R/invGauss.R:
CRAN
invGauss: Threshold Regression that Fits the (Randomized Drift) Inverse Gaussian Distribution to Survival Data
if(.test)
cat("\nNote: negative values in density\n") #
if(no.censor){
if(.test)
cat("\nNote: negative values in density\n") #
if(no.censor){
), "response_10", "response_11"))
o.b <- data.frame(x = c('a', 'a', 'b', 'b'), y = c(1, 2, 1, 3), o.a)
rownames(o.b
), "response_10", "response_11"))
o.b <- data.frame(x = c('a', 'a', 'b', 'b'), y = c(1, 2, 1, 3), o.a)
rownames(o.b
(DF_A, DF_B, on=c("id_A == id_B"))
# compare <-
# dtjoin(DF_B, DF_A, on=c("id_B == id_A"), indicate = TRUE, i.main
with on.first", {
result <- dtjoin(DF_B, DF_A, on="v_B==v_A", on.first=TRUE)
expect_named(result, c("v_B","id_B
fjoin_left with do=FALSE, data.frames", {
expect_null(fjoin_left(DF_A, DF_B, on=c("id_A == id_B"), do=FALSE
#' @param df_a The first dataframe
#' @param df_b The second dataframe
#' @return A dataframe with all combinations
the GenomicRanges package.
Usage
full_range_comparison(df_a, df_b)
A dataframe
#' @param df_b A dataframe
#' @param indices The indices to match rows between \code{df_a} and \code{df_b}. Can
full_range_comparison <- function(df_a, df_b) {
cat("Intersection, differences and union of two dataframes of genomic
full_range_comparison <- function(df_a, df_b) {
cat("Intersection, differences and union of two dataframes of genomic
("dsShareServer::nextDS::smk")
test_that("nothing has been set",
expect_error(nextDS())
", {
fjoin_left(DF_A, DF_B, on="id_A==id_B", order="left") |>
expect_no_error()
)"
if (PRINT_TEST_NAME) cat("\nTest: ", desc, "\n")
test_that(desc, {
(DF_B, DF_A, on=c("id_B == id_A", "t_B < t_A"), nomatch = NULL)
compare <-
dplyr::inner_join(DF_A, DF_B, by=dplyr
("dsShareServer::nextDS::smk")
test_that("nothing has been set",
expect_error(nextDS())
DF_A <- DF_A[1:3,]
DF_B <- DF_B[1:2,]
desc <- "dtjoin_cross"
#' @param df_a The first dataframe
#' @param df_b The second dataframe
#' @return A dataframe with all combinations
the GenomicRanges package.
Usage
full_range_comparison(df_a, df_b)
= df_a, encoded = df_b, limit = 1000))
expect_false(idds.are.values.in.limit(server = df_a, encoded = df_c, limit = 1000
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