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### Running command:
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### /home/biocbuild/bbs-3.11-bioc/R/bin/R CMD check --install=check:geecc.install-out.txt --library=/home/biocbuild/bbs-3.11-bioc/R/library --no-vignettes --timings geecc_1.22.0.tar.gz
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* using log directory ‘/home/biocbuild/bbs-3.11-bioc/meat/geecc.Rcheck’
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘geecc/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘geecc’ version ‘1.22.0’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘geecc’ can be installed ... WARNING
Found the following significant warnings:
Warning: Package 'geecc' is deprecated and will be removed from Bioconductor
See ‘/home/biocbuild/bbs-3.11-bioc/meat/geecc.Rcheck/00install.out’ for details.
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files is not available
* checking files in ‘vignettes’ ... OK
* checking examples ... ERROR
Running examples in ‘geecc-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: geecc-package
> ### Title: Gene set enrichment for two or three categories
> ### Aliases: geecc-package geecc
> ### Keywords: package
>
> ### ** Examples
>
> ##
> ## a completely artificial example run
> ## through the routines of the package
> ##
> R <- 500
> #generate R random gene-ids
> ID <- sapply(1:R, function(r){paste( sample(LETTERS, 10), collapse="" ) } )
> ID <- unique(ID)
>
> #assign artificial differentially expressed genes randomly
> category1 <- list( deg.smallFC=sample(ID, 100, rep=FALSE),
+ deg.hughFC=sample(ID, 100, rep=FALSE) )
> #assign artificial GO terms of genes randomly
> category2 <- list( go1=sample(ID, 50, replace=FALSE),
+ go2=sample(ID, 166, replace=FALSE),
+ go3=sample(ID, 74, replace=FALSE),
+ go4=sample(ID, 68, replace=FALSE) )
> #assign artificial sequence length of genes randomly
> LEN <- setNames(sample(seq(100, 1000, 100), length(ID), replace=TRUE), ID)
> category3 <- split( ID, f=factor(LEN, levels=seq(100, 1000, 100)) )
> CatList <- list(deg=category1, go=category2, len=category3)
>
> ConCubFilter.obj <- new("concubfilter", names=names(CatList))
> ConCub.obj <- new("concub", categories=CatList)
Changed order of categories: deg,len,go
> ConCub.obj.2 <- runConCub( obj=ConCub.obj, filter=ConCubFilter.obj, nthreads=1 )
----------- FAILURE REPORT --------------
--- failure: length > 1 in coercion to logical ---
--- srcref ---
:
--- package (from environment) ---
geecc
--- call from context ---
runConCub(obj = ConCub.obj, filter = ConCubFilter.obj, nthreads = 1)
--- call from argument ---
(length(nms_categories) != length(filter@names)) || (intersect(nms_categories,
filter@names) != nms_categories)
--- R stacktrace ---
where 1: runConCub(obj = ConCub.obj, filter = ConCubFilter.obj, nthreads = 1)
--- value of length: 3 type: logical ---
[1] FALSE FALSE FALSE
--- function from context ---
function (obj, filter, nthreads = 2, subset = NULL, verbose = list(output.step = 0,
show.cat1 = FALSE, show.cat2 = FALSE, show.cat3 = FALSE))
{
NCATS <- length(obj@categories)
nms_categories <- names(obj@categories)
if ((length(nms_categories) != length(filter@names)) || (intersect(nms_categories,
filter@names) != nms_categories)) {
warning("Names from concubfilter-object (", paste0(nms_categories,
collapse = ","), ") and concub-object (", paste0(filter@names,
collapse = ","), ") do not match.")
return(obj)
}
N <- length(obj@population)
N_factor <- .getNumberOfFactorLevels(obj)
items_factor <- .getItemsInEachCategory(obj)
opt_factor <- .getOptOfCategory(obj)
rng_factor <- vector("list", NCATS)
sub_categories <- .getNamesOfEachCategory(obj)
if (!is.null(subset)) {
for (nm in names(subset)) {
sub_categories[[nm]] <- intersect(subset[[nm]], sub_categories[[nm]])
}
}
len_sub_categories <- sapply(sub_categories, length)
ttt <- .getTypeOf_transformTable(form = obj@null.model, nms = nms_categories)
do.strat <- FALSE
if (NCATS == 3) {
do.strat <- obj@options[[3]][["strat"]]
}
frm <- obj@null.model
if (do.strat == TRUE & ttt[[1]] == "mi") {
frm <- update(frm, as.formula(paste("~.-", names(obj@categories)[3])))
}
message(paste("Testing: counts ~ ", as.character(obj@null.model)[2],
" (", ttt[[1]], ")", sep = ""), sep = "")
if (any(sapply(opt_factor, function(x) {
return(x$grouping != "none")
}))) {
message(paste("Grouping: ", paste(names(opt_factor),
":", sapply(opt_factor, function(x) {
return(x$grouping)
}), sep = "", collapse = ", "), sep = ""), sep = "")
}
RNG <- setNames(vector("list", NCATS), nms_categories)
if (sum(sapply(opt_factor, function(x) {
x[["grouping"]] != "none"
})) > 1) {
warning("Grouping-option for multiple categories. Results might be hard to interprete.")
}
for (g in nms_categories) {
opt_grouping <- opt_factor[[g]][["grouping"]]
opt_width <- opt_factor[[g]][["width"]]
local_len <- len_sub_categories[g]
local_rng <- sub_categories[[g]]
RNG[[g]] <- setNames(vector("list", local_len), local_rng)
for (g2 in 1:local_len) {
term <- local_rng[g2]
term2cum <- term
if (opt_grouping == "none") {
term2cum <- term
}
if (opt_grouping == "cumf") {
if (g2 == local_len) {
next
}
term2cum <- local_rng[1:g2]
}
if (opt_grouping == "cumr") {
if (g2 == 1) {
next
}
term2cum <- local_rng[g2:local_len]
}
if (opt_grouping == "sw") {
l <- opt_width
start <- max(c(1, g2 - l))
stop <- min(c(g2 + l, local_len))
term2cum <- local_rng[start:stop]
}
RNG[[g]][[term]] <- term2cum
}
if (opt_grouping == "cumf") {
sub_categories[[g]] <- sub_categories[[g]][1:(local_len -
1)]
}
if (opt_grouping == "cumr") {
sub_categories[[g]] <- sub_categories[[g]][2:local_len]
}
}
Len_term3 <- c()
if (NCATS == 3 && verbose$show.cat3) {
L <- length(sub_categories[[3]])
Len_term3 <- setNames(vector("list", L), sub_categories[[3]])
for (l in 1:L) {
x <- ifelse(l == 1, sub_categories[[3]][L], sub_categories[[3]][l -
1])
Len_term3[[l]] <- paste(paste(rep("\b", times = nchar(x)),
collapse = ""), "\t", sub_categories[[3]][l],
collapse = "", sep = "")
}
}
my_separator <- .my_separator()
tmp0_nms1 <- c(t(outer(sub_categories[[1]], sub_categories[[2]],
paste, sep = my_separator)))
if (NCATS == 3) {
tmp0_nms1 <- c(t(outer(tmp0_nms1, sub_categories[[3]],
paste, sep = my_separator)))
}
tmp1 <- items_factor[[nms_categories[2]]]
tmp2 <- RNG[[nms_categories[2]]]
PreCalc__x_1_ <- setNames(vector("list", length(sub_categories[[nms_categories[2]]])),
names(sub_categories[[nms_categories[2]]]))
for (t in as.integer(1:length(sub_categories[[nms_categories[2]]]))) {
PreCalc__x_1_[[t]] <- unique(.special1(tmp1, tmp2, t))
}
PreCalc__x__1 <- PreCalc__x__2 <- list()
if (NCATS == 3) {
loc_nm3 <- nms_categories[3]
tmp1 <- items_factor[[loc_nm3]]
tmp2 <- RNG[[loc_nm3]]
PreCalc__x__1 <- setNames(vector("list", length(sub_categories[[loc_nm3]])),
names(sub_categories[[loc_nm3]]))
for (t in as.integer(1:length(sub_categories[[loc_nm3]]))) {
PreCalc__x__1[[t]] <- unique(.special1(tmp1, tmp2,
t))
}
}
ITER <- setNames(vector("list", length(tmp0_nms1)), tmp0_nms1)
loc_nm1 <- nms_categories[1]
for (g1 in 1:length(sub_categories[[loc_nm1]])) {
term1 <- sub_categories[[loc_nm1]][g1]
if (verbose$show.cat1) {
cat(term1, sep = "")
}
notterm1 <- paste("not_", term1, sep = "")
x1__ <- unique(unlist(items_factor[[loc_nm1]][RNG[[loc_nm1]][[term1]]],
use.names = FALSE))
x2__ <- setdiffPresort(obj@population, x1__)
RES_CAT2 <- list()
loc_nm2 <- nms_categories[2]
for (g2 in 1:length(sub_categories[[loc_nm2]])) {
term2 <- sub_categories[[loc_nm2]][g2]
if (verbose$show.cat2) {
cat("\r\t", term2, "\t", sep = "")
}
x_1_ <- PreCalc__x_1_[[g2]]
x_2_ <- setdiffPresort(obj@population, PreCalc__x_1_[[g2]])
x1__Ix_1_ <- intersect(x1__, x_1_)
x1__Ix_2_ <- intersectPresort(x_2_, x1__)
x2__Ix_1_ <- intersectPresort(x2__, x_1_)
x2__Ix_2_ <- setdiffPresort(obj@population, c(x1__Ix_1_,
x1__Ix_2_, x2__Ix_1_))
RES_CAT3 <- list()
if (NCATS == 2) {
res <- list()
subpop <- as.character((x1__Ix_1_))
len_subpop <- length(subpop)
CT <- array(c(len_subpop, vapply(list(x2__Ix_1_,
x1__Ix_2_, x2__Ix_2_), length, FUN.VALUE = 123)),
dim = c(2, 2), dimnames = list(factor1 = c(term1,
notterm1), factor2 = c(term2, paste("not_",
term2, sep = ""))))
names(dimnames(CT)) <- nms_categories
ExpectedValues <- hypergea::getExpectedValues(CT)
if (!((skip.zeroobs(filter) && len_subpop ==
0) || (skip.min.obs(filter) >= len_subpop) ||
all(skip.min.group(filter) - c(length(x1__),
length(x_1_)) > 0))) {
res <- .performTest_approx(frm, CT, CT, minExpectedValues = min(ExpectedValues),
approx = obj@approx, nthreads, test.direction(filter))
}
res <- c(res, list(subpop = subpop))
RES_CAT2[[term2]] <- res
}
if (NCATS == 3) {
RES_CAT3 <- list()
loc_nm3 <- nms_categories[3]
for (g3 in 1:length(sub_categories[[loc_nm3]])) {
res <- list()
term3 <- sub_categories[[loc_nm3]][g3]
if (verbose$show.cat3) {
message(Len_term3[[term3]])
}
x__1 <- PreCalc__x__1[[g3]]
x__2 <- setdiffPresort(obj@population, PreCalc__x__1[[g3]])
subpop <- as.character(intersect(x1__Ix_1_,
x__1))
len_subpop <- length(subpop)
CT <- array(NA, dim = c(2, 2, 2), dimnames = list(factor1 = c(term1,
notterm1), factor2 = c(term2, paste("not_",
term2, sep = "")), factor3 = c(term3, paste("not_",
term3, sep = ""))))
names(dimnames(CT)) <- nms_categories
res <- list(estimate = 1, p.value = 1, subpop = subpop)
if (skip.zeroobs(filter) && len_subpop == 0 ||
skip.min.obs(filter) >= len_subpop) {
RES_CAT3[[term3]] <- res
next
}
group.len <- sapply(list(x1__, x_1_, x__1),
length)
if (any(skip.min.group(filter) >= group.len)) {
RES_CAT3[[term3]] <- res
next
}
CT <- .getContingencyCube(CT, x1__Ix_1_, x2__Ix_2_,
x1__Ix_2_, x2__Ix_1_, x__1, x__2)
ExpectedValues <- getExpectedValues(CT)
if (ttt[[1]] %in% c("mi")) {
res <- .performTest_approx(frm, CT, CT, minExpectedValues = min(ExpectedValues),
approx = obj@approx, nthreads = nthreads,
test.direction(filter))
}
else {
if (ttt[[1]] %in% c("sp.1", "sp.2", "sp.3")) {
CT_t <- .transformTable(CT, x = ttt)
ExpectedValues_t <- getExpectedValues(CT_t)
res <- .performTest_approx(frm, CT, CT_t,
minExpectedValues = min(ExpectedValues_t),
approx = obj@approx, nthreads = nthreads,
test.direction(filter))
}
else {
res <- .performTest(frm, CT)
}
}
RES_CAT3[[term3]] <- c(res, list(subpop = subpop))
}
tmp_nms3 <- names(RES_CAT3)
RES_CAT2[paste(term2, tmp_nms3, sep = my_separator)] <- RES_CAT3[tmp_nms3]
}
if (verbose[["output.step"]] > 0 && ((g2%%verbose[["output.step"]] ==
0) || (g2 == length(rng_factor[[2]])))) {
if (verbose$show.cat2) {
cat("\n\tpassed: second category (", nms_categories[2],
"), variable ", term2, " (", g2, ")", sep = "")
}
if (verbose$show.cat2) {
cat("\n")
}
}
}
tmp_nms2 <- names(RES_CAT2)
ITER[paste(term1, tmp_nms2, sep = my_separator)] <- RES_CAT2[tmp_nms2]
if (verbose$show.cat1) {
cat("\n")
}
if (g1%%10 == 0) {
gc()
}
}
cat("\n")
obj@test.result <- ITER
return(obj)
}
<bytecode: 0x560aac092cb8>
<environment: namespace:geecc>
--- function search by body ---
Function runConCub in namespace geecc has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: length > 1 in coercion to logical
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 1 ERROR, 1 WARNING, 1 NOTE
See
‘/home/biocbuild/bbs-3.11-bioc/meat/geecc.Rcheck/00check.log’
for details.