0x1.3f05e866a625bp+5 + -0x1.80a49b1c3e5fep+6i, -0x1.6370195f5b869p+6 + -0x1.2402b49ce1ee4p+6i,
-0x1.7f698e0eab2dp+6 + 0x1.ff6d6238d998p+5i, 0x1.f27c035e5a6a3p+4 + 0x1.81a949b40613ep+6i,
0x1.2294fe5e89734p+6 + -0x1.dc880c1eb9b2p+2i, -0x1.ec741979ee30cp+2 + -0x1.1fd3282d636fdp+5i,
-0x1.a33058803a1b4p+2 + 0x1.1582e04b8d733p+5i, 0x1.2e7d3cf335d1p+6 + 0x1.4539ae5fc743p+0i,
0x1.6caa958d5d8d1p+4 + -0x1.b5e2d805f98d6p+6i, -0x1.e20f2cda562b6p+6 + -0x1.d322abe80e384p+5i,
-0x1.67498377edf73p+6 + 0x1.acf1cea9af3c7p+6i, 0x1.3fa6a9a8b4aaap+6 + 0x1.a5c1b3ca6d384p+6i,
0x1.a2e8ad32e86eep+6 + -0x1.95af6c198386ep+5i, -0x1.9f8a289f693p+4 + -0x1.76bf5685f5b24p+6i,
-0x1.37d5ebcef6fedp+6 + 0x1.76c49c27e23c4p+2i, -0x1.7e3c4d2bb3731p+3 + 0x1.dfc19a67a61f8p+5i,
0x1.3b416a206732bp+5 + 0x1.dee77bf4ac356p+4i, 0x1.9224cd80f596p+5 + -0x1.98884d7af8caep+3i,
0x1.c8eecad92cep+4 + -0x1.20a81653ec883p+6i, -0x1.5779d4a2369d8p+6 + -0x1.8683d2a7fd45ap+6i,
-0x1.9bd341958d44cp+7 + 0x1.0a0b5dc5c0ccep+6i, -0x1.d1bd447147a0cp+4 + 0x1.606c84fdc3a3ep+8i,
0x1.fb5eb0cc82a86p+8 + 0x1.051d3de28d3e5p+8i, 0x1.5a6f77cc92ec6p+9 + -0x1.2601a4792423ep+9i,
-0x1.b45f3a9f9924p+8 + -0x1.5120a4a587f34p+10i, -0x1.0afc620f15bdfp+11 + -0x1.4d0746bd8162p+7i,
-0x1.5fcf985a439e1p+10 + 0x1.5a8a881c987e6p+11i, 0x1.5d8dbe1fbd79dp+11 + 0x1.98cb1c7caf3bap+11i,
0x1.506c88693334ap+12 + -0x1.b213a2174bec2p+10i, 0x1.2a7ad2120b50ep+9 + -0x1.b6b06d2187ffdp+12i,
-0x1.cbb2278d3e6b8p+12 + -0x1.e1e1920d20f52p+11i, -0x1.be98eb71cfb4cp+12 + 0x1.7032fc2fcd6bp+12i
), .Dim = c(128, 128))
assertThat(stats:::fft(z=structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.321017080131018, 0.0537494299351407,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.535561807012201, 0.089671682921641, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.220728071992543, 0.334435544585439, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.223741263039349,
0.336724301468248, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.375834286381578, 0.0202712662066585, 0, 0, 0, 0.0182598350232762,
0.993450326647336, 0.874426385953958, 0.00266901126452313, 0.182786608883428,
0.405190260003368, 0.181993703813423, 0.0458957484629775, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.108588216495898, 1.78392299428442,
0.555512915876226, 0.00383955709680569, 0.122537764238404, 0.264903260019847,
1.41631948309593, 1.73428721646782, 1.89987832126288, 0.377045594721433,
1.10498727551916, 0.330670375161505, 0.616611505841862, 0.155499041881737,
0, 0, 0.109525069777302, 0.360220041524469, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00115321239964115,
0.0825870491410947, 0.792850307483952, 0.636638364904504, 0.562250036033826,
0.531710573039572, 1.53814076550788, 2.92556285735854, 1.24252115616403,
2.35505070039349, 2.11120354726619, 1.90988276016963, 0.888831928816078,
1.40916878374996, 0.0469931863948513, 0, 0, 0, 0, 0.123633439257062,
0.406621449441167, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.668273758455135, 0.0744082856845137, 0.37598735543569,
1.25967774150666, 2.15578423058138, 0.796882010271544, 2.75180072843666,
3.71160614321932, 3.74101367040991, 1.00274451031338, 1.14181084972698,
3.69074764507486, 2.84382686746405, 3.14230815432989, 2.52298369761854,
3.82215688151607, 1.15223396266472, 0.412088690050008, 0.566684472430305,
0.182436454208317, 0.447266772374073, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.231537626145241,
0.139219636136787, 1.52320153233285, 0.226075101111773, 0.811137107859513,
0.874265273050759, 4.19042473344123, 3.66876574027076, 4.59783485554014,
4.3955831978135, 3.08218627684469, 6.38417064171386, 5.17773239139394,
6.31154500378862, 4.91143002716004, 4.42990436490687, 2.53775887267624,
2.33203050889226, 0.796315216528896, 2.38891908864295, 1.97629523764209,
0.337698048214533, 0, 0.167158325557775, 0.386297864861856, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.143167815883686,
0.717459053128941, 0.877836733998247, 1.27475959433274, 3.41348264593947,
1.20739967125306, 1.46146421799247, 5.93558590568705, 8.28798561257856,
6.90561599238778, 4.48508269883706, 11.8437606128498, 8.01542569682379,
10.5163486245619, 8.79975961833868, 7.90773521883384, 5.27282783051549,
5.52748590457611, 3.41486005720636, 2.13227684496156, 1.43680233802754,
2.39334352655658, 0.166545397693214, 0.891982755611891, 0.334808631561633,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0876052774546863,
1.10152416511457, 0.379027947705976, 1.21067179783867, 1.73243513744642,
2.51449748808598, 4.07823045544784, 7.88348748512625, 7.33335037215739,
9.23470512954694, 11.9876582853299, 12.7415376234998, 15.104228645891,
17.4644286185147, 14.6991622506595, 13.9203758917047, 10.3669866331289,
9.46846285302447, 5.76312882689276, 5.70360708279593, 3.47900737088747,
1.91105853972744, 0.0413026732447284, 0.213237197934594, 0.0065152244722517,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.543606976933851,
0.507665074272761, 1.87360691803158, 4.41952302992857, 4.02859610590849,
8.71628370277877, 12.2550047147711, 10.942928257359, 17.9154951092244,
18.8325154061917, 17.586877508347, 22.3206132694977, 23.6606825059883,
20.1842436510217, 21.1574336112995, 20.8866377538921, 18.2296278819156,
9.65234337044745, 6.94357251904972, 7.14680945258352, 3.26173069098062,
0.471849525154883, 0.21176095475462, 0.227345993981828, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(128L, 128L
)))
, identicalTo( expected, tol = 1e-6 ) )
|
AIC.GNARfit <- function(object, ..., k=2){
stopifnot(is.GNARfit(object))
nnodes.in <- object$frbic$nnodes
alphas.in <- object$frbic$alphas.in
betas.in <- object$frbic$betas.in
fact.var <- object$frbic$fact.var
tot.time <- object$frbic$time.in
globalalpha <- object$frbic$globalalpha
dotarg <- list(...)
if(length(dotarg)!=0){
if(!is.null(names(dotarg))){
warning("... not used here, input(s) ", paste(names(dotarg), collapse=", "), " ignored")
}else{
warning("... not used here, input(s) ", paste(dotarg, collapse=", "), " ignored")
}
}
if(!is.null(fact.var)){
f.in <- length(unique(fact.var))
}else{
f.in <- 1
}
stopifnot(is.logical(globalalpha))
stopifnot(length(nnodes.in)==1)
stopifnot(floor(nnodes.in)==nnodes.in)
stopifnot(tot.time != 0)
tmp.resid <- residToMat(GNARobj=object, nnodes=nnodes.in)$resid
tmp.resid[is.na(tmp.resid)] <- 0
larg <- det((1/tot.time) * t(tmp.resid) %*% tmp.resid)
stopifnot(larg != 0)
tmp1 <- log(larg)
if(globalalpha){
tmp2 <- f.in * ( alphas.in + sum(betas.in) ) * k / tot.time
}else{
tmp2 <- ( ncol(tmp.resid) * alphas.in + sum(betas.in) ) * k / tot.time
}
return(tmp1 + tmp2)
}
|
ultimateOscillator <-
function(HLC, n=c(7,14,28), wts=c(4,2,1)) {
if(length(n) != 3 || length(wts) != 3)
stop("length(n) and length(wts) must both be 3")
HLC <- try.xts(HLC, error=as.matrix)
HLC.RECLASS <- attr(HLC, ".RECLASS")
attr(HLC, ".RECLASS") <- FALSE
atr <- ATR(HLC, n=1)
buyPressure <- HLC[,3] - atr[,'trueLow']
osc <- buyPressure * 0.0
for(i in 1:3) {
osc <- osc + wts[i] * (runSum(buyPressure, n[i]) / runSum(atr[,'tr'], n[i]))
}
osc <- 100.0 * osc / sum(wts)
attr(HLC, ".RECLASS") <- HLC.RECLASS
reclass(osc, HLC)
}
|
interALL<-function(d1,n1,d2,n2,rho1,rho2,B=NA,DB=NA,JC=FALSE,CI_Boot=NA,plot=FALSE,type="bca",Estimator=c("Copula","Cov","JDP","MLE"),show_progress=FALSE){
if(is.numeric(d1)){d1=d1}else{stop("d1 is not numeric")}
if(is.numeric(n1)){n1=n1}else{stop("n1 is not numeric")}
if(is.numeric(d2)){d2=d2}else{stop("d2 is not numeric")}
if(is.numeric(n2)){n2=n2}else{stop("n2 is not numeric")}
if(is.numeric(rho1)){rho1=rho1}else{stop("rho1 is not numeric")}
if(is.numeric(rho2)){rho2=rho2}else{stop("rho1 is not numeric")}
if(length(d1)==length(n1) && length(d2)==length(n2) && length(d1)==length(d2)){}else{stop("Input vectors do not have the same length")}
NT1=length(d1)
NT2=length(d2)
NE=length(Estimator)
PEST=data.frame(Estimator=Estimator,value=NA,Type=c("PEST"),correction=rep("none",NE),B=NA,DB=NA,CI_Boot=NA,lower_upper_ci=c(""),stringsAsFactors = FALSE)
JackEST=data.frame(Estimator=Estimator,value=NA,Type=c("CEST"),correction=rep("Jackknife",NE),B=NA,DB=NA,CI_Boot=NA,lower_upper_ci=c(""),stringsAsFactors = FALSE)
if(show_progress){
if(JC)
cat("original and jackknife corrected estimators\n")
else
cat("original estimators\n")
pb=txtProgressBar(style = 3)
}
for(i in 1:NE){
try({
temp=
switch (JackEST$Estimator[i],
"Copula" = interCopula(df1 = d1/n1,df2=d2/n2,JC = JC),
"Cov" = interCov(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,JC = JC),
"JDP" = interJDP(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,JC = JC),
"MLE" = interMLE(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,JC = JC),
NA
)
PEST$value[i]=temp$Original
if(JC)
JackEST$value[i]=temp$Jackknife
})
if(show_progress)
setTxtProgressBar(pb,value = i/NE)
}
if(show_progress)
cat("\n")
Estimators=PEST
if(JC)
Estimators=rbind(Estimators,JackEST)
if(!is.na(B)){
BootEST=data.frame(Estimator=Estimator,value=NA,Type=c("CEST"),correction=rep("Bootstrap",NE),B=B,DB=NA,CI_Boot=NA,lower_upper_ci=c(""),stringsAsFactors = FALSE)
LIBootEST=data.frame(Estimator=Estimator,value=NA,Type=c("IEST"),correction=rep("Bootstrap",NE),B=B,DB=NA,CI_Boot=CI_Boot,lower_upper_ci=rep("lower",NE),stringsAsFactors = FALSE)
UIBootEST=data.frame(Estimator=Estimator,value=NA,Type=c("IEST"),correction=rep("Bootstrap",NE),B=B,DB=NA,CI_Boot=CI_Boot,lower_upper_ci=rep("upper",NE),stringsAsFactors = FALSE)
if(show_progress){
cat("bootstrap corrected estimators\n")
pb=txtProgressBar(style = 3)
}
if(!is.na(CI_Boot)){
for(i in 1:NE){
try({
temp=
switch (LIBootEST$Estimator[i],
"Copula" = interCopula(df1 = d1/n1,df2=d2/n2,B = B,CI_Boot = CI_Boot),
"Cov" = interCov(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,B=B,CI_Boot = CI_Boot),
"JDP" = interJDP(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,B=B,CI_Boot = CI_Boot),
"MLE" = interMLE(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,B=B,CI = CI_Boot),
NA
)
BootEST$value[i]=temp$Bootstrap
LIBootEST$value[i]=temp$CI_Boot[1]
UIBootEST$value[i]=temp$CI_Boot[2]
})
if(show_progress)
setTxtProgressBar(pb,value = i/NE)
}
if(show_progress)
cat("\n")
Estimators=rbind(Estimators,BootEST,LIBootEST,UIBootEST)
}
else{
for(i in 1:NE){
try({
temp=
switch (LIBootEST$Estimator[i],
"Copula" = interCopula(df1 = d1/n1,df2=d2/n2,B = B),
"Cov" = interCov(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,B=B),
"JDP" = interJDP(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,B=B),
"MLE" = interMLE(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,B=B),
NA
)
BootEST$value[i]=temp$Bootstrap
})
if(show_progress)
setTxtProgressBar(pb,value = i/NE)
}
if(show_progress)
cat("\n")
Estimators=rbind(Estimators,BootEST)
}
}
if(all(!is.na(DB))){
DBootEST=data.frame(Estimator=Estimator,value=NA,Type=c("CEST"),correction=rep("Double Bootstrap",NE),B=DB[1],DB=DB[2],CI_Boot=NA,lower_upper_ci=c(""),stringsAsFactors = FALSE)
if(show_progress){
cat("double bootstrap corrected estimators\n")
pb=txtProgressBar(style = 3)
}
for(i in 1:NE){
try({
DBootEST$value[i]=
switch (DBootEST$Estimator[i],
"Copula" = interCopula(df1 = d1/n1,df2=d2/n2,DB = DB)$Double_Bootstrap,
"Cov" = interCov(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,DB=DB)$Double_Bootstrap,
"JDP" = interJDP(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,DB=DB)$Double_Bootstrap,
"MLE" = interMLE(d1 = d1,n1 = n1,d2 = d2,n2 = n2,rho1 = rho1,rho2 = rho2,DB=DB)$Double_Bootstrap,
NA
)
})
if(show_progress)
setTxtProgressBar(pb,value = i/NE)
}
if(show_progress)
cat("\n")
Estimators=rbind(Estimators,DBootEST)
}
if(plot==TRUE){
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
plots <- c(list(...), plotlist)
numPlots = length(plots)
if (is.null(layout)) {
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
for (i in 1:numPlots) {
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
DTS=cbind(d1,d2)
N=cbind(n1,n2)
NT=length(d1)
Time=rep(1:NT,2)
Defaults=c(DTS[,1],DTS[,2])
Sector=c(rep("S1",NT),rep("S2",NT))
Mean=apply(DTS,2,mean,na.rm=TRUE)
DTS_plot=ggplot(data.frame(Time=Time,Defaults=Defaults,Sector=Sector),aes(x=Time,y=Defaults,col=Sector))+
geom_line()+
geom_hline(data = data.frame(Sector=c("S1","S2"),Mean=Mean),mapping = aes(yintercept=Mean,col=Sector))+ylab("Default Time Series")
if(!is.na(CI_Boot)){
colnames(Estimators)<-c("Estimator","Estimate","Type","correction","B","DB","CI_Boot","CI")
Estimate=Estimators$Estimate
CI=Estimators$CI
correction=Estimators$correction
EST_plot=ggplot(data = Estimators,aes(x="",y=Estimate,shape=CI,col=correction))+theme_bw() +geom_point()+facet_grid(.~Estimator)+theme(axis.title.x = element_blank())+ylab("Inter Correlation")
}else{
colnames(Estimators)<-c("Estimator","Estimate","Type","correction","B","DB","CI_Boot","CI")
Estimate=Estimators$Estimate
correction=Estimators$correction
EST_plot=ggplot(data = Estimators,aes(x="",y=Estimate,col=correction))+theme_bw() +geom_point()+facet_grid(.~Estimator)+theme(axis.title.x = element_blank())+ylab("Inter Correlation")
}
multiplot(DTS_plot,EST_plot,layout = matrix(c(1,2,2,2),ncol = 1))
}
return(Estimators)
}
|
`Last modified` <- Name <- NULL
adj.p.value <- p.value <- comparison <- conf.high <- conf.low <- estimate <- statistic <- term <- std.error <- NULL
age_check <- overall_max <- max_age <- min_age <- max_age_locality <- min_age_locality <- NULL
age_type <- at_locality <- locality_longname <- mindat_id <- NULL
num_localities <- num_localities_element <- num_localities_mineral <- mineral_id <- mineral_name <- NULL
atomic_mass <- atomic_radius <- pauling <- element_table_group <- element_table_period <- number_of_protons <- element_metal_type <- element_density <- element_specific_heat <- element_hsab <- element_crust_percent_weight <- NULL
closeness <- network_degree <- network_degree_norm <- cov_pauling <- mean_pauling <- w_cov_pauling <- w_mean_pauling <- cluster_ID <- NULL
Element <- Mineral <- ima_chemistry <- rruff_chemistry <- NULL
color <- color.background <- color.border <- colour <- shape <- size <- NULL
font.color <- font.face <- font.size <- frame.color <- label.cex <- NULL
mean_font_size <- mean_size <- rescale_cex <- rescale_size <- NULL
element <- element_name <- chemistry_elements <- element_as_redox <- NULL
to <- from <- id <- label <- title <- group <- type <- both <- NULL
element_redox_mineral <- has_element <- element_redox_mineral_sign <- element_redox_network <- NULL
available <- num <- num_nodes <- NULL
rd <- ers <- redox <- thesign <- X2 <- X3 <- X4 <- NULL
V1 <- V2 <- x <- y <- contrast <- NULL
early_age <- late_age <- label_x <- label_y <- eon_era_name <- selected_age_range <- yend <- ymax <- ymin <- NULL
|
expected <- eval(parse(text="structure(c(NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_), .Dim = c(4L, 17L))"));
test(id=0, code={
argv <- eval(parse(text="list(NA_character_, 4L, 17L, FALSE, NULL, FALSE, FALSE)"));
.Internal(matrix(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]]));
}, o=expected);
|
center_at_origin <- function(hellip){
mu <- get(hellip,"center")
sig <- get(hellip,"shape")
c <- get(hellip,"size")
hellip2 <- hyperellipsoid(matrix(0, length(mu), 1), sig, c)
return(list(hellip2,mu))
}
|
changeContrast <- function(data,contr){
isfact <- sapply(data, function(c){is.factor(c)||is.character(c)})
for(i in c( 1:ncol(data))[isfact]){
data[ ,i] <- as.factor(data[ ,i])
data[ ,i] <- droplevels(data[ ,i])
contrasts(data[ ,i]) <- contr
}
return(data)
}
|
knitr::opts_chunk$set(
collapse = TRUE,
echo = FALSE,
comment = "
)
knitr::kable(nflreadr::dictionary_ff_rankings)
|
bhl_booksearch <- function(...) {
.Defunct(new = "bhl_publicationsearch", package = "rbhl",
msg = "see ?bhl_publicationsearch")
}
|
make.quasse <- function(tree, states, states.sd, lambda, mu,
control=NULL, sampling.f=NULL) {
cache <- make.cache.quasse(tree, states, states.sd, lambda, mu,
control, sampling.f)
all.branches <- make.all.branches.quasse(cache, cache$control)
rootfunc <- make.rootfunc.quasse(cache)
f.pars <- make.pars.quasse(cache)
ll <- function(pars, condition.surv=TRUE, root=ROOT.OBS,
root.f=NULL, intermediates=FALSE) {
pars2 <- f.pars(pars)
ans <- all.branches(pars2, intermediates)
rootfunc(ans, pars2, condition.surv, root, root.f, intermediates)
}
class(ll) <- c("quasse", "dtlik", "function")
ll
}
make.info.quasse <- function(lambda, mu, phy) {
if ( !is.null(lambda) )
argnames <- default.argnames.quasse(lambda, mu)
else
argnames <- NULL
list(name="quasse",
name.pretty="QuaSSE",
np=NA,
argnames=argnames,
ny=NA,
k=NA,
idx.e=NA,
idx.d=NA,
phy=phy,
ml.default="subplex",
mcmc.lowerzero=FALSE,
doc=NULL,
reference=c("FitzJohn (2010) doi:10.1093/sysbio/syq053"),
lambda=lambda,
mu=mu)
}
default.argnames.quasse <- function(lambda, mu) {
c(sprintf("l.%s", names(formals(lambda))[-1]),
sprintf("m.%s", names(formals(mu))[-1]),
"drift", "diffusion")
}
make.cache.quasse <- function(tree, states, states.sd, lambda, mu,
control, sampling.f, for.split=FALSE) {
tree <- check.tree(tree)
tmp <- check.states.quasse(tree, states, states.sd)
states <- tmp$states
states.sd <- tmp$states.sd
control <- check.control.quasse(control, tree, states)
cache <- make.cache(tree)
cache$states <- states
cache$states.sd <- states.sd
cache$control <- control
if ( !for.split ) {
n.lambda <- check.f.quasse(lambda)
n.mu <- check.f.quasse(mu)
n.args <- n.lambda + n.mu + 2
args <- list(lambda=seq_len(n.lambda),
mu=seq_len(n.mu) + n.lambda,
drift=n.lambda + n.mu + 1,
diffusion=n.lambda + n.mu + 2)
cache$lambda <- lambda
cache$mu <- mu
cache$args <- args
sampling.f <- check.sampling.f(sampling.f, 1)
cache$sampling.f <- sampling.f
}
cache$info <- make.info.quasse(lambda, mu, tree)
cache
}
initial.tip.quasse <- function(cache, control, x) {
nx <- control$nx * control$r
npad <- nx - length(x)
e0 <- 1 - cache$sampling.f
if ( control$tips.combined ) {
tips <- cache$tips
t <- cache$len[tips]
i <- order(t)
target <- tips[i]
states <- cache$states[i]
states.sd <- cache$states.sd[i]
y <- mapply(function(mean, sd)
c(dnorm(x, mean, sd), rep(0, npad)),
states, states.sd, SIMPLIFY=FALSE)
y <- matrix(c(rep(e0, nx), unlist(y)), nx, length(target)+1)
list(target=target, y=y, t=t[i])
} else {
y <- mapply(function(mean, sd)
c(rep(e0, nx),
dnorm(x, mean, sd), rep(0, npad)),
cache$states, cache$states.sd, SIMPLIFY=FALSE)
dt.tips.ordered(y, cache$tips, cache$len[cache$tips])
}
}
make.initial.conditions.quasse <- function(control) {
tc <- control$tc
r <- control$r
nx.lo <- control$nx
nx.hi <- nx.lo * r
eps <- 1e-8
function(init, pars, t, idx) {
if ( length(init[[1]]) != length(init[[2]]) )
stop("Data have incompatible length")
if ( t < tc ) {
nx <- nx.hi
lambda <- pars[[1]]$lambda
} else {
nx <- nx.lo
lambda <- pars[[2]]$lambda
}
ndat <- length(lambda)
i <- seq_len(nx)
j <- seq.int(nx+1, nx + ndat)
c(init[[1]][i],
init[[1]][j] * init[[2]][j] * lambda,
rep.int(0.0, nx - ndat))
}
}
make.rootfunc.quasse <- function(cache) {
root.idx <- cache$root
nx <- cache$control$nx
dx <- cache$control$dx
function(res, pars, condition.surv, root, root.f, intermediates) {
vals <- matrix(res$vals, nx, 2)[seq_len(pars$lo$ndat),]
lq <- res$lq
d.root <- vals[,2]
root.p <- root.p.quasse(d.root, pars$lo, root, root.f)
if ( condition.surv ) {
lambda <- pars$lo$lambda
e.root <- vals[,1]
d.root <- d.root / sum(root.p * lambda * (1 - e.root)^2) * dx
}
loglik <- log(sum(root.p * d.root) * dx) + sum(lq)
if ( intermediates ) {
attr(loglik, "intermediates") <- res
attr(loglik, "vals") <- vals
}
loglik
}
}
root.p.quasse <- function(d.root, pars, root, root.f) {
if ( !is.null(root.f) && root != ROOT.GIVEN )
warning("Ignoring specified root state")
x <- pars$x
dx <- x[2] - x[1]
if ( root == ROOT.FLAT ) {
p <- 1 / ((pars$nx-1) * dx)
} else if ( root == ROOT.OBS ) {
p <- d.root / (sum(d.root) * dx)
} else if ( root == ROOT.GIVEN ) {
p <- root.f(x)
} else {
stop("Unsupported root mode")
}
p
}
make.all.branches.quasse <- function(cache, control) {
branches <- make.branches.quasse(cache, control)
initial.conditions <- make.initial.conditions.quasse(control)
function(pars, intermediates, preset=NULL) {
cache$y <- initial.tip.quasse(cache, cache$control, pars[[1]]$x)
all.branches.list(pars, cache, initial.conditions,
branches, preset)
}
}
make.branches.quasse <- function(cache, control) {
if ( control$method == "fftC" )
branches <- make.branches.quasse.fftC(control)
else if ( control$method == "fftR" )
branches <- make.branches.quasse.fftR(control)
else if ( control$method == "mol" )
branches <- make.branches.quasse.mol(control)
else
stop("Unknown method", control$method)
}
check.pars.quasse <- function(lambda.x, mu.x, drift, diffusion) {
if ( any(!is.finite(c(lambda.x, mu.x, drift, diffusion))) )
stop("Non-finite/NA parameters")
if ( any(lambda.x < 0) || any(mu.x < 0) || diffusion <= 0 )
stop("Illegal negative parameters")
if ( !any(lambda.x > 0) )
stop("No positive lambda; cannot compute likelihood")
}
|
tidy.jobjRef <- function(x, matrix = c("beta", "gamma"), log = FALSE,
normalized = TRUE, smoothed = TRUE, ...) {
s <- x$toString()
if (!stringr::str_detect(s, "TopicModel")) {
stop("Don't know how to tidy jobjRef ", s)
}
matrix <- match.arg(matrix)
if (matrix == "beta") {
func <- mallet::mallet.topic.words
} else {
func <- mallet::mallet.doc.topics
}
m <- func(x, normalized = normalized, smoothed = smoothed)
ret <- dplyr::tbl_df(reshape2::melt(m))
if (matrix == "beta") {
colnames(ret) <- c("topic", "term", "beta")
ret$term <- x$getVocabulary()[ret$term]
} else {
colnames(ret) <- c("document", "topic", "gamma")
ret$document <- x$getDocumentNames()[ret$document]
}
if (log) {
ret[[matrix]] <- log(ret[[matrix]])
}
ret
}
augment.jobjRef <- function(x, data, ...) {
s <- x$toString()
if (!stringr::str_detect(s, "TopicModel")) {
stop("Don't know how to augment jobjRef ", s)
}
if (missing(data)) {
stop("data argument must be provided in order to augment a mallet model")
}
beta <- t(mallet::mallet.topic.words(x, normalized = TRUE, smoothed = TRUE))
gamma <- mallet::mallet.doc.topics(x, normalized = TRUE, smoothed = TRUE)
term_indices <- match(data$term, x$getVocabulary())
doc_indices <- match(data$document, x$getDocumentNames())
products <- beta[term_indices, ] * gamma[doc_indices, ]
keep <- !is.na(term_indices) & !is.na(doc_indices)
data$.topic <- NA
data$.topic[keep] <- apply(products[keep, ], 1, which.max)
data
}
|
NULL
rray_dim_names <- function(x) {
rray__dim_names(x)
}
dimnames.vctrs_rray <- function(x) {
rray_dim_names(x)
}
`rray_dim_names<-` <- function(x, value) {
rray_set_dim_names(x, value)
}
rray_set_dim_names <- function(x, dim_names) {
dim <- rray_dim(x)
dim_n <- vec_size(dim)
if (is_null(dim_names)) {
dim_names <- rray_empty_dim_names(dim_n)
}
validate_dim_names(dim_names, dim)
rray_set_dim_names_impl(x, dim_names)
}
rray_set_dim_names_impl <- function(x, dim_names) {
if (is.array(x)) {
attr(x, which = "dimnames") <- dim_names
}
else {
attr(x, "names") <- dim_names[[1]]
}
x
}
validate_dim_names <- function(dim_names, dim) {
dim_n <- vec_size(dim)
n_dim_names <- vec_size(dim_names)
if (dim_n != n_dim_names) {
glubort(
"The dimensionality of the object ({dim_n}) must be equal ",
"to the size of the `dim_names` ({n_dim_names})."
)
}
map2(dim_names, dim, validate_axis_names)
invisible(dim_names)
}
validate_axis_names <- function(axis_names, n) {
if (is_null(axis_names)) {
return(invisible(axis_names))
}
if (!is.character(axis_names)) {
abort("All dim names must be character vectors, or `NULL`.")
}
if (vec_size(axis_names) != n) {
glubort(
"The size of each dimension's names must be equal to the ",
"size of the corresponding dimension."
)
}
return(invisible(axis_names))
}
`dimnames<-.vctrs_rray` <- function(x, value) {
rray_set_dim_names(x, value)
}
`names<-.vctrs_rray` <- function(x, value) {
if (rray_dim_n(x) > 1L) {
glubort(
"Cannot set `names` on a 2D+ object. Use `rray_dim_names<-()` instead."
)
}
if (!is.null(value)) {
value <- list(value)
}
rray_set_dim_names(x, value)
}
rray_axis_names <- function(x, axis) {
validate_axis(axis, x)
rray_dim_names(x)[[axis]]
}
`rray_axis_names<-` <- function(x, axis, value) {
rray_set_axis_names(x, axis, value)
}
rray_set_axis_names <- function(x, axis, names, meta = NULL) {
axis <- vec_cast(axis, integer())
vec_assert(axis, size = 1L, arg = "axis")
validate_axis(axis, x)
new_dim_names <- rray_dim_names(x)
new_dim_names[axis] <- list(names)
if (!is.null(meta)) {
vec_assert(meta, ptype = character(), size = 1L, arg = "meta")
names2(new_dim_names)[axis] <- meta
}
rray_set_dim_names(x, new_dim_names)
}
`names2<-` <- function (x, value) {
if (is_null(names(x))) {
names(x) <- names2(x)
}
names(x) <- value
x
}
rray_row_names <- function(x) {
rray_axis_names(x, 1L)
}
`rray_row_names<-` <- function(x, value) {
rray_set_row_names(x, value)
}
rray_set_row_names <- function(x, names, meta = NULL) {
rray_set_axis_names(x, 1L, names, meta)
}
rray_col_names <- function(x) {
rray_axis_names(x, 2L)
}
`rray_col_names<-` <- function(x, value) {
rray_set_col_names(x, value)
}
rray_set_col_names <- function(x, names, meta = NULL) {
rray_set_axis_names(x, 2L, names, meta)
}
|
context("canvasXpress Web Charts - SPLOM")
test_that("cXsplom1", {
check_ui_test(cXsplom1())
})
test_that("cXsplom2", {
check_ui_test(cXsplom2())
})
test_that("cXsplom3", {
check_ui_test(cXsplom3())
})
test_that("cXsplom4", {
check_ui_test(cXsplom4())
})
test_that("cXsplom5", {
check_ui_test(cXsplom5())
})
test_that("cXsplom6", {
check_ui_test(cXsplom6())
})
test_that("cXsplom7", {
check_ui_test(cXsplom7())
})
test_that("cXsplom8", {
check_ui_test(cXsplom8())
})
|
createInventory <- function(directory = "EnvCanada"){
if (!file.exists(directory)) stop(" Directory does not exist")
filePat <- "^99.+Env\\.csv$"
csvFiles <- list.files(path = directory,full.names = TRUE, pattern = filePat)
stations <- length(csvFiles)
dirChar <- nchar(directory)+2
ids <- as.numeric(substring(csvFiles,dirChar,dirChar+7))
Inventory <- data.frame(Name = rep(NA,stations),
Province = rep(NA,stations),
Lat = vector(mode = "numeric", length = stations),
Lon = vector(mode = "numeric", length = stations),
Altitude = vector(mode = "numeric", length = stations),
Id = rep(NA,stations),
WMO = vector(mode = "numeric", length = stations),
TCid = rep(NA,stations),
downloadId = ids)
for( files in 1 :length(csvFiles)){
print(csvFiles[files])
S <- read.csv(csvFiles[files], nrows = 7, stringsAsFactors = FALSE)
inv <- as.data.frame(t(S),stringsAsFactors=FALSE)
Inventory$Name[files] <- rownames(inv)[2]
Inventory$Province[files] <- inv$V1[2]
Inventory$Lat[files] <- inv$V2[2]
Inventory$Lon[files] <- inv$V3[2]
Inventory$Altitude[files] <- inv$V4[2]
Inventory$Id[files] <- inv$V5[2]
Inventory$WMO[files] <- inv$V6[2]
Inventory$TCid[files] <- inv$V7[2]
}
return(Inventory)
}
|
replaceTabs.inner <- function( text, width=8 )
{
spaces <- " "
if(nchar(text)<1) return(text)
text.split <- strsplit(text,"\t")[[1]]
if(length(text.split)==1)
return(text)
else
{
nSpaceAdd <- 8 - nchar(text.split) %% 8
nSpaceAdd[length(nSpaceAdd)] <- 0
nSpaceAdd[nSpaceAdd==8] <- 0
retval <- ""
for(i in 1:length(text.split))
{
tmp.text <- chartr("\t"," ", text.split[i])
retval <- paste(retval, tmp.text, substr(spaces,0,nSpaceAdd[i]-1 ), sep='' )
}
return(retval)
}
}
replaceTabs <- function(text, width=8)
{
text <- as.character(text)
retval <- sapply(text, replaceTabs.inner)
names(retval) <- names(text)
retval
}
|
.plt.add <-
function(add, x1, x2, y1, y2,
add_cex, add_lwd, add_lty, add_color, add_fill, add_trans, ...) {
n.obj <- length(add)
n.clr <- length(add_color)
n.fll <- length(add_fill)
n.lty <- length(add_lty)
n.lwd <- length(add_lwd)
n.cex <- length(add_cex)
n.trn <- length(add_trans)
i.clr <- 0; i.fll <- 0; i.lty <- 0; i.lwd <- 0; i.cex <- 0; i.trn <- 0
x1i <- 0; x2i <- 0; y1i <- 0; y2i <- 0;
usr <- par("usr")
for (i in 1:n.obj) {
i.clr <- i.clr + 1; if (i.clr > n.clr) i.clr <- 1
i.fll <- i.fll + 1; if (i.fll > n.fll) i.fll <- 1
i.lty <- i.lty + 1; if (i.lty > n.lty) i.lty <- 1
i.lwd <- i.lwd + 1; if (i.lwd > n.lwd) i.lwd <- 1
i.cex <- i.cex + 1; if (i.cex > n.cex) i.cex <- 1
i.trn <- i.trn + 1; if (i.trn > n.trn) i.trn <- 1
if (!is.null(add_trans[i.trn])) {
if (add[i] %in% c("rect", "point"))
add_fill[i.fll] <- .maketrans(add_fill[i.fll], (1-add_trans[i.trn])*256)
}
geom <- c("rect", "line", "v_line", "h_line", "arrow", "point")
if (!(add[i] %in% geom)) {
if (n.obj == 1) {
xx <- x1; yy <- y1
}
else {
x1i <- x1i + 1; y1i <- y1i + 1
xx <- x1[x1i]; yy <- y1[y1i]
}
text(xx, yy, labels=add[i], cex=add_cex[i.cex], col=add_color[i.clr], ...)
}
else if (add[i] == "v_line") {
if (n.obj == 1)
xx <- x1
else {
x1i <- x1i + 1
xx <- x1[x1i]
}
segments(xx, usr[3], xx, usr[4], col=add_color[i.clr],
lwd=add_lwd[i.lwd], lty=add_lty[i.lty], ...)
}
else if (add[i] == "h_line") {
y1i <- y1i + 1
yy <- y1[y1i]
if (n.obj == 1) yy <- y1
segments(usr[1], yy, usr[2], yy, col=add_color[i.clr],
lwd=add_lwd[i.lwd], lty=add_lty[i.lty], ...)
}
else if (add[i] %in% c("line", "rect", "arrow")) {
if (n.obj == 1) {
xx1 <- x1; yy1 <- y1; xx2 <- x2; yy2 <- y2;
}
else {
x1i <- x1i + 1; x2i <- x2i + 1; y1i <- y1i + 1; y2i <- y2i + 1
xx1 <- x1[x1i]; xx2 <- x2[x2i]; yy1 <- y1[y1i]; yy2 <- y2[y2i]
}
if (add[i] == "line")
segments(xx1, yy1, xx2, yy2, col=add_color[i.clr],
lwd=add_lwd[i.lwd], lty=add_lty[i.lty], ...)
if (add[i] == "rect")
rect(xx1, yy1, xx2, yy2, border=add_color[i.clr], col=add_fill[i.fll],
lwd=add_lwd[i.lwd], lty=add_lty[i.lty], ...)
if (add[i] == "arrow")
arrows(xx1, yy1, xx2, yy2, col=add_color[i.clr],
lwd=add_lwd[i.lwd], lty=add_lty[i.lty], ...)
}
else if (add[i] %in% c("point")) {
if (n.obj == 1) {
xx1 <- x1; yy1 <- y1;
}
else {
x1i <- x1i + 1; y1i <- y1i + 1;
xx1 <- x1[x1i]; yy1 <- y1[y1i];
}
points(xx1, yy1, col=add_color[i.clr], bg=add_fill[i.fll],
cex=add_cex[i.cex], pch=21)
}
}
}
|
print.lpcfdrobj <- function(x,...){
lpcfdr.obj <- x
cat("Call:\n",fill=F)
dput(lpcfdr.obj$call)
cat(fill=T)
cat(c("Pi0: ", round(lpcfdr.obj$pi0,4)),fill=T)
cat(c("Soft Thresh: ", round(lpcfdr.obj$soft.thresh,4)),fill=T)
numGenesT <- NULL
numGenesLPC <- NULL
for(thr in c(.001,.01,.05,.1,.2,.4)){
numGenesT <- c(numGenesT, sum(lpcfdr.obj$fdrt <= thr))
numGenesLPC <- c(numGenesLPC, sum(lpcfdr.obj$fdrlpc <= thr))
}
cat(fill=T)
cat("FDR Cut-off and
mat <- cbind(numGenesT, numGenesLPC)
dimnames(mat) <- list(c(.001,.01,.05,.1,.2,.4), c("
print(mat, quote=FALSE)
invisible()
}
|
.getPenMatrix <- function(q, UrS, sp, Mp, U1)
{
if (length(UrS)) {
rp <- gam.reparam(UrS, sp, 0)
T <- diag( q )
T[1:ncol(rp$Qs), 1:ncol(rp$Qs)] <- rp$Qs
T <- U1%*%T
St <- rbind(cbind(rp$S,matrix(0,nrow(rp$S),Mp)),matrix(0,Mp,q))
St <- T %*% St %*% t(T)
} else {
T <- diag(q);
St <- matrix(0,q,q)
}
return( St )
}
|
t.test( ~ (vitamin / placebo), data = Endurance)
|
recruitment <- function(param, tsample, catch_column = NA, plot = FALSE){
res <- param
Linf <- res$Linf
K <- res$K
t0 <- ifelse("t0" %in% names(res),res$t0, 0)
D <- ifelse("D" %in% names(res),res$D, 1)
C <- ifelse("C" %in% names(res),res$C, 0)
ts <- ifelse("ts" %in% names(res),res$ts, 0)
classes <- as.character(res$midLengths)
classes.num <- do.call(rbind,strsplit(classes, split="\\+"))
classes.num <- as.numeric(classes.num[,1])
if(is.na(catch_column)) catch <- res$catch
if(!is.na(catch_column)) catch <- res$catch[,catch_column]
if(inherits(catch,"matrix") || inherits(catch,"data.frame")){
len_catch <- dim(catch)[1]
catch_sets <- dim(catch)[2]
}else{
len_catch <- length(catch)
catch_sets <- 1
}
res_months <- data.frame(month = 1:12)
N_months_loop <- matrix(NA, nrow = 12, ncol = catch_sets)
ti <- matrix(NA, nrow = len_catch, ncol = catch_sets)
tS_frac <- matrix(NA, nrow = len_catch, ncol = catch_sets)
correspond_month <- matrix(NA, nrow = len_catch, ncol = catch_sets)
for(i in 1:catch_sets){
if(catch_sets > 1){
catchi <- catch[,i]
tsampli <- tsample[i]
}else{
catchi <- catch
tsampli <- tsample
}
ti[,i] <- (log(1 - (classes.num/Linf)^D) +
C * ((K*D)/2*pi)*sin(2*pi)*((t0-tsampli)-ts))/(-K*D) + t0 - tsampli
tS_frac[,i] <- ti[,i] - floor(ti[,i])
correspond_month[,i] <- floor(tS_frac[,i] * 12 + 1)
N_months_pre <- aggregate(list(numbers=catchi), by = list(month = correspond_month[,i]),
sum, na.rm =TRUE)
N_months_all <- merge(res_months, N_months_pre, by.x = "month", all.x=TRUE)
N_months_all$numbers[is.na(N_months_all$numbers)] <- 0
N_months_loop[,i] <- N_months_all$numbers
}
N_months_final <- apply(N_months_loop, 1, FUN = mean, na.rm = TRUE)
N_months_per <- (N_months_final/sum(N_months_final, na.rm = TRUE)) * 100
if("t0" %in% names(res)){
months_abb <- month.abb[res_months$month]
}
if(catch_sets == 1){
ti <- ti[,1]
tS_frac <- tS_frac[,1]
correspond_month <- correspond_month[,1]
N_months_loop <- N_months_loop[,1]
}
res2 <- c(res,list(ti = ti,
tS_frac = tS_frac,
cor_months = correspond_month,
months = res_months$month))
if("t0" %in% names(res)) res2$months_abb <- months_abb
ret <- c(res2,list(all_recruits = N_months_loop,
mean_recruits = N_months_final,
per_recruits = N_months_per))
class(ret) <- "recruitment"
if(plot) plot(ret)
return(ret)
}
|
context("periscope create new application")
expect_cleanup_create_new_application <- function(fullname, sampleapp = FALSE, dashboard_plus = FALSE, leftsidebar = TRUE) {
local_edition(3)
expect_true(dir.exists(fullname))
expect_true(file.exists(paste0(fullname, "/global.R")))
expect_true(file.exists(paste0(fullname, "/server.R")))
expect_true(file.exists(paste0(fullname, "/ui.R")))
expect_true(dir.exists(paste0(fullname, "/www")))
expect_true(dir.exists(paste0(fullname, "/www/css")))
expect_true(dir.exists(paste0(fullname, "/www/js")))
expect_true(file.exists(paste0(fullname, "/www/periscope_style.yaml")))
expect_true(dir.exists(paste0(fullname, "/www/img")))
expect_true(file.exists(paste0(fullname, "/www/img/loader.gif")))
expect_true(file.exists(paste0(fullname, "/www/img/tooltip.png")))
expect_true(dir.exists(paste0(fullname, "/program")))
expect_true(file.exists(paste0(fullname, "/program/global.R")))
expect_true(file.exists(paste0(fullname, "/program/server_global.R")))
expect_true(file.exists(paste0(fullname, "/program/server_local.R")))
expect_true(file.exists(paste0(fullname, "/program/ui_body.R")))
expect_true(dir.exists(paste0(fullname, "/program/data")))
expect_true(dir.exists(paste0(fullname, "/program/fxn")))
expect_true(dir.exists(paste0(fullname, "/log")))
if (leftsidebar) {
expect_true(file.exists(paste0(fullname, "/program/ui_sidebar.R")))
} else {
expect_true(!file.exists(paste0(fullname, "/program/ui_sidebar.R")))
}
if (sampleapp) {
expect_true(file.exists(paste0(fullname, "/program/data/example.csv")))
expect_true(file.exists(paste0(fullname, "/program/fxn/program_helpers.R")))
expect_true(file.exists(paste0(fullname, "/program/fxn/plots.R")))
}
if (dashboard_plus) {
expect_true(file.exists(paste0(fullname, "/program/ui_sidebar_right.R")))
} else {
expect_true(!file.exists(paste0(fullname, "/program/ui_sidebar_right.R")))
}
unlink(fullname, TRUE)
}
test_that("create_new_application", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = FALSE, rightsidebar = NULL),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp)
})
test_that("create_new_application sample", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, rightsidebar = NULL),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp, sampleapp = TRUE)
})
test_that("create_new_application sample right_sidebar", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
right_sidebar <- "gears"
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, rightsidebar = right_sidebar),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp, sampleapp = TRUE, dashboard_plus = !is.null(right_sidebar))
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, rightsidebar = TRUE),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp, sampleapp = TRUE, dashboard_plus = TRUE)
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_error(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, rightsidebar = 1),
"Framework creation could not proceed, invalid type for rightsidebar, only logical or character allowed")
})
test_that("create_new_application sample right_sidebar without left_sidebar", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, rightsidebar = TRUE, leftsidebar = FALSE),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp, sampleapp = TRUE, dashboard_plus = TRUE, leftsidebar = FALSE)
})
test_that("create_new_application sample invalid right_sidebar", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_error(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, rightsidebar = mtcars),
"Framework creation could not proceed, invalid type for rightsidebar, only logical or character allowed")
})
test_that("create_new_application no left sidebar", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, leftsidebar = FALSE),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp, leftsidebar = FALSE)
})
test_that("create_new_application no reset button", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, resetbutton = FALSE),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp, sampleapp = TRUE)
})
test_that("create_new_application no reset button, no left sidebar", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = TRUE, resetbutton = FALSE, leftsidebar = FALSE),
"Framework creation was successful.")
expect_cleanup_create_new_application(appTemp, sampleapp = TRUE, leftsidebar = FALSE)
})
test_that("create_new_application invalid yaml file", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
expect_warning(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = FALSE, rightsidebar = NULL, custom_theme_file = ""),
"'custom_theme_file' must be single character value pointing to valid yaml file location. Using default values.")
})
test_that("create_new_application with valid yaml file", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
yaml_loc <- "sample_app/www/periscope_style.yaml"
expect_message(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = FALSE, rightsidebar = NULL, custom_theme_file = yaml_loc),
"Framework creation was successful.")
})
test_that("create_new_application invalid location", {
expect_warning(create_new_application(name = "Invalid", location = tempfile(), sampleapp = FALSE),
"Framework creation could not proceed, location=.* does not exist!")
})
test_that("create_new_application existing location", {
appTemp.dir <- tempdir()
appTemp <- tempfile(pattern = "TestThatApp", tmpdir = appTemp.dir)
appTemp.name <- gsub('\\\\|/', '', (gsub(appTemp.dir, "", appTemp, fixed = T)))
create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = FALSE)
expect_warning(create_new_application(name = appTemp.name, location = appTemp.dir, sampleapp = FALSE),
paste0("Framework creation could not proceed, path=.* already exists!"))
expect_cleanup_create_new_application(appTemp)
})
|
cl <- kmeans(iris[,-5], 3)
plot(iris[,-5], col = cl$cluster)
points(cl$centers, col = 1:3, pch = 8)
|
require(crs)
set.seed(42)
n <- 10000
num.eval <- 50
x1 <- runif(n,-5,5)
x2 <- runif(n,-5,5)
dgp <- sin(sqrt(x1^2+x2^2))/sqrt(x1^2+x2^2)
y <- dgp + rnorm(n,sd=.1)
model.nomad <- crs(y~x1+x2,
basis="auto",
cv="nomad",
complexity="degree-knots",
knots="uniform",
deriv=1,
cv.func="cv.aic")
summary(model.nomad)
model.nomad <- crs(y~x1+x2,
basis="auto",
degree=c(3,3),
segments=c(2,4),
cv="nomad",
complexity="degree-knots",
knots="uniform",
deriv=1,
cv.func="cv.aic")
summary(model.nomad)
model <- crs(y~x1+x2,
basis="auto",
cv="exhaustive",
complexity="degree-knots",
knots="uniform",
deriv=1,
cv.func="cv.aic")
summary(model)
|
gdalwarp <-
function(srcfile, dstfile, ..., s_srs, t_srs, ct, to,
novshiftgrid, order, tps, rpc, geoloc, et, refine_gcps,
te, te_srs, tr, tap, ts, ovr, wo, ot, wt, r, srcnodata,
dstnodata, srcalpha, nosrcalpha, dstalpha, wm, multi, q,
IF, of, co, cutline, cl, cwhere, csql, cblend,
crop_to_cutline, overwrite, nomd, cvmd, setci, oo, doo,
config, dryrun = FALSE)
{
args <- mget(names(match.call())[-1])
args[c("srcfile", "dstfile", "dryrun")] <- NULL
formalsTable <- getFormalsTable("gdalwarp")
opts <- process_args(args, formalsTable)
if(dryrun) {
x <- CLI_call("gdalwarp", srcfile, dstfile, opts=opts)
return(x)
}
gdal_utils("warp", srcfile, dstfile, opts)
invisible(dstfile)
}
|
stat_function <- function(mapping = NULL, data = NULL,
geom = "path", position = "identity",
...,
fun,
xlim = NULL,
n = 101,
args = list(),
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = StatFunction,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
fun = fun,
n = n,
args = args,
na.rm = na.rm,
xlim = xlim,
...
)
)
}
StatFunction <- gganimintproto("StatFunction", Stat,
default_aes = aes(y = ..y..),
compute_group = function(data, scales, fun, xlim = NULL, n = 101, args = list()) {
range <- xlim %||% scales$x$dimension()
xseq <- seq(range[1], range[2], length.out = n)
if (scales$x$is_discrete()) {
x_trans <- xseq
} else {
x_trans <- scales$x$trans$inverse(xseq)
}
data.frame(
x = xseq,
y = do.call(fun, c(list(quote(x_trans)), args))
)
}
)
|
`i.detrend.series` <- function(y, y.name=NULL, nyrs = NULL, f = 0.5,
pos.slope = FALSE)
{
fits <- detrend.series(y, y.name, make.plot=TRUE, nyrs = nyrs, f = f,
pos.slope = pos.slope)
fits <- fits[, !colAlls(is.na(fits)), drop=FALSE]
col.names <- names(fits)
cat(gettextf("\nChoose a detrending method for this series %s.\n",
y.name, domain="R-dplR"))
cat(gettext("Methods are: \n", domain="R-dplR"))
for(i in seq_along(col.names))
cat(i, ": ", col.names[i], "\n", sep="")
ans <- as.integer(readline(gettext("Enter a number ", domain="R-dplR")))
while(ans < 1 || ans > i || is.na(ans)){
message("number out of range or not an integer\n")
ans <- as.integer(readline(gettext("Enter a number ", domain="R-dplR")))
}
method <- col.names[ans]
res <- fits[, method]
names(res) <- names(y)
res
}
|
e2e_plot_sens_mc <- function(model, selection="SENS", use.example=FALSE) {
oo <- options()
on.exit(options(oo))
if (use.example == TRUE) hasExamples()
start_par = par()$mfrow
on.exit(par(mfrow = start_par))
if(selection=="SENS"){
plot_sensitivity_results(model=model, use.example=use.example)
} else if(selection=="MC"){
plot_mc_parameter_likelihoods(model=model, use.example=use.example)
} else {
stop("Error: unknown plot selection '", selection, "' !\n")
}
}
|
compare_props <- function(
dataset, var1, var2, levs = "",
alternative = "two.sided", conf_lev = .95,
comb = "", adjust = "none", data_filter = "",
envir = parent.frame()
) {
df_name <- if (is_string(dataset)) dataset else deparse(substitute(dataset))
vars <- c(var1, var2)
dataset <- get_data(dataset, vars, filt = data_filter, na.rm = FALSE, envir = envir) %>%
mutate_all(as.factor)
dataset <- dataset[!is.na(dataset[[1]]), , drop = FALSE]
n_miss_df <- group_by_at(dataset, var1) %>%
summarise_at(n_missing, .vars = var2) %>%
set_colnames(c(var1, "n_miss"))
dataset <- na.omit(dataset)
if (length(levels(dataset[[var1]])) == nrow(dataset)) {
return("Test requires multiple observations in each group. Please select another variable." %>%
add_class("compare_props"))
}
lv <- levels(dataset[[var2]])
if (levs != "") {
if (levs %in% lv && lv[1] != levs) {
dataset[[var2]] %<>% as.character %>% as.factor() %>% relevel(levs)
lv <- levels(dataset[[var2]])
}
}
not_vary <- colnames(dataset)[summarise_all(dataset, does_vary) == FALSE]
if (length(not_vary) > 0) {
return(paste0("The following variable(s) show no variation. Please select other variables.\n\n** ", paste0(not_vary, collapse = ", "), " **") %>%
add_class("compare_props"))
}
rn <- ""
prop_input <- group_by_at(dataset, .vars = c(var1, var2)) %>%
summarise(n = n()) %>%
spread(!! var2, "n") %>%
as.data.frame(stringsAsFactors = FALSE) %>%
{
rn <<- .[[1]] %>% as.character()
select(., -1) %>%
as.matrix() %>%
set_rownames(rn)
}
prop_input[is.na(prop_input)] <- 0
lv <- rownames(prop_input)
cmb <- combn(lv, 2) %>%
t() %>%
as.data.frame(stringsAsFactors = FALSE)
rownames(cmb) <- cmb %>% apply(1, paste, collapse = ":")
colnames(cmb) <- c("group1", "group2")
if (!radiant.data::is_empty(comb)) {
if (all(comb %in% rownames(cmb))) {
cmb <- cmb[comb, ]
} else {
cmb <- cmb[1, ]
}
}
res <- cmb
res[, c("chisq.value", "p.value", "df", "ci_low", "ci_high", "sim")] <- 0
for (i in 1:nrow(cmb)) {
ind <- c(which(cmb[i, 1] == rownames(prop_input)), which(cmb[i, 2] == rownames(prop_input)))
pinp <- prop_input[ind, ]
res[i, c("chisq.value", "p.value", "df", "ci_low", "ci_high")] <-
sshhr(prop.test(pinp, alternative = alternative, conf.level = conf_lev, correct = FALSE)) %>%
tidy() %>%
.[1, c("statistic", "p.value", "parameter", "conf.low", "conf.high")]
E <- (rowSums(pinp) %*% t(colSums(pinp))) / sum(pinp)
if (any(E < 5)) {
res[i, "p.value"] <- sshhr(chisq.test(pinp, simulate.p.value = TRUE, B = 2000) %>% tidy() %>% .$p.value)
res[i, "df"] <- NA
}
}
if (adjust != "none") {
res$p.value %<>% p.adjust(method = adjust)
}
res$sig_star <- sig_stars(res$p.value)
me_calc <- function(se, conf.lev = .95)
se * qnorm(conf.lev / 2 + .5, lower.tail = TRUE)
dat_summary <- data.frame(prop_input, check.names = FALSE, stringsAsFactors = FALSE) %>%
mutate_if(is.numeric, as.integer) %>%
mutate(
p = .[[1]] / as.integer(rowSums(.[, 1:2])),
n = as.integer(rowSums(.[, 1:2])),
n_missing = 0,
sd = sqrt(p * (1 - p)),
se = sqrt(p * (1 - p) / n),
me = me_calc(se, conf_lev)
) %>%
set_rownames(rownames(prop_input)) %>%
rownames_to_column(var = var1)
dat_summary[[var1]] %<>% factor(., levels = .)
dat_summary <- suppressWarnings(left_join(dat_summary, n_miss_df, by = var1)) %>%
mutate(n_missing = n_miss) %>%
select(-n_miss)
vars <- paste0(vars, collapse = ", ")
rm(i, me_calc, envir)
as.list(environment()) %>% add_class("compare_props")
}
summary.compare_props <- function(object, show = FALSE, dec = 3, ...) {
if (is.character(object)) return(object)
cat("Pairwise proportion comparisons\n")
cat("Data :", object$df_name, "\n")
if (!radiant.data::is_empty(object$data_filter)) {
cat("Filter :", gsub("\\n", "", object$data_filter), "\n")
}
cat("Variables :", object$vars, "\n")
cat("Level :", object$levs, "in", object$var2, "\n")
cat("Confidence:", object$conf_lev, "\n")
cat("Adjustment:", if (object$adjust == "bonf") "Bonferroni" else "None", "\n\n")
object$dat_summary %>%
as.data.frame(stringsAsFactors = FALSE) %>%
format_df(dec = dec, mark = ",") %>%
print(row.names = FALSE)
cat("\n")
hyp_symbol <- c(
"two.sided" = "not equal to",
"less" = "<",
"greater" = ">"
)[object$alternative]
props <- object$dat_summary$p
names(props) <- object$rn
ci_perc <- ci_label(object$alternative, object$conf_lev)
res <- object$res
res$`Alt. hyp.` <- paste(res$group1, hyp_symbol, res$group2, " ")
res$`Null hyp.` <- paste(res$group1, "=", res$group2, " ")
res$diff <- (props[res$group1 %>% as.character()] - props[res$group2 %>% as.character()]) %>% round(dec)
res_sim <- is.na(res$df)
if (show) {
res <- res[, c("Null hyp.", "Alt. hyp.", "diff", "p.value", "chisq.value", "df", "ci_low", "ci_high", "sig_star")]
res[, c("chisq.value", "ci_low", "ci_high")] %<>% format_df(dec, mark = ",")
res$df[res_sim] <- "*1*"
res <- rename(res, !!! setNames(c("ci_low", "ci_high"), ci_perc))
} else {
res <- res[, c("Null hyp.", "Alt. hyp.", "diff", "p.value", "sig_star")]
}
res <- rename(res, ` ` = "sig_star")
res$p.value[res$p.value >= .001] %<>% round(dec)
res$p.value[res$p.value < .001] <- "< .001"
res$p.value[res_sim] %<>% paste0(" (2000 replicates)")
print(res, row.names = FALSE, right = FALSE)
cat("\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n")
}
plot.compare_props <- function(
x, plots = "bar", shiny = FALSE,
custom = FALSE, ...
) {
if (is.character(x)) return(x)
v1 <- colnames(x$dataset)[1]
v2 <- colnames(x$dataset)[-1]
lev_name <- x$levs
plot_list <- list()
if ("bar" %in% plots) {
plot_list[[which("bar" == plots)]] <-
ggplot(x$dat_summary, aes_string(x = v1, y = "p", fill = v1)) +
geom_bar(stat = "identity", alpha = 0.5) +
geom_errorbar(width = .1, aes(ymin = p - me, ymax = p + me)) +
geom_errorbar(width = .05, aes(ymin = p - se, ymax = p + se), color = "blue") +
theme(legend.position = "none") +
scale_y_continuous(labels = scales::percent) +
labs(y = paste0("Proportion of \"", lev_name, "\" in ", v2))
}
if ("dodge" %in% plots) {
plot_list[[which("dodge" == plots)]] <- group_by_at(x$dataset, .vars = c(v1, v2)) %>%
summarise(count = n()) %>%
group_by_at(.vars = v1) %>%
mutate(perc = count / sum(count)) %>%
ggplot(aes_string(x = v1, y = "perc", fill = v2)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.5) +
scale_y_continuous(labels = scales::percent) +
labs(y = paste0("Proportions per level of ", v1))
}
if (length(plot_list) > 0) {
if (custom) {
if (length(plot_list) == 1) plot_list[[1]] else plot_list
} else {
patchwork::wrap_plots(plot_list, ncol = 1) %>%
{if (shiny) . else print(.)}
}
}
}
|
b0 <- 3; b1 <- 5; sigma <- 2
x <- rep(1:5, each = 4)
e <- rnorm(length(x), sd = sigma)
y <- b0 + b1 * x + e
model <- lm(y ~ x)
confint(model)
|
context("Liver risk scores")
library(transplantr)
test_that("UKELD gives correct result", {
expect_equal(round(ukeld(1.0, 212, 54, 126), 2), 63.22)
})
test_that("MELD gives correct result", {
expect_equal(round(meld(INR = 2.0, bili = 54, creat = 170, dialysis = 0), 3), 24.798)
})
test_that("MELD-Na gives correct result", {
expect_equal(round(meld_na(INR = 1.8, bili = 2, creat = 2, Na = 131, dialysis = 0, units = "US"), 3), 26.257)
})
test_that("PELD gives correct result", {
expect_equal(round(peld(INR = 2, bili = 3.1, albumin = 2.5, listing_age = 2, growth_failure = 1, units = "US"), 0), 19)
})
test_that("APRI gives correct result", {
expect_equal(round(apri(ast = 38, plt = 150, ast_uln = 40), 2), 0.63)
expect_equal(round(apri(ast = 160, plt = 75), 2), 5.33)
})
test_that("ET-DRI gives correct result", {
expect_equal(round(et_dri(age = 39, cod = "trauma", dcd = 0, split = 0, share = "local", cit = 8, ggt = 50, rescue = 0), 2), 1.00)
expect_equal(round(et_dri(age = 65, cod = "trauma", dcd = 0, split = 0, share = "local", cit = 8, ggt = 50, rescue = 0), 2), 1.50)
expect_equal(round(et_dri(age = 65, cod = "trauma", dcd = 0, split = 0, share = "local", cit = 14, ggt = 50, rescue = 0), 2), 1.59)
expect_equal(round(et_dri(age = 25, cod = "trauma", dcd = 0, split = 0, share = "local", cit = 8, ggt = 200, rescue = 0), 2), 1.09)
expect_equal(round(et_dri(age = 25, cod = "trauma", dcd = 0, split = 0, share = "local", cit = 12, ggt = 50, rescue = 1), 2), 1.24)
expect_equal(round(et_dri(age = 25, cod = "trauma", dcd = 1, split = 0, share = "local", cit = 8, ggt = 50, rescue = 0), 2), 1.51)
expect_equal(round(et_dri(age = 25, cod = "cva", dcd = 0, split = 0, share = "local", cit = 8, ggt = 50, rescue = 0), 2), 1.15)
expect_equal(round(et_dri(age = 25, cod = "trauma", dcd = 0, split = 1, share = "regional", cit = 8, ggt = 50, rescue = 0), 2), 1.69)
})
test_that("Liver DRI gives correct result", {
expect_equal(round(liver_dri(age = 25, cod = "trauma", eth = "white", dcd = 0, split = 0, share = "local", cit = 8, height = 170), 2), 1.00)
expect_equal(round(liver_dri(age = 64, cod = "trauma", eth = "white", dcd = 0, split = 0, share = "local", cit = 8, height = 170), 2), 1.53)
expect_equal(round(liver_dri(age = 64, cod = "cva", eth = "white", dcd = 0, split = 0, share = "local", cit = 8, height = 170), 2), 1.77)
expect_equal(round(liver_dri(age = 64, cod = "cva", eth = "white", dcd = 0, split = 0, share = "local", cit = 14, height = 170), 2), 1.88)
expect_equal(round(liver_dri(age = 25, cod = "trauma", eth = "white", dcd = 0, split = 0, share = "local", cit = 14, height = 170), 2), 1.06)
expect_equal(round(liver_dri(age = 25, cod = "trauma", eth = "white", dcd = 1, split = 0, share = "local", cit = 14, height = 170), 2), 1.60)
})
test_that("SOFT scores give correct results", {
expect_equal(soft(Age = 35, BMI = 20, PrevTx = 0, AbdoSurg = 1, Albumin = 30, Dx = 0, ICU = 0, Admitted = 0, MELD = 29, LifeSupport = 0, Encephalopathy = 1, PVThrombosis = 0, Ascites = 1, PortalBleed = 0, DonorAge = 44, DonorCVA = 0, DonorSCr = 110, National = 0, CIT = 8), 7)
expect_equal(p_soft(Age = 65, BMI = 36, PrevTx = 2, AbdoSurg = 1, Albumin = 29, Dx = 0, ICU = 0, Admitted = 1, MELD = 32, LifeSupport = 0, Encephalopathy = 1, PVThrombosis = 1, Ascites = 1), 37)
expect_equal(soft2(PSoft = 4, PortalBleed = 0, DonorAge = 61, DonorCVA = 1, DonorSCr = 140, National = 1, CIT = 12), 13)
})
test_that("Pedi-SOFT score gives correct results", {
expect_equal(pedi_soft(CTVG = 0, Weight = 31.5, Dx = 0, LifeSupport = 0, PrevTx = 0), 0)
expect_equal(pedi_soft(CTVG = 0, Weight = 17.6, Dx = 1, LifeSupport = 0, PrevTx = 0), 17)
expect_equal(pedi_soft(CTVG = 1, Weight = 4.9, Dx = 0, LifeSupport = 0, PrevTx = 1), 25)
expect_equal(pedi_soft(CTVG = 1, Weight = 5.4, Dx = 1, LifeSupport = 0, PrevTx = 0), 27)
expect_equal(pedi_soft(CTVG = 1, Weight = 5.9, Dx = 0, LifeSupport = 1, PrevTx = 1), 52)
})
|
nearest_spd <- function(A) {
stopifnot(is.numeric(A), is.matrix(A))
eps <- .Machine$double.eps
m <- nrow(A); n <- ncol(A)
if (m != n) {
stop("Argument 'A' must be a square matrix.")
} else if (n == 1 && A <= 0)
return(as.matrix(eps))
B <- (A + t(A)) / 2
svdB <- svd(B)
H <- svdB$v %*% diag(svdB$d) %*% t(svdB$v)
Ahat <- (B + H) / 2
Ahat <- (Ahat + t(Ahat)) / 2
k <- 0; not_pd <- TRUE
while (not_pd) {
k <- k + 1
try_R <- try(chol(Ahat), silent = TRUE)
if(inherits(try_R, "try-error")) {
mineig <- min(eigen(Ahat, symmetric = TRUE, only.values = TRUE)$values)
Ahat = Ahat + (-mineig*k^2 + eps(mineig)) * diag(1, n)
} else
not_pd <- FALSE
}
Ahat
}
|
test_that("AUC prep arranges data", {
test_dat <- dplyr::tibble(
delay = base::rep(base::c(1 / 7, 1, 2, 4, 26, 52), 2),
indiff = base::c(base::c(95, 75, 50, 20, 5, 1), c(95, 75, 50, 20, 5, 1) + .25),
sub = base::c(base::rep(1, 6), base::rep(2, 6))
) %>%
dplyr::arrange(indiff)
expect_equivalent(
prep_AUC(test_dat, "indiff", "delay", "sub", prob_disc = FALSE),
test_dat %>% dplyr::arrange(sub, delay)
)
expect_equivalent(
prep_AUC(test_dat, "indiff", "delay", "sub", prob_disc = TRUE),
test_dat %>% dplyr::arrange(sub, delay)
)
})
|
pls_loading <- function(pls){
T <- data.frame(pls$T)
P <- data.frame(pls$P)
U <- data.frame(pls$U)
Q <- data.frame(pls$Q)
n <- nrow(T)
PLS_loadings <- NULL
p_PLS <- NULL
for(i in 1:ncol(T)){
Wx <- P[,i];Wy <- Q[,i]; S <- U[,i]
lambda <- stats::cov(T[,i], U[,i])
loading <- (sqrt(n-1)*lambda*Wx)/as.numeric(sqrt(t(S)%*%S))
p <- 2*stats::pt(abs(loading)*sqrt(n-2)/sqrt(1-loading^2), n-2, lower.tail=FALSE)
PLS_loadings <- cbind(PLS_loadings,loading)
p_PLS <- cbind(p_PLS,p)
}
pls$loading$R <- PLS_loadings
pls$loading$p.value <- p_PLS
return(pls)
}
|
xxirt_compute_itemprobs <- function( item_list, items, Theta, ncat,
partable, partable_index, item_index=NULL )
{
TP <- nrow(Theta)
maxK <- max(ncat)
if ( is.null(item_index) ){
I <- length(items)
item_index <- 1:I
}
I <- length(item_index)
probs <- array( 0, dim=c(I,maxK,TP) )
for (jj in 1:I){
ii <- item_index[jj]
item_ii <- item_list[[ii]]
par_ii <- partable[ partable_index[[ii]], "value" ]
arg_ii <- list( par=par_ii, Theta=Theta, ncat=ncat[ii] )
probs_ii <- do.call( item_ii$P, arg_ii )
probs[ jj, 1:ncat[ii],] <- t(probs_ii)
}
return(probs)
}
|
camelCase <- function(x) {
gsub("_(.)", "\\U\\1", x, perl = TRUE)
}
toCamel <- function(x) {
if (is.list(x)) {
x[] <- lapply(x, toCamel)
}
if (!is.null(names(x))) {
names(x) <- camelCase(names(x))
}
x
}
|
context("check if the fluxing functions add new links to the food web")
load("groups.level.RData")
test_that('zeros are stills zeros',{
mat.fluxes = fluxing(groups.level$mat,
groups.level$biomasses,
0.71*groups.level$bodymasses,
groups.level$efficiencies)
zero.flux = which(mat.fluxes == 0)
zero.binary = which(groups.level$mat == 0)
expect_equal(zero.flux, zero.binary)
})
|
esize_T_ci_n <- function(lineup_table, alpha){
N = sum(lineup_table)
a <- 4/N
b <- sum((lineup_table/N)^3)
c <- (sum((lineup_table/N)^2)^2)
var_i <- a*(b-c)
sd_i <- sqrt(var_i)
ci_low <- i_esize_T(lineup_table) + qnorm((1-alpha)/2)*sd_i
ci_high <- i_esize_T(lineup_table) + qnorm(alpha+((1-alpha)/2))*sd_i
e_ci <- list(ci_low = 1/(1-ci_low), ci_high = 1/(1-ci_high))
}
|
hidden_pinv <- function(A){
return(aux_pinv(A))
}
hidden_vech <- function(A, diag=TRUE){
if ((!is.matrix(A))||(nrow(A)!=ncol(A))){
stop("* hidden_vech : input should be a square matrix.")
}
mydiag = as.logical(diag)
return(A[base::lower.tri(A, diag=mydiag)])
}
hidden_ivech <- function(a, diag=TRUE){
k = length(a)
if (diag){
n = round((-1 + sqrt(1+(8*k)))/2)
output = array(0,c(n,n))
output[lower.tri(output, diag = TRUE)] = a
output = output + t(output)
diag(output) = diag(output)/2
} else {
n = round((1+sqrt(1+8*k))/2)
output = array(0,c(n,n))
output[lower.tri(output, diag = FALSE)] = a
output = output + t(output)
}
return(output)
}
hidden_lab2ind <- function(label){
ulabel = base::unique(label)
nlabel = length(ulabel)
index = list()
for (k in 1:nlabel){
index[[k]] = which(label==ulabel[k])
}
return(index)
}
hidden_ind2lab <- function(index){
K = length(index)
N = sum(unlist(lapply(index, length)))
output = rep(0,N)
for (k in 1:K){
output[index[[k]]] = k
}
return(output)
}
hidden_subsetid <- function(n, k){
return(base::split(base::sample(n), base::sort(n%%k)))
}
hidden_geigen <- function(A, B, normalize=TRUE){
n = nrow(A)
runs = cpp_geigen(A,B)
tval = as.vector(base::Re(runs$values))[n:1]
tvec = base::Re(runs$vectors)[,n:1]
if (normalize){
for (i in 1:n){
tgt = as.vector(tvec[,i])
tvec[,i] = tgt/sqrt(sum(tgt^2))
}
}
output = list()
output$values = tval
output$vectors = tvec
return(output)
}
hidden_knn <- function(dat, nnbd=2, ...){
nnbd = round(nnbd)
return(RANN::nn2(dat, k=nnbd, ...))
}
hidden_knee_clamped_basic <- function(x, y){
m = length(x)
c = x[1]
d = y[1]
a = x[m]
b = y[m]
y2 = (((b-d)/(a-c))*(x-c))+d
return(sum((y-y2)^2))
}
hidden_knee_clamped <- function(x, y){
x = as.vector(x)
y = as.vector(y)
n = length(x)
if (n < 3){
stop("* knee point detection : length must be larger than 2.")
}
scores = rep(Inf, n)
for (i in 2:(n-1)){
x.left = x[1:i]
y.left = y[1:i]
x.right = x[i:n]
y.right = y[i:n]
term1 = hidden_knee_clamped_basic(x.left, y.left)
term2 = hidden_knee_clamped_basic(x.right, y.right)
scores[i] = term1+term2
}
return(which.min(scores))
}
hidden_knn_binary <- function(data, nnbd=1, construction=c("or","and")){
n = base::nrow(data)
nnbd = max(round(nnbd), 1)
construction = match.arg(construction)
if (all(construction=="and")){
intersect = TRUE
} else {
intersect = FALSE
}
run_knn = RANN::nn2(data, k=nnbd+1)$nn.idx[,2:(nnbd+1)]-1
run_res = src_construct_by_knn(run_knn, intersect)
return(run_res)
}
|
simulate_Basic_Virus_Model_stochastic <- function(U = 1e+05, I = 0, V = 1, n = 0, dU = 0, dI = 1, dV = 2, b = 2e-05, p = 5, g = 1, tfinal = 30, rngseed = 123)
{
Basic_Virus_Model_fct <- function(y, parms, t)
{
with(as.list(c(y,parms)),
{
rates = c(b*V*U, b*g*V*U, dI*I, dU*U, dV*V, n, p*I)
return(rates)
}
)
}
transitions = list(c(I = +1,U = -1),
c(V = -1),
c(I = -1),
c(U = -1),
c(V = -1),
c(U = +1),
c(V = +1))
set.seed(rngseed)
varvec = c(U = U, I = I, V = V)
parvec = c(n = n, dU = dU, dI = dI, dV = dV, b = b, p = p, g = g)
simout = adaptivetau::ssa.adaptivetau(init.values = varvec, transitions = transitions,
rateFunc = Basic_Virus_Model_fct, params = parvec, tf = tfinal)
result <- list()
result$ts <- as.data.frame(simout)
return(result)
}
|
crm.plot <-
function (xx, xname = deparse(substitute(xx)), crm.mean = NULL,
crm.sd = NULL, n.sd = 2, crm.tol = NULL, ...)
{
if (is.null(crm.mean))
stop("The accepted CRM mean must be provided\n")
if (is.null(crm.sd) && is.null(crm.tol))
stop("Either the crm.sd or crm.tol must be provided\n")
temp.x <- remove.na(xx)
x <- temp.x$x[1:temp.x$n]
n <- temp.x$n
xmean <- mean(x)
xsd <- sqrt(var(x))
xrsd <- round(100 * xsd/xmean, 2)
xmed <- median(x)
xmad <- mad(x)
rrsd <- round(100 * xmad/xmed, 2)
if (is.null(crm.sd))
n.sd = NULL
cat(" Control Reference Material:", xname, "\n\t\t\t\t Mean =",
crm.mean, "\t SD =", crm.sd, "\t N.SD =", n.sd, "\tPercent tolerance =",
crm.tol, "\n")
cat(" This project:\t N =", n, "\t Mean =", signif(xmean, 3), "\t SD =",
signif(xsd, 3), "\t RSD% =", xrsd, "\n\t\t\t\tMedian =", signif(xmed, 3),
"\tMAD =", signif(xmad, 3), "\trRSD% =", rrsd, "\n")
x.range <- range(x)
if (!is.null(crm.sd)) {
hitol <- crm.mean + n.sd * crm.sd
lotol <- crm.mean - n.sd * crm.sd
plot.min <- min(x.range[1], lotol)
plot.max <- max(x.range[2], hitol)
ylabel <- paste(xname, ", red lines indicate +/-", n.sd,
" SD limits", sep = "")
plotid <- 1
}
if (!is.null(crm.tol)) {
x <- 100 * (abs(x - crm.mean)/crm.mean)
plot.min <- 0
plot.max <- max(max(x), crm.tol)
ylabel <- "Percentage absolute difference relative to accepted value"
plotid <- 2
}
plot(seq(1:n), x, ylim = c(plot.min, plot.max), ylab = ylabel,
xlab = paste("Ordered determinations of", xname), ...)
if (plotid == 1) {
abline(h = crm.mean, col = 3, lty = 3)
abline(h = hitol, col = 2, lty = 2)
abline(h = lotol, col = 2, lty = 2)
}
else {
abline(h = crm.tol, col = 2, lty = 2)
}
invisible()
}
|
`print.summary.segmented` <-
function(x, short = x$short, var.diff = x$var.diff,
digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"),...){
cat("\n\t***Regression Model with Segmented Relationship(s)***\n\n")
cat( "Call: \n" )
print( x$call )
cat("\nEstimated Break-Point(s):\n ")
est.psi<-x$psi[,-1,drop=FALSE]
rownames(est.psi)<-rownames(x$psi)
print(round(est.psi,3))
if(short){
cat("\nDifference-in-slopes parameter(s):\n")
nome<-rownames(x$psi)
nomiU<-rownames(x$gap)
print(x$Ttable[nomiU,])
} else {cat("\nMeaningful coefficients of the linear terms:\n")
if(is.null(dim(x$Ttable))){
print(x$Ttable)
} else {
printCoefmat(x$Ttable, digits = digits, signif.stars = signif.stars,na.print = "NA", ...)
}
}
if("summary.lm"%in%class(x)){
if(var.diff){
for(i in 1:length(x$sigma.new)){
cat("\nResidual standard error ",i,":", format(signif(x$sigma.new[i],
digits)), "on", x$df.new[i], "degrees of freedom")}
cat("\n")
} else {
cat("\nResidual standard error:", format(signif(x$sigma,
digits)), "on", x$df[2], "degrees of freedom\n")}
if (!is.null(x$fstatistic)) {
cat("Multiple R-Squared:", formatC(x$r.squared, digits = digits))
cat(", Adjusted R-squared:", formatC(x$adj.r.squared,
digits = digits), "\n")}
}
if("summary.glm"%in%class(x)){
cat("\n(Dispersion parameter for ", x$family$family, " family taken to be ",
format(x$dispersion), ")\n\n", apply(cbind(paste(format.default(c("Null",
"Residual"), width = 8, flag = ""), "deviance:"),
format(unlist(x[c("null.deviance", "deviance")]),
digits = max(5, digits + 1)), " on", format(unlist(x[c("df.null",
"df.residual")])), " degrees of freedom\n"),
1, paste, collapse = " "), "AIC: ", format(x$aic,
digits = max(4, digits + 1)), "\n", sep = "")
}
if("summary.Arima"%in%class(x)){
cm <- x$call$method
if (is.null(cm) || cm != "CSS")
cat("\nsigma^2 estimated as ", format(x$sigma2, digits = digits),
", log likelihood = ", format(round(x$loglik, 2)),
", aic = ", format(round(x$aic, 2)), "\n", sep = "")
else cat("\nsigma^2 estimated as ", format(x$sigma2, digits = digits),
", part log likelihood = ", format(round(x$loglik, 2)),
"\n", sep = "")
}
invisible(x)
if(x$n.boot>0){
cat("\nBoot restarting based on", x$n.boot, "samples. Last fit:")
}
cat("\nConvergence",if(x$conv.warn) "*not*" else NULL , "attained in",x$it,"iterations (rel. change",paste(signif(x$epsilon,5),")\n",sep=""))
}
|
times <- faithful$eruptions
gf_dhistogram( ~ times, binwidth = 0.25, alpha = 0.3) %>%
gf_dens( ~ times, size = 1, alpha = 0.9)
|
setGeneric("as.matrix")
as.matrix.pim.summary <- function(x, ...){
cbind(
Estimate = coef(x),
"Std. Error" = x@se,
"z value" = x@zval,
"Pr(>|z|)" = x@pr
)
}
setMethod("as.matrix",
"pim.summary",
as.matrix.pim.summary)
setMethod("as.matrix",
"pim",
function(x, ...){
as.matrix(summary(x))
}
)
|
context("Setting and getting node attributes")
test_that("setting node attributes is possible", {
graph <-
create_graph() %>%
add_path(n = 8)
graph_set_a <-
set_node_attrs(
graph = graph,
nodes = 1,
node_attr = value,
values = 5)
expect_equal(
graph_set_a$nodes_df[
which(graph_set_a$nodes_df$id == 1), 4], 5)
graph_set_a <-
set_node_attrs(
graph = graph,
nodes = 1,
node_attr = value,
values = 8)
expect_equal(
graph_set_a$nodes_df[
which(graph_set_a$nodes_df$id == 1), 4], 8)
graph_select_a <-
select_nodes(
graph = graph,
nodes = 1)
graph_select_a <-
set_node_attrs_ws(
graph = graph_select_a,
node_attr = value,
value = 5)
expect_equal(
graph_select_a$nodes_df[
which(graph_select_a$nodes_df$id == 1), 4], 5)
graph_set_all <-
set_node_attrs(
graph = graph,
node_attr = value,
values = 5)
expect_true(
all(graph_set_all$nodes_df$value == 5))
graph_node_selection <-
graph %>%
select_nodes(nodes = 1) %>%
set_node_attrs_ws(
node_attr = value,
value = 5)
expect_equal(
graph_node_selection$nodes_df[
which(graph_node_selection$nodes_df[, 1] == 1), 4], 5)
expect_error(
set_node_attrs(
graph = graph,
nodes = 1,
node_attr = value,
values = c(1, 2)))
})
test_that("setting edge attributes is possible", {
graph <-
create_graph() %>%
add_path(n = 8)
graph_set_a_1 <-
set_edge_attrs(
graph = graph,
from = 1,
to = 2,
edge_attr = value,
values = 5)
expect_equal(
graph_set_a_1$edges_df[
which(graph_set_a_1$edges_df$from == 1 &
graph_set_a_1$edges_df$to == 2), 5], 5)
graph_set_a_1 <-
set_edge_attrs(
graph = graph_set_a_1,
from = 1,
to = 2,
edge_attr = value,
values = 8)
expect_equal(
graph_set_a_1$edges_df[
which(graph_set_a_1$edges_df$from == 1 &
graph_set_a_1$edges_df$to == 2), 5], 8)
graph_select_a_1 <-
select_edges(
graph = graph,
from = 1,
to = 2)
graph_select_a_1 <-
set_edge_attrs_ws(
graph = graph_select_a_1,
edge_attr = value,
value = 5)
expect_equal(
graph_select_a_1$edges_df[
which(graph_select_a_1$edges_df$from == 1 &
graph_select_a_1$edges_df$to == 2), 5], 5)
graph_set_all <-
set_edge_attrs(
graph = graph,
edge_attr = value,
values = 5)
expect_true(
all(
graph_set_all$edges_df$value == 5))
graph_edge_selection <-
graph %>%
select_edges(
from = 1,
to = 2) %>%
set_edge_attrs_ws(
edge_attr = value,
value = 5)
expect_equal(
graph_edge_selection$edges_df[
which(graph_edge_selection$edges_df$from == 1 &
graph_edge_selection$edges_df$to == 2), 5], 5)
})
test_that("Getting node attributes is possible", {
graph <-
create_graph() %>%
add_gnm_graph(
n = 4,
m = 4,
node_data = node_data(
value = 1:4),
set_seed = 23)
all_nodes <-
get_node_attrs(
graph = graph,
node_attr = value)
expect_is(
all_nodes, "integer")
expect_equal(
length(all_nodes), 4)
expect_true(
all(1:4 %in% names(all_nodes)))
expect_equal(
all_nodes[[1]], 1)
expect_equal(
all_nodes[[2]], 2)
expect_equal(
all_nodes[[3]], 3)
expect_equal(
all_nodes[[4]], 4)
nodes_1_3 <-
get_node_attrs(
graph = graph,
node_attr = value,
nodes = c(1, 3))
expect_equal(
length(nodes_1_3), 2)
expect_true(
all(c(1, 3) %in% names(nodes_1_3)))
expect_equal(
nodes_1_3[[1]], 1)
expect_equal(
nodes_1_3[[2]], 3)
expect_error(
get_node_attrs(
graph = graph,
node_attr = id))
})
test_that("Getting edge attributes is possible", {
graph <-
create_graph() %>%
add_n_nodes(n = 4) %>%
{
edges <-
create_edge_df(
from = c(1, 2, 1, 4),
to = c(2, 3, 4, 3),
rel = "rel")
add_edge_df(
graph = .,
edge_df = edges)
} %>%
set_edge_attrs(
edge_attr = value,
values = 1.6,
from = 1,
to = 2) %>%
set_edge_attrs(
edge_attr = value,
values = 4.3,
from = 1,
to = 4) %>%
set_edge_attrs(
edge_attr = value,
values = 2.9,
from = 2,
to = 3) %>%
set_edge_attrs(
edge_attr = value,
values = 8.4,
from = 4,
to = 3)
all_edges <-
get_edge_attrs(
graph = graph,
edge_attr = value)
expect_is(
all_edges, "numeric")
expect_equal(
length(all_edges), 4)
expect_true(
all(c("1->2", "2->3", "1->4", "4->3") %in%
names(all_edges)))
expect_equal(
all_edges[[1]], 1.6)
expect_equal(
all_edges[[2]], 2.9)
expect_equal(
all_edges[[3]], 4.3)
expect_equal(
all_edges[[4]], 8.4)
some_edges <-
get_edge_attrs(
graph = graph,
edge_attr = value,
from = c(1, 2),
to = c(2, 3))
expect_equal(
length(some_edges), 2)
expect_true(
all(c("1->2", "2->3") %in%
names(some_edges)))
expect_equal(
some_edges[[1]], 1.6)
expect_equal(
some_edges[[2]], 2.9)
expect_error(
get_edge_attrs(
graph = graph,
edge_attr = from))
expect_error(
get_edge_attrs(
graph = graph,
edge_attr = to))
expect_error(
get_edge_attrs(
graph = graph,
edge_attr = value,
from = c(1, 2),
to = 2))
})
test_that("Getting node attributes with a selection is possible", {
graph <-
create_graph() %>%
add_gnm_graph(
n = 4, m = 4,
node_data = node_data(value = 1:4),
set_seed = 23
) %>%
select_nodes_by_id(nodes = c(1, 3))
nodes_1_3 <-
get_node_attrs_ws(
graph = graph,
node_attr = value)
expect_is(
nodes_1_3, "integer")
expect_equal(
length(nodes_1_3), 2)
expect_true(
all(c(1, 3) %in% names(nodes_1_3)))
expect_equal(
nodes_1_3[[1]], 1)
expect_equal(
nodes_1_3[[2]], 3)
expect_error(
get_node_attrs_ws(
graph = graph %>%
clear_selection(),
node_attr = value))
expect_error(
get_node_attrs_ws(
graph = graph,
node_attr = id))
})
test_that("Getting edge attributes with a selection is possible", {
graph <-
create_graph() %>%
add_n_nodes(n = 4) %>%
{
edges <-
create_edge_df(
from = c(1, 2, 1, 4),
to = c(2, 3, 4, 3),
rel = "rel")
add_edge_df(
graph = .,
edge_df = edges)
} %>%
set_edge_attrs(
edge_attr = value,
values = 1.6,
from = 1,
to = 2) %>%
set_edge_attrs(
edge_attr = value,
values = 4.3,
from = 1,
to = 4) %>%
set_edge_attrs(
edge_attr = value,
values = 2.9,
from = 2,
to = 3) %>%
set_edge_attrs(
edge_attr = value,
values = 8.4,
from = 4,
to = 3) %>%
select_edges(
from = c(1, 2),
to = c(2, 3))
edge_attr_values <-
graph %>%
get_edge_attrs_ws(
edge_attr = value)
expect_is(
edge_attr_values, "numeric")
expect_equal(
length(edge_attr_values), 2)
expect_true(
all(c("1->2", "2->3") %in% names(edge_attr_values)))
expect_equal(
edge_attr_values[[1]], 1.6)
expect_equal(
edge_attr_values[[2]], 2.9)
expect_error(
get_edge_attrs_ws(
graph = graph %>%
clear_selection(),
edge_attr = value))
})
|
.remGrpSam <- function(spectra, kill, group = TRUE) {
.chkArgs(mode = 0L)
if (is.character(kill)) {
drop <- NA_integer_
for (n in 1:length(kill)) {
if (group) more <- grep(kill[n], spectra$groups)
if (!group) more <- grep(kill[n], spectra$names)
drop <- c(drop, more)
}
kill <- drop[-1]
}
if ((length(kill) == 0L) & (group)) stop("No matching groups found to remove")
if ((length(kill) == 0L) & (!group)) stop("No matching samples found to remove")
if (inherits(spectra, "Spectra")) {
spectra$data <- spectra$data[-kill, ]
spectra$sym <- spectra$sym[-kill]
spectra$alt.sym <- spectra$alt.sym[-kill]
}
if (inherits(spectra, "Spectra2D")) spectra$data <- spectra$data[-kill, drop = FALSE]
spectra$names <- spectra$names[-kill]
spectra$groups <- spectra$groups[-kill, drop = TRUE]
spectra$colors <- spectra$colors[-kill]
if (length(spectra$names) == 0) warning("You have removed all your samples!")
jnk <- .extraData(spectra)
chkSpectra(spectra)
return(spectra)
}
|
getVars <- function(
pcaobj,
components = NULL)
{
if (!is.null(components)) {
return(pcaobj$variance[components])
} else if (is.null(components)) {
return(pcaobj$variance)
}
}
|
cvstats=function(cvstuff,foldid,nfolds,lambda,nz,grouped,...){
if (grouped){
nlams=rep(dim(cvstuff$cvraw)[2],nfolds)
cvstuff= cvcompute(cvstuff, foldid, nlams)
}
cvm=with(cvstuff,apply(cvraw, 2, weighted.mean, w = weights, na.rm = TRUE))
cvsd=with(cvstuff, sqrt(apply(scale(cvraw, cvm, FALSE)^2, 2, weighted.mean,
w = weights, na.rm = TRUE)/(N - 1)))
nas=is.na(cvsd)
if(any(nas)){
lambda=lambda[!nas]
cvm=cvm[!nas]
cvsd=cvsd[!nas]
nz=nz[!nas]
}
list(lambda = lambda, cvm = cvm, cvsd = cvsd, cvup = cvm +
cvsd, cvlo = cvm - cvsd, nzero = nz)
}
|
NULL
codepipeline <- function(config = list()) {
svc <- .codepipeline$operations
svc <- set_config(svc, config)
return(svc)
}
.codepipeline <- list()
.codepipeline$operations <- list()
.codepipeline$metadata <- list(
service_name = "codepipeline",
endpoints = list("*" = list(endpoint = "codepipeline.{region}.amazonaws.com", global = FALSE), "cn-*" = list(endpoint = "codepipeline.{region}.amazonaws.com.cn", global = FALSE), "us-iso-*" = list(endpoint = "codepipeline.{region}.c2s.ic.gov", global = FALSE), "us-isob-*" = list(endpoint = "codepipeline.{region}.sc2s.sgov.gov", global = FALSE)),
service_id = "CodePipeline",
api_version = "2015-07-09",
signing_name = "codepipeline",
json_version = "1.1",
target_prefix = "CodePipeline_20150709"
)
.codepipeline$service <- function(config = list()) {
handlers <- new_handlers("jsonrpc", "v4")
new_service(.codepipeline$metadata, handlers, config)
}
|
"post.pairs"<-function(postP, threshold=0, rel="PO"){
if(is.list(postP)){
stop("P must be samples from the JOINT posterior of parentage")
}else{
post<-matrix(0, dim(postP)[1], dim(postP)[1])
if(rel=="PO"){
for(i in 1:(dim(postP)[2]/2)){
post<-post+outer(rownames(postP), postP[,i*2-1], "==")
post<-post+outer(rownames(postP), postP[,i*2], "==")
}
post<-post+t(post)
}else{
dummyD<-paste("D", letters, 1:dim(postP)[1], letters)
dummyS<-paste("S", letters, 1:dim(postP)[1], letters)
for(i in 1:(dim(postP)[2]/2)){
usv<-which(postP[,i*2-1]=="us")
postP[,i*2-1][usv]<-dummyD[1:length(usv)]
usv<-which(postP[,i*2]=="us")
postP[,i*2][usv]<-dummyS[1:length(usv)]
op<-outer(postP[,i*2-1], postP[,i*2-1], "==")+outer(postP[,i*2], postP[,i*2], "==")
diag(op)<-0
if(rel=="S"){
op<-(op>0)
}
if(rel=="FS"){
op<-(op==2)
}
if(rel=="HS"){
op<-(op==1)
}
post<-post+op
}
}
}
rownames(post)<-rownames(postP)
post[which(upper.tri(post)==TRUE)]<-0
P1<-rownames(post)[which(post>(dim(postP)[2]*threshold/2), arr.ind = TRUE)[,1]]
P2<-rownames(post)[which(post>(dim(postP)[2]*threshold/2), arr.ind = TRUE)[,2]]
sig.pairs<-cbind(P1, P2)
list(P=sig.pairs, prob=(2*post/dim(postP)[2])[which(post>(dim(postP)[2]*threshold/2))])
}
|
context("soundex")
test_that("Test that soundex works", {
skip_on_cran()
test <- read.csv("soundex.csv", comment.char = "
for(i in 1:nrow(test)) {
if(is.na(test$value[i])) {
expect_warning(testValue <- soundex(test$word[i]))
expect_true(is.na(testValue))
} else
expect_true(soundex(test$word[i]) == test$value[i])
}
for(i in 1:nrow(test)) {
if(is.na(test$value[i])) {
expect_false(is.na(testValue <- soundex(test$word[i], clean = FALSE)))
} else
expect_true(soundex(test$word[i], clean = FALSE) == test$value[i])
}
})
test_that("The soundex algorithm implementation can handle NAs", {
skip_on_cran()
test_data <- soundex(NA_character_)
expect_true(is.na(test_data))
})
test_that("The soundex algorithm implementation can handle NULLs", {
skip_on_cran()
test_data <- soundex(NULL)
expect_true(is.na(test_data))
})
test_that("Test that refined soundex works", {
skip_on_cran()
test <- read.csv("soundex-refined.csv", comment.char = "
for(i in 1:nrow(test)) {
if(is.na(test$value[i])) {
expect_warning(testValue <- refinedSoundex(test$word[i]))
expect_true(is.na(testValue))
} else
expect_true(refinedSoundex(test$word[i]) == test$value[i])
}
for(i in 1:nrow(test)) {
if(is.na(test$value[i])) {
expect_false(is.na(testValue <- refinedSoundex(test$word[i], clean = FALSE)))
} else
expect_true(refinedSoundex(test$word[i], clean = FALSE) == test$value[i])
}
})
test_that("The refined soundex algorithm implementation can handle NAs", {
skip_on_cran()
test_data <- refinedSoundex(NA_character_)
expect_true(is.na(test_data))
})
test_that("The refined soundex algorithm implementation can handle NULLs", {
skip_on_cran()
test_data <- refinedSoundex(NULL)
expect_true(is.na(test_data))
})
|
formatter_freq <- function(x) {
return(format(as.character(x), width = 13, justify = "centre"))
}
formatter <- function(x) {
return(format(as.character(x), width = 13, justify = "right"))
}
percent <- function(x, y) {
out <- round((x / y) * 100, 2)
return(out)
}
formata <- function(x, round, width, justify = "centre") {
return(format(as.character(round(x, round)), width = width, justify = justify))
}
formatas <- function(x, round, width, justify = "centre") {
return(format(x, width = width, justify = justify))
}
bin_size <- function(data, bins) {
return((max(data, na.rm = TRUE) - min(data, na.rm = TRUE)) / bins)
}
intervals <- function(data, bins, na.rm = TRUE) {
binsize <- bin_size(data, bins)
bin <- bins - 1
interval <- min(data)
for (i in seq_len(bin)) {
out <- interval[i] + binsize
interval <- c(interval, out)
}
interval <- c(interval, max(data))
return(interval)
}
freq <- function(data, bins, inta) {
result <- c()
for (i in seq_len(bins)) {
k <- i + 1
freq <- data >= inta[i] & data <= inta[k]
out <- length(data[freq])
result <- c(result, out)
}
return(result)
}
div_by <- function(x) {
1 / x
}
standardize <- function(x, avg, stdev, p) {
((x - avg) / stdev) ^ p
}
sums <- function(x, q) {
avg <- mean(x)
stdev <- stats::sd(x)
result <- sum(sapply(x, standardize, avg, stdev, q))
return(result)
}
md_helper <- function(x, y) {
abs(x - y)
}
ds_std_error <- function(x) {
stats::sd(x) / (length(x) ^ 0.5)
}
uss <- function(x, y) {
(x - y) ^ 2
}
stat_uss <- function(x) {
sum(x ^ 2)
}
formatl <- function(x) {
return(format(format(x, nsmall = 2), width = 20, justify = "left"))
}
formatol <- function(x, w) {
format(as.character(x), width = w, justify = "centre")
}
formatr <- function(x, w) {
format(rounda(x), nsmall = 2, width = w, justify = "right")
}
formatc <- function(x, w) {
if (is.numeric(x)) {
ret <- format(as.character(round(x, 2)), width = w, justify = "centre")
} else {
ret <- format(as.character(x), width = w, justify = "centre")
}
return(ret)
}
formatnc <- function(x, w) {
format(format(round(x, 2), nsmall = 2), width = w, justify = "centre")
}
fs <- function() {
x <- rep(" ")
}
formats <- function() {
x <- rep(" ")
}
format_gap <- function(w) {
x <- rep("", w)
}
return_pos <- function(data, number) {
out <- c()
for (i in seq_len(length(data))) {
if (data[i] == number) {
out <- c(out, i)
}
}
return(out)
}
row_pct <- function(mat, tot) {
rows <- dim(mat)[1]
l <- length(tot)
result <- c()
for (i in seq_len(rows)) {
diva <- mat[i, ] / tot[i]
result <- rbind(result, diva)
}
rownames(result) <- NULL
return(result)
}
col_pct <- function(mat, tot) {
cols <- dim(mat)[2]
l <- length(tot)
result <- c()
for (i in seq_len(cols)) {
diva <- mat[, i] / tot[i]
result <- cbind(result, diva)
}
colnames(result) <- NULL
return(result)
}
rounda <- function(x) {
round(x, 2)
}
l <- function(x) {
x <- as.character(x)
k <- grep("\\$", x)
if (length(k) == 1) {
temp <- strsplit(x, "\\$")
out <- temp[[1]][2]
} else {
out <- x
}
return(out)
}
fround <- function(x) {
format(round(x, 2), nsmall = 2)
}
seqlp <- function(mean, sd, el) {
if (el > 4) {
lmin <- mean - (el * sd)
lmax <- mean + (el * sd)
} else {
lmin <- mean - (4 * sd)
lmax <- mean + (4 * sd)
}
l <- seq(lmin, lmax, sd)
return(l)
}
xmmp <- function(mean, sd, el) {
if (el > 4) {
xmin <- mean - (el * sd)
xmax <- mean + (el * sd)
} else {
xmin <- mean - (4 * sd)
xmax <- mean + (4 * sd)
}
out <- c(xmin, xmax)
return(out)
}
seql <- function(mean, sd) {
lmin <- mean - (5 * sd)
lmax <- mean + (5 * sd)
l <- seq(lmin, lmax, sd)
return(l)
}
xmm <- function(mean, sd) {
xmin <- mean - (5 * sd)
xmax <- mean + (5 * sd)
out <- c(xmin, xmax)
return(out)
}
seqln <- function(mean, sd) {
lmin <- mean - 3 * sd
lmax <- mean + 3 * sd
l <- seq(lmin, lmax, sd)
return(l)
}
xmn <- function(mean, sd) {
xmin <- mean - 3 * sd
xmax <- mean + 3 * sd
out <- c(xmin, xmax)
return(out)
}
trimmed_mean <- function(x, na.rm = FALSE) {
if (na.rm) {
x <- stats::na.omit(x)
}
mean(x, trim = 0.05)
}
quant1 <- function(x, na.rm = FALSE) {
if (na.rm) {
x <- stats::na.omit(x)
}
stats::quantile(x, probs = 0.25)
}
quant3 <- function(x, na.rm = FALSE) {
if (na.rm) {
x <- stats::na.omit(x)
}
stats::quantile(x, probs = 0.75)
}
string_to_name <- function(x, index = 1) {
rlang::sym(x$varnames[index])
}
check_suggests <- function(pkg) {
pkg_flag <- tryCatch(utils::packageVersion(pkg), error = function(e) NA)
if (is.na(pkg_flag)) {
msg <- message(paste0('\n', pkg, ' must be installed for this functionality.'))
if (interactive()) {
message(msg, "\nWould you like to install it?")
if (utils::menu(c("Yes", "No")) == 1) {
utils::install.packages(pkg)
} else {
stop(msg, call. = FALSE)
}
} else {
stop(msg, call. = FALSE)
}
}
}
check_df <- function(data) {
data_name <- deparse(substitute(data))
if (!is.data.frame(data)) {
rlang::abort(paste0(data_name, ' must be a `data.frame` or `tibble`.'))
}
}
check_numeric <- function(data, var, var_name) {
vary <- rlang::enquo(var)
ndata <- dplyr::pull(data, !! vary)
var_class <- class(ndata)
msg <- paste0(var_name, ' is not a continuous variable. The function expects an object of type `numeric` or `integer` but ', var_name, ' is of type `', var_class, '`.')
if (!is.numeric(ndata)) {
rlang::abort(msg)
}
}
check_factor <- function(data, var, var_name) {
vary <- rlang::enquo(var)
fdata <- dplyr::pull(data, !! vary)
var_class <- class(fdata)
msg <- paste0(var_name, ' is not a categorical variable. The function expects an object of type `factor` but ', var_name, ' is of type `', var_class, '`.')
if (!is.factor(fdata)) {
rlang::abort(msg)
}
}
ds_rule <- function(text = NULL) {
con_wid <- options()$width
text_len <- nchar(text) + 2
dash_len <- (con_wid - text_len) / 2
cat(paste(rep("-", dash_len)), ' ', text, ' ',
paste(rep("-", dash_len)), sep = "")
}
|
pop_Nancestors <- function(ped, reference, nboot = 10000L, seed = NULL, skip_Ng = FALSE) {
check_basic(ped, "id", "dam", "sire")
ref <- check_reference(ped, reference, "reference")
check_int(nboot)
if (nboot < 100 || nboot > 100000000) stop("Number of bootstrap iterations expected in the range[100, 100000000]")
if (!base::is.null(seed)) check_int(seed)
check_bool(skip_Ng)
rp <- ped[which(ped[reference] == TRUE & !is.na(ped[reference])), ]$id
.Call(`_purgeR_ancestors`, ped, ref, rp - 1, nboot, seed, skip_Ng)
}
pop_Nf <- function(ped, reference) {
purgeR::pop_Nancestors(ped, reference, skip_Ng = TRUE)$Nf
}
pop_Nfe <- function(ped, reference) {
purgeR::pop_Nancestors(ped, reference, skip_Ng = TRUE)$Nfe
}
pop_Na <- function(ped, reference) {
purgeR::pop_Nancestors(ped, reference, skip_Ng = TRUE)$Na
}
pop_Nae <- function(ped, reference) {
purgeR::pop_Nancestors(ped, reference, skip_Ng = TRUE)$Nae
}
pop_Ng <- function(ped, reference, nboot = 10000L, seed = NULL) {
Ng_ <- purgeR::pop_Nancestors(ped, reference, nboot, seed, skip_Ng = FALSE)
list(
Ng = Ng_$Ng,
se_Ng = Ng_$se_Ng
)
}
|
plotbFbEVSn=function(n,pF,cF,pE,cE, shape1F,shape2F,shape1E,shape2E, colF="red",colE="green",...){
pF=pF
pE=pE
cF=cF
cE=cE
bF=CritBoundaryF5(n=n, pF=pF, critF=cF, shape1=shape1F,shape2=shape2F)
bE=CritBoundaryE5(n=n, pE=pE, critE=cE, shape1=shape1E,shape2=shape2E)
t_col <- function(color, percent = 50, name = NULL) {
rgb.val <- col2rgb(color)
t.col <- rgb(rgb.val[1], rgb.val[2], rgb.val[3],
maxColorValue = 255,
alpha = (100-percent)*255/100,
names = name)
invisible(t.col)
}
mycolF <- t_col(colF, perc = 50, name = "lt.colF")
mycolE <- t_col(colE, perc = 50, name = "lt.colE")
plot(c(0:n),c(0:n), ylim=c(-0.5,max(c(bF,bE))),yaxt="n",xaxs="i",yaxs="i",type="n",
xlab="Number of enrolled patients",ylab="Decision boundaries",
main=paste0("Boundaries for futility (",colF,") and efficacy (",colE,")"),
sub=paste0("pF =",pF,", cF=",cF,", pE=",pE,", cE=",cE ),... )
axis(side=2,at=c(0:max(c(bF,bE))))
lines(c(1:n),bF, col=colF,lwd=3)
lines(c(1:n),bE, col=colE,lwd=3)
abline(v=0:n,lty=3,lwd=1,col="grey")
abline(h=c(0:max(c(bF,bE))), lty=3, lwd=1,col="grey")
polygon(c(1,c(1:n),n),c(-1,bF,-1), border = NA,col=mycolF)
polygon(c(1,c(1:n),n),c(max(c(bF,bE)),bE,max(c(bF,bE))), border = NA,col=mycolE)
}
|
flowPath <- function(x, p, ...) {
r <- raster(x)
if (length(p) > 1) {
p <- cellFromXY(r, p[1:2])
}
cell <- p
row <- rowFromCell(r, cell)
col <- colFromCell(r, cell)
nr <- nrow(r)
nc <- ncol(r)
path <- NULL
while (!is.na(x[cell])) {
path <- c(path, cell)
fd <- x[cell]
row <- if(fd %in% c(32, 64, 128)) row - 1 else
if(fd %in% c(8, 4, 2)) row + 1 else row
col <- if(fd %in% c(32, 16, 8)) col - 1 else
if(fd %in% c(128, 1, 2)) col + 1 else col
cell <- cellFromRowCol(r, row, col)
if (is.na(x[cell])) break
if (cell %in% path) break
}
return(path)
}
.flowPath1 <- function(x, p) {
out <- raster(x)
if (length(p) > 1) {
p <- cellFromXY(out, p[1:2])
}
row <- rowFromCell(out, p)
col <- colFromCell(out, p)
out[row, col] <- 1
while (!is.na(x[row, col])) {
out[row, col] <- 1
fdval <- x[row, col]
col <- if(fdval %in% c(32, 16, 8)) col - 1 else
if(fdval %in% c(128, 1, 2)) col + 1 else col
row <- if(fdval %in% c(32, 64, 128)) row - 1 else
if(fdval %in% c(8, 4, 2)) row + 1 else row
if (row < 1 || row > dim(x)[1] || col < 1 || col > dim(x)[2]) break
if (is.na(x[row, col])) break
if (!is.na(out[row, col])) break
}
return(out)
}
|
setClass(
"pomp",
slots=c(
data = "array",
times = "numeric",
t0 = "numeric",
timename = "character",
rinit = "pomp_fun",
rprocess = "rprocPlugin",
dprocess = "pomp_fun",
rmeasure = "pomp_fun",
dmeasure = "pomp_fun",
emeasure = "pomp_fun",
vmeasure = "pomp_fun",
rprior = "pomp_fun",
dprior = "pomp_fun",
skeleton = "skelPlugin",
partrans = "partransPlugin",
params = "numeric",
states = "array",
covar = "covartable",
accumvars = "character",
solibs = "list",
userdata = "list"
),
prototype=prototype(
data=array(data=numeric(),dim=c(0,0)),
times=numeric(),
t0=numeric(),
timename="time",
rinit=pomp_fun(slotname="rinit"),
rprocess=rproc_plugin(),
dprocess=pomp_fun(slotname="dprocess"),
rmeasure=pomp_fun(slotname="rmeasure"),
dmeasure=pomp_fun(slotname="dmeasure"),
emeasure=pomp_fun(slotname="emeasure"),
vmeasure=pomp_fun(slotname="vmeasure"),
rprior=pomp_fun(slotname="rprior"),
dprior=pomp_fun(slotname="dprior"),
skeleton=skel_plugin(),
partrans=parameter_trans(),
params=numeric(),
states=array(data=numeric(),dim=c(0,0)),
covar=covariate_table(),
accumvars=character(),
solibs=list(),
userdata=list()
),
validity=function (object) {
retval <- character(0)
if (length(object@times)<1)
retval <- append(retval,paste(sQuote("times"),"is a required argument"))
if (!is.numeric(object@params) || (length(object@params)>0 && is.null(names(object@params))))
retval <- append(retval,paste(sQuote("params"),"must be a named numeric vector"))
if (ncol(object@data)!=length(object@times))
retval <- append(retval,paste("the length of",sQuote("times"),"should match the number of observations"))
if (length(object@t0)<1)
retval <- append(retval,paste(sQuote("t0"),"is a required argument"))
if (!is.numeric(object@t0) || !is.finite(object@t0) || length(object@t0)>1)
retval <- append(retval,paste(sQuote("t0"),"must be a single number"))
if (object@t0 > object@times[1])
retval <- append(retval,paste("the zero-time",sQuote("t0"),
"must occur no later than the first observation"))
if (length(retval)==0) TRUE else retval
}
)
|
factor_scores_mctfa <- function(Y, g, q, pivec, A, xi, omega, D, v,
tau = NULL, clust = NULL, ...) {
if(!is.matrix(Y))
Y <- as.matrix(Y)
p <- ncol(Y)
n <- nrow(Y)
U <- array(0, c(n, q, g))
gamma <- array(0, c(p, q))
inv_D <- diag(1/diag(D))
for (i in 1 : g) {
gamma <- (inv_D - sweep(A, 1, diag(inv_D), '*') %*%
chol.inv(chol.inv(omega[,, i]) +
t(A * diag(inv_D)) %*% A) %*%
sweep(t(A), 2, diag(inv_D), '*')) %*% A %*% omega[,, i]
U[,, i] <- sweep(sweep(Y, 2, A %*% xi[, i, drop = F], '-') %*% gamma,
2, xi[, i, drop = F], "+")
}
if (is.null(tau)) {
tau <- tau.mctfa(Y, g, q, pivec, A, xi, omega, D, v)
}
if (is.null(clust)) {
clust <- apply(tau, 1, which.max)
}
UC <- array(0, c(n, q))
Umean <- array(0, c(n, q))
for (i in 1 : n) {
UC[i, ] <- U[i,, clust[i]]
Umean[i, ] <- tau[i, ] %*% t(matrix(U[i,, ], c(q, g)))
}
return(list(Uscores = U, Uclust = UC, Umean = Umean))
}
|
fac_and_contr <- function(x, levels, contrasts, ordered = FALSE) {
x <- factor(x, levels = levels, ordered = ordered)
contrasts(x) <- contrasts
return(x)
}
named_contr_sum <- function(x, scale = 1, return_contr = TRUE) {
x <- factor(x, ordered = FALSE)
if (!is.scalar(scale, 1)) {
stop("'scale' must be a single positive number")
}
n <- length(lvs <- reorder_ft(sort(levels(x))))
contr <- stats::contr.sum(lvs) * scale
colnames(contr) <- lvs[-n]
if (return_contr) return(contr)
return(fac_and_contr(x, lvs, contr, FALSE))
}
scaled_contr_poly <- function(x, scale = 1, return_contr = TRUE) {
if (sc <- is.scalar(x, 1)) {
lvs <- paste(1:x)
} else if (is.factor(x)) {
lvs <- setdiff(levels(x), NA)
} else {
lvs <- setdiff(x, NA)
}
if (!is.scalar(scale, 1)) {
stop("'scale' must be a single positive number")
}
if ((n <- length(lvs)) < 3) {
stop("Factors with fewer than 3 levels should be coded as unordered ",
"with sum contrasts")
}
contr <- stats::contr.poly(n)
contr <- scale(contr)[, 1:(n - 1)] * scale
if (!sc) rownames(contr) <- lvs
if (return_contr) return(contr)
return(fac_and_contr(x, lvs, contr, TRUE))
}
|
NULL
insert_ns <- function () {
curr_editor <- rstudioapi::getSourceEditorContext()
id <- curr_editor$id
sel_rng <- curr_editor$selection[[1]]$range
sel_text <- curr_editor$selection[[1]]$text
mod_text <- paste0("ns(", sel_text, ")")
rstudioapi::modifyRange(sel_rng, mod_text, id = id)
}
go_to <- function(
file,
wd = golem::get_golem_wd()
){
file <- path(
wd, file
)
if (! file_exists(file)){
message(file, "not found.")
}
stop_if_not(
rstudioapi::hasFun("navigateToFile"),
msg = "Your version of RStudio does not support `navigateToFile`"
)
rstudioapi::navigateToFile( file )
}
go_to_start <- function(){
go_to("dev/01_start.R")
}
go_to_dev <- function(){
go_to("dev/02_dev.R")
}
go_to_deploy <- function(){
go_to("dev/03_deploy.R")
}
go_to_run_dev <- function(){
go_to("dev/run_dev.R")
}
go_to_app_ui <- function(){
go_to("R/app_ui.R")
}
go_to_app_server <- function(){
go_to("R/app_server.R")
}
go_to_run_app <- function(){
go_to("R/run_app.R")
}
|
digit.curves <- function(start, curve, nPoints, closed=TRUE){
nPoints <- nPoints+2
if(!is.matrix(curve)) stop("Input must be a p-x-k matrix of curve coordinates")
nCurvePoints = NROW(curve)
if(nCurvePoints < 2) stop("curve matrix does not have enough points to estimate any interior points")
if(nPoints > (nCurvePoints - 1)) {
if((nCurvePoints - 1) == 1) nPoints = 1
if((nCurvePoints - 1) > 1) nPoints = nCurvePoints - 2
cat("\nWarning: because the number of desired points exceeds the number of curve points,")
cat("\nthe number of points will be truncated to", nPoints, "\n\n")
}
start <- as.numeric(start)
if(!setequal(start, curve[1,])) curve <- rbind(start, curve)
if(closed) curve <- rbind(curve, curve[1,])
res <- evenPts(curve, nPoints)
if(closed) res <- res[-NROW(res),]
res
}
|
staple_pdf <- function(input_directory = NULL, input_files = NULL,
output_filepath = NULL, overwrite = TRUE)
{
if(!is.null(input_directory) & !is.null(input_files)){
stop("One of input_directory and input_files has to be NULL.")
}
if(is.null(input_directory) & is.null(input_files)) {
input_directory <- tcltk::tk_choose.dir(caption = "Select directory which contains PDF fies")
}
if(!is.null(input_directory)){input_filepaths <- (Sys.glob(file.path(input_directory, "*.pdf")))}
if(!is.null(input_files)){input_filepaths <- input_files}
if(is.null(output_filepath)){
output_filepath <- tcltk::tclvalue(tcltk::tkgetSaveFile(filetypes = '{Pdf {.pdf}}'))
}
input_filepaths <- normalizePath(input_filepaths, mustWork = TRUE)
output_filepath <- normalizePath(output_filepath, mustWork = FALSE)
if(!overwrite & file.exists(output_filepath)){
stop(paste(output_filepath,'already exists. Set overwrite = TRUE to overwrite'))
}
system_command <- paste(pdftk_cmd(),
paste(shQuote(input_filepaths), collapse = " "),
"cat",
"output",
shQuote(output_filepath),
sep = " ")
system(command = system_command)
}
|
interactive_rect_grob <- function(...) {
grob_interactive(grid::rectGrob, ...)
}
drawDetails.interactive_rect_grob <- function(x, recording) {
dsvg_tracer_on()
NextMethod()
ids <- dsvg_tracer_off()
interactive_attr_toxml(x = x, ids = ids)
invisible()
}
|
getParPredReg <- function(object, newdata, ...) {
UseMethod("getParPredReg")
}
getParPredReg.default <- function(object, newdata, ...) {
pred <- stats::predict(object, newdata = newdata, ...)
if (is.matrix(pred) || is.data.frame(pred)) {
pred <- pred[, 1L, drop = TRUE]
}
mean(pred, na.rm = TRUE)
}
getParPredReg.gbm <- function(object, newdata, ...) {
invisible(utils::capture.output(
pred <- stats::predict(object, newdata = newdata, ...)
))
mean(pred, na.rm = TRUE)
}
getParPredReg.ksvm <- function(object, newdata, ...) {
mean(kernlab::predict(object, newdata = newdata, ...)[, 1L, drop = TRUE],
na.rm = TRUE)
}
getParPredReg.mars <- function(object, newdata, ...) {
mean(stats::predict(object,
newdata = data.matrix(newdata), ...)[, 1L, drop = TRUE],
na.rm = TRUE)
}
getParPredReg.ranger <- function(object, newdata, ...) {
mean(stats::predict(object, data = newdata, ...)$predictions, na.rm = TRUE)
}
getParPredReg.xgb.Booster <- function(object, newdata, ...) {
mean(stats::predict(object, newdata = newdata, ...),
na.rm = TRUE)
}
getParPredRegInvLink <- function(object, newdata, inv.link, ...) {
UseMethod("getParPredRegInvLink")
}
getParPredRegInvLink.default <- function(object, newdata, inv.link, ...) {
pred <- stats::predict(object, newdata = newdata, ...)
if (is.matrix(pred) || is.data.frame(pred)) {
pred <- pred[, 1L, drop = TRUE]
}
pred <- pred[!is.na(pred)]
mean(inv.link(pred))
}
getParPredRegInvLink.gbm <- function(object, newdata, inv.link, ...) {
invisible(utils::capture.output(
pred <- stats::predict(object, newdata = newdata, ...)
))
pred <- pred[!is.na(pred)]
mean(inv.link(pred))
}
getParPredRegInvLink.ksvm <- function(object, newdata, inv.link, ...) {
pred <- kernlab::predict(object, newdata = newdata, ...)[, 1L, drop = TRUE]
pred <- pred[!is.na(pred)]
mean(inv.link(pred))
}
getParPredRegInvLink.mars <- function(object, newdata, inv.link, ...) {
pred <- stats::predict(object, newdata = data.matrix(newdata),
...)[, 1L, drop = TRUE]
pred <- pred[!is.na(pred)]
mean(inv.link(pred))
}
getParPredRegInvLink.ranger <- function(object, newdata, inv.link, ...) {
pred <- stats::predict(object, data = newdata, ...)$predictions
pred <- pred[!is.na(pred)]
mean(inv.link(pred))
}
getParPredRegInvLink.xgb.Booster <- function(object, newdata, inv.link, ...) {
pred <- stats::predict(object, newdata = newdata, ...)
pred <- pred[!is.na(pred)]
mean(inv.link(pred))
}
|
plot.empCirc<-function(x,main=NULL, lcolor=c("blue","blue","red","red"), ltype=c(1,2), lwd=3, defhexa= list(hexa=TRUE, seg=TRUE,gr=5,r=4,nseg=7,x.cent=0,y.cent=0), ...)
{
if(is.null(main)){main<-paste( "angular locations for ",deparse(substitute(x)),sep="")}
hexa <- defhexa$hexa
seg <- defhexa$seg
gr<-defhexa$gr
r=defhexa$r
nseg=defhexa$nseg
x.cent <- defhexa$x.cent
y.cent <- defhexa$y.cent
H<-seq((0),((2*pi)), length.out=nseg)
plot.new()
plot.window(xlim=c(-gr,gr), ylim=c(-gr,gr),asp=1,main=main)
Hxx <- x.cent + r*sin( H )
Hyy <- y.cent + r*cos( H )
if(hexa==TRUE){
lines(Hxx,Hyy, col=lcolor[1],lty=ltype[1],lwd=lwd)
}
if (seg==TRUE){
for (i in 1:(nseg-1)){
lines(c(x.cent,Hxx[i]),c(y.cent,Hyy[i]), col=lcolor[1],lty=ltype[1],lwd=lwd)
}}
BP <- colnames(x$mat)
BPxx <- x.cent + (r+.4)*sin( H[1:6] )
BPyy <- y.cent + (r+.4)*cos( H[1:6] )
text(BPxx,BPyy, labels= BP, col=lcolor[2])
rwink<-(x$mat[1, ])
Exx <- x.cent + r*sin((rwink))
Eyy <- y.cent + r*cos((rwink))
for (i in 1:6){
lines(c(x.cent,Exx[i]),c(y.cent,Eyy[i]),col=lcolor[3],lty=ltype[2],lwd=lwd)
}
BE <- colnames(x$mat)
BExx <- x.cent + (r-.7)*sin( (rwink+.1) )
BEyy <- y.cent + (r-.7)*cos( (rwink+.1) )
text(BExx,BEyy, labels= BE, col=lcolor[4])
}
|
iplotLegend <- function(colramp = IDPcolorRamp,
ncol = NULL, cex.axis = par("cex.axis"),
border = FALSE, mar = c(0,0,0,3), las = 1, ...)
{
if(is.null(ncol)) {
border <- FALSE
mycol <- c(par("bg"),colramp(100))
}else {
ncol <- ceiling(ncol)
mycol <- c(par("bg"),colramp(ncol))
}
lev <- 0:length(mycol)
par(mar=mar, las=las, cex.axis=cex.axis, ...)
plot.new()
plot.window(xlim=c(0, 1), ylim=range(lev,na.rm=TRUE),
xaxs="i", yaxs="i")
if(border)
rect(0, lev[-length(lev)], 1, lev[-1], col=mycol)
else
rect(0, lev[-length(lev)], 1, lev[-1],
col=mycol, border=mycol)
box()
if(is.null(ncol)) {
axis(side=4, at=c(0.5,length(mycol)-0.5), labels=c("0","max"))
}else {
ap <- pretty(lev)
axis(side=4, at=ap+0.5, labels=paste(ap))
}
}
|
save_h2o_model <- function(object, path, overwrite = FALSE) {
is_acceptable_class <- c("workflow", "model_fit") %>%
purrr::map_lgl(.f = function(cl) inherits(object, cl)) %>%
any()
if (!is_acceptable_class) {
rlang::abort("'object' must be class 'workflow' or 'model_fit'.")
}
path_extension <- fs::path_ext(path)
if (path_extension != "") {
msg <- glue::glue("'path' should be a directory only. Found extension: {path_extension}. No file extensions are permitted.")
rlang::abort(msg)
}
if (!overwrite) {
if (fs::dir_exists(path)) {
msg <- glue::glue("A directory exists at path: {path}. Use 'overwrite = TRUE' to overwrite.")
rlang::abort(msg)
}
}
if (!fs::dir_exists(path)) {
fs::dir_create(path)
}
rds_path <- fs::path(path, "modeltime_model.rds")
saveRDS(object, file = rds_path)
if (inherits(object, "workflow")) {
h2o_model <- object$fit$fit$fit$models$model_1
} else {
h2o_model <- object$fit$models$model_1
}
fileout <- h2o::h2o.saveModel(
object = h2o_model,
path = path,
force = overwrite
)
file.rename(fileout, fs::path(path, 'h2o_model'))
msg <- glue::glue("\n\nModel saved at path: {path}")
message(msg)
}
load_h2o_model <- function(path) {
path_extension <- fs::path_ext(path)
if (path_extension != "") {
msg <- glue::glue("'path' should be a directory only. Found extension: {path_extension}. No file extensions are permitted.")
rlang::abort(msg)
}
rds_path <- fs::path(path, "modeltime_model.rds")
object <- readRDS(file = rds_path)
path <- fs::path(path, "h2o_model")
model <- h2o::h2o.loadModel(path)
if (inherits(object, "workflow")) {
object$fit$fit$fit$models$model_1 <- model
} else {
object$fit$models$model_1 <- model
}
return(object)
}
|
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