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###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:diffcyt.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings diffcyt_1.23.1.tar.gz
###
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* using log directory ‘/home/biocbuild/bbs-3.19-bioc/meat/diffcyt.Rcheck’
* using R Under development (unstable) (2024-03-18 r86148)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
* running under: Ubuntu 22.04.4 LTS
* using session charset: UTF-8
* checking for file ‘diffcyt/DESCRIPTION’ ... OK
* this is package ‘diffcyt’ version ‘1.23.1’
* 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 ‘diffcyt’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code 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 loading without being on the library search path ... 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 ... NOTE
calcCounts: no visible binding for global variable ‘cluster_id’
calcCounts: no visible binding for global variable ‘sample_id’
calcMedians: no visible binding for global variable ‘cluster_id’
calcMedians: no visible binding for global variable ‘sample_id’
calcMedians: no visible binding for global variable ‘value’
calcMediansByClusterMarker: no visible binding for global variable
  ‘cluster_id’
calcMediansByClusterMarker: no visible binding for global variable
  ‘marker_id’
calcMediansByClusterMarker: no visible binding for global variable
  ‘value’
calcMediansBySampleMarker: no visible binding for global variable
  ‘sample_id’
calcMediansBySampleMarker: no visible binding for global variable
  ‘marker_id’
calcMediansBySampleMarker: no visible binding for global variable
  ‘value’
Undefined global functions or variables:
  cluster_id marker_id sample_id value
* 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 files in ‘vignettes’ ... OK
* checking examples ... ERROR
Running examples in ‘diffcyt-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: testDA_GLMM
> ### Title: Test for differential abundance: method 'diffcyt-DA-GLMM'
> ### Aliases: testDA_GLMM
> 
> ### ** Examples
> 
> # For a complete workflow example demonstrating each step in the 'diffcyt' pipeline, 
> # see the package vignette.
> 
> # Function to create random data (one sample)
> d_random <- function(n = 20000, mean = 0, sd = 1, ncol = 20, cofactor = 5) {
+   d <- sinh(matrix(rnorm(n, mean, sd), ncol = ncol)) * cofactor
+   colnames(d) <- paste0("marker", sprintf("%02d", 1:ncol))
+   d
+ }
> 
> # Create random data (without differential signal)
> set.seed(123)
> d_input <- list(
+   sample1 = d_random(), 
+   sample2 = d_random(), 
+   sample3 = d_random(), 
+   sample4 = d_random()
+ )
> 
> # Add differential abundance (DA) signal
> ix_DA <- 801:900
> ix_cols_type <- 1:10
> d_input[[3]][ix_DA, ix_cols_type] <- d_random(n = 1000, mean = 2, ncol = 10)
> d_input[[4]][ix_DA, ix_cols_type] <- d_random(n = 1000, mean = 2, ncol = 10)
> 
> experiment_info <- data.frame(
+   sample_id = factor(paste0("sample", 1:4)), 
+   group_id = factor(c("group1", "group1", "group2", "group2")), 
+   stringsAsFactors = FALSE
+ )
> 
> marker_info <- data.frame(
+   channel_name = paste0("channel", sprintf("%03d", 1:20)), 
+   marker_name = paste0("marker", sprintf("%02d", 1:20)), 
+   marker_class = factor(c(rep("type", 10), rep("state", 10)), 
+                         levels = c("type", "state", "none")), 
+   stringsAsFactors = FALSE
+ )
> 
> # Prepare data
> d_se <- prepareData(d_input, experiment_info, marker_info)
> 
> # Transform data
> d_se <- transformData(d_se)
> 
> # Generate clusters
> d_se <- generateClusters(d_se)
FlowSOM clustering completed in 0.4 seconds
> 
> # Calculate counts
> d_counts <- calcCounts(d_se)
> 
> # Create model formula
> formula <- createFormula(experiment_info, cols_fixed = "group_id", cols_random = "sample_id")
> 
> # Create contrast matrix
> contrast <- createContrast(c(0, 1))
> 
> # Test for differential abundance (DA) of clusters
> res_DA <- testDA_GLMM(d_counts, formula, contrast)

 *** caught segfault ***
address (nil), cause 'unknown'

Traceback:
 1: .Call(merPredDCreate, as(X, "matrix"), Lambdat, LamtUt, Lind,     RZX, Ut, Utr, V, VtV, Vtr, Xwts, Zt, beta0, delb, delu, theta,     u0)
 2: initializePtr()
 3: .Object$initialize(...)
 4: initialize(value, ...)
 5: initialize(value, ...)
 6: methods::new(def, ...)
 7: (new("refMethodDef", .Data = function (...) {    methods::new(def, ...)}, mayCall = c("methods", "new"), name = "new", refClassName = "refGeneratorSlot",     superClassMethod = ""))(Zt = new("dgCMatrix", i = 0:3, p = 0:4,     Dim = c(4L, 4L), Dimnames = list(c("sample1", "sample2",     "sample3", "sample4"), c("sample1", "sample2", "sample3",     "sample4")), x = c(1, 1, 1, 1), factors = list()), theta = 1,     Lambdat = new("dgCMatrix", i = 0:3, p = 0:4, Dim = c(4L,     4L), Dimnames = list(NULL, NULL), x = c(1, 1, 1, 1), factors = list()),     Lind = c(1L, 1L, 1L, 1L), n = 4L, X = c(1, 1, 1, 1, 0, 0,     1, 1))
 8: do.call(merPredD$new, c(reTrms[c("Zt", "theta", "Lambdat", "Lind")],     n = nrow(X), list(X = X)))
 9: (function (fr, X, reTrms, family, nAGQ = 1L, verbose = 0L, maxit = 100L,     control = glmerControl(), ...) {    stopifnot(length(nAGQ <- as.integer(nAGQ)) == 1L, 0L <= nAGQ,         nAGQ <= 25L)    verbose <- as.integer(verbose)    maxit <- as.integer(maxit)    rho <- list2env(list(verbose = verbose, maxit = maxit, tolPwrss = control$tolPwrss,         compDev = control$compDev), parent = parent.frame())    rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt", "theta",         "Lambdat", "Lind")], n = nrow(X), list(X = X)))    rho$resp <- if (missing(fr))         mkRespMod(family = family, ...)    else mkRespMod(fr, family = family)    nAGQinit <- if (control$nAGQ0initStep)         0L    else 1L    if (length(y <- rho$resp$y) > 0) {        checkResponse(y, control$checkControl)        rho$verbose <- as.integer(verbose)        .Call(glmerLaplace, rho$pp$ptr(), rho$resp$ptr(), nAGQinit,             control$tolPwrss, maxit, verbose)        rho$lp0 <- rho$pp$linPred(1)        rho$pwrssUpdate <- glmerPwrssUpdate    }    rho$lower <- reTrms$lower    mkdevfun(rho, nAGQinit, maxit = maxit, verbose = verbose,         control = control)})(fr = list(y = c(0.016, 0.019, 0.01, 0.011), group_id = c(1L, 1L, 2L, 2L), sample_id = 1:4, `(weights)` = c(1000, 1000, 1000, 1000)), X = c(1, 1, 1, 1, 0, 0, 1, 1), reTrms = list(Zt = new("dgCMatrix",     i = 0:3, p = 0:4, Dim = c(4L, 4L), Dimnames = list(c("sample1",     "sample2", "sample3", "sample4"), c("sample1", "sample2",     "sample3", "sample4")), x = c(1, 1, 1, 1), factors = list()),     theta = 1, Lind = c(1L, 1L, 1L, 1L), Gp = c(0L, 4L), lower = 0,     Lambdat = new("dgCMatrix", i = 0:3, p = 0:4, Dim = c(4L,     4L), Dimnames = list(NULL, NULL), x = c(1, 1, 1, 1), factors = list()),     flist = list(sample_id = 1:4), cnms = list(sample_id = "(Intercept)"),     Ztlist = list(`1 | sample_id` = new("dgCMatrix", i = 0:3,         p = 0:4, Dim = c(4L, 4L), Dimnames = list(c("sample1",         "sample2", "sample3", "sample4"), c("sample1", "sample2",         "sample3", "sample4")), x = c(1, 1, 1, 1), factors = list())),     nl = c(sample_id = 4L)), family = list(family = "binomial",     link = "logit", linkfun = function (mu)     .Call(C_logit_link, mu), linkinv = function (eta)     .Call(C_logit_linkinv, eta), variance = function (mu)     mu * (1 - mu), dev.resids = function (y, mu, wt)     .Call(C_binomial_dev_resids, y, mu, wt), aic = function (y,         n, mu, wt, dev)     {        m <- if (any(n > 1))             n        else wt        -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m *             y), round(m), mu, log = TRUE))    }, mu.eta = function (eta)     .Call(C_logit_mu_eta, eta), initialize = {        if (NCOL(y) == 1) {            if (is.factor(y))                 y <- y != levels(y)[1L]            n <- rep.int(1, nobs)            y[weights == 0] <- 0            if (any(y < 0 | y > 1))                 stop("y values must be 0 <= y <= 1")            mustart <- (weights * y + 0.5)/(weights + 1)            m <- weights * y            if ("binomial" == "binomial" && any(abs(m - round(m)) >                 0.001))                 warning(gettextf("non-integer #successes in a %s glm!",                   "binomial"), domain = NA)        }        else if (NCOL(y) == 2) {            if ("binomial" == "binomial" && any(abs(y - round(y)) >                 0.001))                 warning(gettextf("non-integer counts in a %s glm!",                   "binomial"), domain = NA)            n <- (y1 <- y[, 1L]) + y[, 2L]            y <- y1/n            if (any(n0 <- n == 0))                 y[n0] <- 0            weights <- weights * n            mustart <- (n * y + 0.5)/(n + 1)        }        else stop(gettextf("for the '%s' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures",             "binomial"), domain = NA)    }, validmu = function (mu)     all(is.finite(mu)) && all(mu > 0 & mu < 1), valideta = function (eta)     TRUE, simulate = function (object, nsim)     {        ftd <- fitted(object)        n <- length(ftd)        ntot <- n * nsim        wts <- object$prior.weights        if (any(wts%%1 != 0))             stop("cannot simulate from non-integer prior.weights")        if (!is.null(m <- object$model)) {            y <- model.response(m)            if (is.factor(y)) {                yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd),                   labels = levels(y))                split(yy, rep(seq_len(nsim), each = n))            }            else if (is.matrix(y) && ncol(y) == 2) {                yy <- vector("list", nsim)                for (i in seq_len(nsim)) {                  Y <- rbinom(n, size = wts, prob = ftd)                  YY <- cbind(Y, wts - Y)                  colnames(YY) <- colnames(y)                  yy[[i]] <- YY                }                yy            }            else rbinom(ntot, size = wts, prob = ftd)/wts        }        else rbinom(ntot, size = wts, prob = ftd)/wts    }, dispersion = 1), wmsgs = character(0), verbose = 0L, control = list(    optimizer = c("bobyqa", "Nelder_Mead"), restart_edge = FALSE,     boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE,     checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop",         check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop",         check.nobs.vs.nRE = "stop", check.rankX = "message+drop.cols",         check.scaleX = "warning", check.formula.LHS = "stop",         check.response.not.const = "stop"), checkConv = list(        check.conv.grad = list(action = "warning", tol = 0.002,             relTol = NULL), check.conv.singular = list(action = "message",             tol = 1e-04), check.conv.hess = list(action = "warning",             tol = 1e-06)), optCtrl = list(), tolPwrss = 1e-07,     compDev = TRUE, nAGQ0initStep = TRUE), nAGQ = 0L)
10: do.call(mkGlmerDevfun, c(glmod, list(verbose = verbose, control = control,     nAGQ = nAGQinit)))
11: glmer(formula$formula, data = data_i, family = "binomial", weights = n_cells_smp)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch({    y <- counts[i, ]/n_cells_smp    data_i <- cbind(y, n_cells_smp, formula$data)    fit <- glmer(formula$formula, data = data_i, family = "binomial",         weights = n_cells_smp)    test <- glht(fit, contrast)    summary(test)$test$pvalues}, error = function(e) NA)
16: testDA_GLMM(d_counts, formula, contrast)
An irrecoverable exception occurred. R is aborting now ...
Segmentation fault (core dumped)
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 1 ERROR, 2 NOTEs
See
  ‘/home/biocbuild/bbs-3.19-bioc/meat/diffcyt.Rcheck/00check.log’
for details.

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###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD INSTALL diffcyt
###
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* installing to library ‘/home/biocbuild/bbs-3.19-bioc/R/site-library’
* installing *source* package ‘diffcyt’ ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (diffcyt)

R Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(diffcyt)
> 
> test_check("diffcyt")
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 8 ]
> 
> proc.time()
   user  system elapsed 
  9.789   0.780  10.557