Running ‘testthat.R’ [1s/1s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(GMMAT)
  > Sys.setenv(MKL_NUM_THREADS = 1)
  > 
  > test_check("GMMAT")
  
   *** caught segfault ***
  address 0x110, cause 'invalid permissions'
  
   *** caught segfault ***
  address 0x110, cause 'invalid permissions'
  
  Traceback:
   1: eval(c.expr, envir = args, enclos = envir)
   2: eval(c.expr, envir = args, enclos = envir)
   3: doTryCatch(return(expr), name, parentenv, handler)
   4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5: tryCatchList(expr, classes, parentenv, handlers)
   6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
   7: FUN(X[[i]], ...)
   8: lapply(X = S, FUN = FUN, ...)
   9: doTryCatch(return(expr), name, parentenv, handler)
  10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  11: tryCatchList(expr, classes, parentenv, handlers)
  12: tryCatch(expr, error = function(e) {    call <- conditionCall(e)    if (!is.null(call)) {        if (identical(call[[1L]], quote(doTryCatch)))             call <- sys.call(-4L)        dcall <- deparse(call, nlines = 1L)        prefix <- paste("Error in", dcall, ": ")        LONG <- 75L        sm <- strsplit(conditionMessage(e), "\n")[[1L]]        w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")        if (is.na(w))             w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],                 type = "b")        if (w > LONG)             prefix <- paste0(prefix, "\n  ")    }    else prefix <- "Error : "    msg <- paste0(prefix, conditionMessage(e), "\n")    .Internal(seterrmessage(msg[1L]))    if (!silent && isTRUE(getOption("show.error.messages"))) {        cat(msg, file = outFile)        .Internal(printDeferredWarnings())    }    invisible(structure(msg, class = "try-error", condition = e))})
  13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
  14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
  15: FUN(X[[i]], ...)
  16: lapply(seq_len(cores), inner.do)
  17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed,     mc.silent = silent, mc.cores = cores)
  18: e$fun(obj, substitute(ex), parent.frame(), e$data)
  19: foreach(i = 1:ncores) %dopar% {    if (!is.null(obj$P)) {        if (bgenInfo$LayoutFlag == 2) {            .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,                 infile, paste0(outfile, "_tmp.", i), center2,                 MAF.range[1], MAF.range[2], miss.cutoff, miss.method,                 nperbatch, select, threadInfo$begin[i], threadInfo$end[i],                 threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,                 1)        }        else {            .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,                 infile, paste0(outfile, "_tmp.", i), center2,                 MAF.range[1], MAF.range[2], miss.cutoff, miss.method,                 nperbatch, select, threadInfo$begin[i], threadInfo$end[i],                 threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,                 1)        }    }    else {        if (bgenInfo$LayoutFlag == 2) {            .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i,                 obj$Sigma_iX, obj$cov, infile, paste0(outfile,                   "_tmp.", i), center2, MAF.range[1], MAF.range[2],                 miss.cutoff, miss.method, nperbatch, select,                 threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],                 bgenInfo$N, bgenInfo$CompressionFlag, 1)        }        else {            .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,                 obj$Sigma_iX, obj$cov, infile, paste0(outfile,                   "_tmp.", i), center2, MAF.range[1], MAF.range[2],                 miss.cutoff, miss.method, nperbatch, select,                 threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],                 bgenInfo$N, bgenInfo$CompressionFlag, 1)        }    }}
  20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,     outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
  21: eval(code, test_env)
  22: eval(code, test_env)
  23: withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt)
  24: doTryCatch(return(expr), name, parentenv, handler)
  25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  26: tryCatchList(expr, classes, parentenv, handlers)
  27: tryCatch(withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
  28: doWithOneRestart(return(expr), restart)
  29: withOneRestart(expr, restarts[[1L]])
  30: withRestarts(tryCatch(withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal),     end_test = function() {    })
  31: test_code(code, parent.frame())
  32: test_that("cross-sectional id le 400 binomial", {    plinkfiles <- strsplit(system.file("extdata", "geno.bed",         package = "GMMAT"), ".bed", fixed = TRUE)[[1]]    bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")    samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")    gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")    txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")    txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")    txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")    data(example)    suppressWarnings(RNGversion("3.5.0"))    set.seed(123)    pheno <- rbind(example$pheno, example$pheno[1:100, ])    pheno$id <- 1:500    pheno$disease[sample(1:500, 20)] <- NA    pheno$age[sample(1:500, 20)] <- NA    pheno$sex[sample(1:500, 20)] <- NA    pheno <- pheno[sample(1:500, 450), ]    pheno <- pheno[pheno$id <= 400, ]    kins <- example$GRM    obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj1$id_include))    select[is.na(select)] <- 0    obj1.outfile.bed.noselect.1 <- tempfile()    glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)    obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1,         header = TRUE, as.is = TRUE)    obj1.outfile.bed.noselect.1.tmp <- tempfile()    expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp,         ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")    unlink(obj1.outfile.bed.noselect.1.tmp)    obj1.outfile.bed.select.1 <- tempfile()    glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)    obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)    obj1.outfile.bgen.noselect.1 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj1.outfile.bgen.noselect.1)    obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1,         header = TRUE, as.is = TRUE)    obj1.outfile.bgen.noselect.1.tmp <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)    obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)    unlink(obj1.outfile.bgen.noselect.1.tmp)    obj1.outfile.bgen.select.1 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj1.outfile.bgen.select.1)    obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)    expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1",         "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[,         c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE",             "VAR", "PVAL")])    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj1.outfile.gds.noselect.1 <- tempfile()        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)        obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1,             header = TRUE, as.is = TRUE)        obj1.outfile.gds.noselect.1.tmp <- tempfile()        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp,             ncores = 2)        obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)        unlink(obj1.outfile.gds.noselect.1.tmp)        obj1.outfile.gds.select.1 <- tempfile()        glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)        obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)        expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))        expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942,             0.986534857)))        unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))    }    obj1.outfile.txt.select.1 <- tempfile()    glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)    obj1.outfile.txt.select.1.tmp <- tempfile()    expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")    unlink(obj1.outfile.txt.select.1.tmp)    obj1.outfile.txt1.select.1 <- tempfile()    glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt.select.1, obj1.txt1.select.1)    obj1.outfile.txt2.select.1 <- tempfile()    glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))
  Traceback:
   1:     obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1,         header = TRUE, as.is = TRUE)eval(c.expr, envir = args, enclos = envir)
   2: eval(c.expr, envir = args, enclos = envir)    expect_equal(obj1.txt.select.1, obj1.txt2.select.1)    unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1,         obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1,         obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1,         obj1.outfile.txt2.select.1))
   3: doTryCatch(return(expr), name, parentenv, handler)
   4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5:     skip_on_cran()    obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj2$id_include))tryCatchList(expr, classes, parentenv, handlers)
   6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
      select[is.na(select)] <- 0    obj2.outfile.bed.noselect.1 <- tempfile()    glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1)    obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1,         header = TRUE, as.is = TRUE)    obj2.outfile.bed.select.1 <- tempfile()    glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)    obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) 7: FUN(X[[i]], ...)
   8: lapply(X = S, FUN = FUN, ...)    obj2.outfile.bgen.noselect.1 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj2.outfile.bgen.noselect.1)    obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1,         header = TRUE, as.is = TRUE)
   9: doTryCatch(return(expr), name, parentenv, handler)
  10:     obj2.outfile.bgen.select.1 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj2.outfile.bgen.select.1)    obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)    expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1",         "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[,         c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE",             "VAR", "PVAL")])tryCatchOne(expr, names, parentenv, handlers[[1L]])
  11: tryCatchList(expr, classes, parentenv, handlers)
      if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj2.outfile.gds.noselect.1 <- tempfile()        glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1)        obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, 12: tryCatch(expr, error = function(e) {    call <- conditionCall(e)    if (!is.null(call)) {        if (identical(call[[1L]], quote(doTryCatch)))             header = TRUE, as.is = TRUE)        obj2.outfile.gds.select.1 <- tempfile()        glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1)        obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.noselect.1, obj2.gds.select.1)        expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL))            call <- sys.call(-4L)        dcall <- deparse(call, nlines = 1L)        prefix <- paste("Error in", dcall, ": ")        LONG <- 75L        sm <- strsplit(conditionMessage(e), "\n")[[1L]]        w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")        expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918,             0.996996766)))    }    obj2.outfile.txt.select.1 <- tempfile()    glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1,         if (is.na(w))             w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],                 type = "b")        if (w > LONG)             prefix <- paste0(prefix, "\n  ")    }        header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL)    obj2.outfile.txt1.select.1 <- tempfile()    glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt.select.1, obj2.txt1.select.1)    obj2.outfile.txt2.select.1 <- tempfile()    glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,     else prefix <- "Error : "    msg <- paste0(prefix, conditionMessage(e), "\n")    .Internal(seterrmessage(msg[1L]))    if (!silent && isTRUE(getOption("show.error.messages"))) {        cat(msg, file = outFile)        .Internal(printDeferredWarnings())    }    invisible(structure(msg, class = "try-error", condition = e))})
          select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt.select.1, obj2.txt2.select.1)13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
  14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))    idx <- sample(nrow(pheno))    pheno <- pheno[idx, ]    obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")
  15: FUN(X[[i]], ...)
  16:     select <- match(1:400, unique(obj1$id_include))    select[is.na(select)] <- 0    obj1.outfile.bed.noselect.2 <- tempfile()    glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2)    obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, lapply(seq_len(cores), inner.do)
  17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed,     mc.silent = silent, mc.cores = cores)        header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2)    obj1.outfile.bed.select.2 <- tempfile()    glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2)    obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, 
  18: e$fun(obj, substitute(ex), parent.frame(), e$data)
  19:         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.select.1, obj1.bed.select.2)    obj1.outfile.bgen.noselect.2 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj1.outfile.bgen.noselect.2)foreach(i = 1:ncores) %dopar% {    if (!is.null(obj$P)) {        if (bgenInfo$LayoutFlag == 2) {            .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,                 infile, paste0(outfile, "_tmp.", i), center2,                 MAF.range[1], MAF.range[2], miss.cutoff, miss.method,                 nperbatch, select, threadInfo$begin[i], threadInfo$end[i],                 threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,                 1)        }        else {            .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,                 infile, paste0(outfile, "_tmp.", i), center2,     obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2)    obj1.outfile.bgen.select.2 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj1.outfile.bgen.select.2)    obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.select.1, obj1.bgen.select.2)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj1.outfile.gds.noselect.2 <- tempfile()                MAF.range[1], MAF.range[2], miss.cutoff, miss.method,                 nperbatch, select, threadInfo$begin[i], threadInfo$end[i],                 threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,                 1)        }    }        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2)        obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2)        obj1.outfile.gds.select.2 <- tempfile()    else {        if (bgenInfo$LayoutFlag == 2) {            .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i,                 obj$Sigma_iX, obj$cov, infile, paste0(outfile,                   "_tmp.", i), center2, MAF.range[1], MAF.range[2],         glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2)        obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.select.1, obj1.gds.select.2)    }                miss.cutoff, miss.method, nperbatch, select,                 threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],                 bgenInfo$N, bgenInfo$CompressionFlag, 1)        }        else {    obj1.outfile.txt.select.2 <- tempfile()    glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))            .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,                 obj$Sigma_iX, obj$cov, infile, paste0(outfile,                   "_tmp.", i), center2, MAF.range[1], MAF.range[2],                 miss.cutoff, miss.method, nperbatch, select,                 threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],     obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt.select.1, obj1.txt.select.2)    obj1.outfile.txt1.select.2 <- tempfile()    glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2,                 bgenInfo$N, bgenInfo$CompressionFlag, 1)        }    }}
          infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2,         header = TRUE, as.is = TRUE)20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,     outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
  21:     expect_equal(obj1.txt1.select.1, obj1.txt1.select.2)    obj1.outfile.txt2.select.2 <- tempfile()    glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env)
  22: eval(code, test_env)
  23: withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }            "Allele2"))    obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt2.select.1, obj1.txt2.select.2)    obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj2$id_include))    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    select[is.na(select)] <- 0    obj2.outfile.bed.noselect.2 <- tempfile()    glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2)    obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2,         header = TRUE, as.is = TRUE)    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt)
  24:     expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2)    obj2.outfile.bed.select.2 <- tempfile()    glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2)    obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2,         header = TRUE, as.is = TRUE)doTryCatch(return(expr), name, parentenv, handler)
  25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
      expect_equal(obj2.bed.select.1, obj2.bed.select.2)    obj2.outfile.bgen.noselect.2 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj2.outfile.bgen.noselect.2)    obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, 26: tryCatchList(expr, classes, parentenv, handlers)
  27: tryCatch(withCallingHandlers({        header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2)    obj2.outfile.bgen.select.2 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj2.outfile.bgen.select.2)    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.select.1, obj2.bgen.select.2)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        obj2.outfile.gds.noselect.2 <- tempfile()        glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2)        obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2)        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")        obj2.outfile.gds.select.2 <- tempfile()        glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2)        obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.select.1, obj2.gds.select.2)}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
  28: doWithOneRestart(return(expr), restart)    }    obj2.outfile.txt.select.2 <- tempfile()    glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2,         header = TRUE, as.is = TRUE)
  29: withOneRestart(expr, restarts[[1L]])
  30: withRestarts(tryCatch(withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {    expect_equal(obj2.txt.select.1, obj2.txt.select.2)    obj2.outfile.txt1.select.2 <- tempfile()    glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",         skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))            "Allele2"))    obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt1.select.1, obj2.txt1.select.2)    obj2.outfile.txt2.select.2 <- tempfile()    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal),     end_test = function() {    })
  31: test_code(code, parent.frame())
  32:         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)    idx <- sample(nrow(kins))    kins <- kins[idx, idx]    obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, test_that("cross-sectional id le 400 binomial", {    plinkfiles <- strsplit(system.file("extdata", "geno.bed",         package = "GMMAT"), ".bed", fixed = TRUE)[[1]]    bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")    samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")        id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj1$id_include))    select[is.na(select)] <- 0    obj1.outfile.bed.noselect.3 <- tempfile()    gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")    txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")    txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")    txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")    data(example)    glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3)    obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3)    obj1.outfile.bed.select.3 <- tempfile()    suppressWarnings(RNGversion("3.5.0"))    set.seed(123)    pheno <- rbind(example$pheno, example$pheno[1:100, ])    pheno$id <- 1:500    pheno$disease[sample(1:500, 20)] <- NA    glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3)    obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.select.1, obj1.bed.select.3)    obj1.outfile.bgen.noselect.3 <- tempfile()    pheno$age[sample(1:500, 20)] <- NA    pheno$sex[sample(1:500, 20)] <- NA    pheno <- pheno[sample(1:500, 450), ]    pheno <- pheno[pheno$id <= 400, ]    kins <- example$GRM    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj1.outfile.bgen.noselect.3)    obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)    obj1.outfile.bgen.select.3 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj1.outfile.bgen.select.3)    obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {    obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj1$id_include))    select[is.na(select)] <- 0    obj1.outfile.bed.noselect.1 <- tempfile()    glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)    obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1,         header = TRUE, as.is = TRUE)    obj1.outfile.bed.noselect.1.tmp <- tempfile()    expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp,         ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")    unlink(obj1.outfile.bed.noselect.1.tmp)    obj1.outfile.bed.select.1 <- tempfile()    glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)        obj1.outfile.gds.noselect.3 <- tempfile()        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)        obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3,             header = TRUE, as.is = TRUE)    obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)    obj1.outfile.bgen.noselect.1 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)        obj1.outfile.gds.select.3 <- tempfile()        glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)        obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.select.1, obj1.gds.select.3)    }    obj1.outfile.txt.select.3 <- tempfile()    glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         outfile = obj1.outfile.bgen.noselect.1)    obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1,         header = TRUE, as.is = TRUE)    obj1.outfile.bgen.noselect.1.tmp <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt.select.1, obj1.txt.select.3)    obj1.outfile.txt1.select.3 <- tempfile()    glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",         outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)    obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)    unlink(obj1.outfile.bgen.noselect.1.tmp)            "Allele2"))    obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)    obj1.outfile.txt2.select.3 <- tempfile()    glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)    obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj2$id_include))    select[is.na(select)] <- 0    obj2.outfile.bed.noselect.3 <- tempfile()    glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)    obj1.outfile.bgen.select.1 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj1.outfile.bgen.select.1)    obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)    expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1",         "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[,         c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE",             "VAR", "PVAL")])    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {    obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)    obj2.outfile.bed.select.3 <- tempfile()    glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3)    obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.select.1, obj2.bed.select.3)        obj1.outfile.gds.noselect.1 <- tempfile()        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)        obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1,             header = TRUE, as.is = TRUE)        obj1.outfile.gds.noselect.1.tmp <- tempfile()        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp,             ncores = 2)        obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp,     obj2.outfile.bgen.noselect.3 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj2.outfile.bgen.noselect.3)    obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)    obj2.outfile.bgen.select.3 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)        unlink(obj1.outfile.gds.noselect.1.tmp)        obj1.outfile.gds.select.1 <- tempfile()        glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)        obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)        select = select, outfile = obj2.outfile.bgen.select.3)    obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj2.outfile.gds.noselect.3 <- tempfile()        glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)        expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))        expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942,             0.986534857)))        unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))    }    obj1.outfile.txt.select.1 <- tempfile()        obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)        obj2.outfile.gds.select.3 <- tempfile()        glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)    glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)    obj1.outfile.txt.select.1.tmp <- tempfile()    expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp,         obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.select.1, obj2.gds.select.3)    }    obj2.outfile.txt.select.3 <- tempfile()    glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")    unlink(obj1.outfile.txt.select.1.tmp)    obj1.outfile.txt1.select.1 <- tempfile()    glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt.select.1, obj2.txt.select.3)    obj2.outfile.txt1.select.3 <- tempfile()    glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)            "Allele2"))    obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt.select.1, obj1.txt1.select.1)    obj1.outfile.txt2.select.1 <- tempfile()    obj2.outfile.txt2.select.3 <- tempfile()    glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt.select.1, obj1.txt2.select.1)    obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)    unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,         obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1,     unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1,         obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1,         obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1,         obj1.outfile.txt2.select.1))    skip_on_cran()        obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1,         obj2.outfile.txt2.select.1))    unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2,         obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2,         obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2,     obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj2$id_include))    select[is.na(select)] <- 0    obj2.outfile.bed.noselect.1 <- tempfile()    glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1)    obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1,         obj1.outfile.txt2.select.2))    unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,         obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2,         obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2,         obj2.outfile.txt2.select.2))    unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3,         obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3,         obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3,         obj1.outfile.txt2.select.3))    unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3,         obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3,         obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3,         header = TRUE, as.is = TRUE)    obj2.outfile.bed.select.1 <- tempfile()    glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)    obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1,         header = TRUE, as.is = TRUE)        obj2.outfile.txt2.select.3))    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE))         unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1,             obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2,     expect_equal(obj2.bed.noselect.1, obj2.bed.select.1)    obj2.outfile.bgen.noselect.1 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj2.outfile.bgen.noselect.1)    obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1,             obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2,             obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3,             obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
          header = TRUE, as.is = TRUE)    obj2.outfile.bgen.select.1 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj2.outfile.bgen.select.1)    obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, 33: eval(code, test_env)
  34: eval(code, test_env)        header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)    expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1",         "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[,         c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 
  35: withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations            "VAR", "PVAL")])    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj2.outfile.gds.noselect.1 <- tempfile()        glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1)    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()        obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1,             header = TRUE, as.is = TRUE)        obj2.outfile.gds.select.1 <- tempfile()        glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1)        obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1,     }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }            header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.noselect.1, obj2.gds.select.1)        expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL))        expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918,             0.996996766)))}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt)
  36: doTryCatch(return(expr), name, parentenv, handler)
  37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  38:     }    obj2.outfile.txt.select.1 <- tempfile()    glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1,         header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers)
  39: tryCatch(withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL)    obj2.outfile.txt1.select.1 <- tempfile()    glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1,         header = TRUE, as.is = TRUE)    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    expect_equal(obj2.txt.select.1, obj2.txt1.select.1)    obj2.outfile.txt2.select.1 <- tempfile()    glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1,     if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
  40:         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt.select.1, obj2.txt2.select.1)    idx <- sample(nrow(pheno))    pheno <- pheno[idx, ]    obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, doWithOneRestart(return(expr), restart)
  41: withOneRestart(expr, restarts[[1L]])
          id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj1$id_include))    select[is.na(select)] <- 0    obj1.outfile.bed.noselect.2 <- tempfile()42: withRestarts(tryCatch(withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {    glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2)    obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2)    obj1.outfile.bed.select.2 <- tempfile()        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }    glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2)    obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.select.1, obj1.bed.select.2)    obj1.outfile.bgen.noselect.2 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj1.outfile.bgen.noselect.2)    obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2)    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal),     end_test = function() {    })    obj1.outfile.bgen.select.2 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj1.outfile.bgen.select.2)    obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2,         header = TRUE, as.is = TRUE)
  43: test_code(code = exprs, env = env, reporter = get_reporter() %||%     StopReporter$new())
      expect_equal(obj1.bgen.select.1, obj1.bgen.select.2)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj1.outfile.gds.noselect.2 <- tempfile()        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2)        obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2,             header = TRUE, as.is = TRUE)44: source_file(path, env = env(env), desc = desc, shuffle = shuffle,     error_call = error_call)
  45:         expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2)        obj1.outfile.gds.select.2 <- tempfile()        glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2)        obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2,             header = TRUE, as.is = TRUE)FUN(X[[i]], ...)
  46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,     error_call = error_call)        expect_equal(obj1.gds.select.1, obj1.gds.select.2)    }    obj1.outfile.txt.select.2 <- tempfile()    glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 
  47: doTryCatch(return(expr), name, parentenv, handler)
  48:         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt.select.1, obj1.txt.select.2)tryCatchOne(expr, names, parentenv, handlers[[1L]])
  49: tryCatchList(expr, classes, parentenv, handlers)
      obj1.outfile.txt1.select.2 <- tempfile()    glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))50: tryCatch(code, testthat_abort_reporter = function(cnd) {    cat(conditionMessage(cnd), "\n")    NULL})    obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt1.select.1, obj1.txt1.select.2)    obj1.outfile.txt2.select.2 <- tempfile()    glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, 
  51: with_reporter(reporters$multi, lapply(test_paths, test_one_file,     env = env, desc = desc, shuffle = shuffle, error_call = error_call))
          infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2,         header = TRUE, as.is = TRUE)52: test_files_serial(test_dir = test_dir, test_package = test_package,     test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,     env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,     desc = desc, load_package = load_package, shuffle = shuffle,     expect_equal(obj1.txt2.select.1, obj1.txt2.select.2)    obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj2$id_include))    select[is.na(select)] <- 0    obj2.outfile.bed.noselect.2 <- tempfile()    error_call = error_call)
  53: test_files(test_dir = path, test_paths = test_paths, test_package = package,     reporter = reporter, load_helpers = load_helpers, env = env,     glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2)    obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2)    obj2.outfile.bed.select.2 <- tempfile()    glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2)    obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.select.1, obj2.bed.select.2)    stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,     load_package = load_package, parallel = parallel, shuffle = shuffle)
  54: test_dir("testthat", package = package, reporter = reporter,     ..., load_package = "installed")
  55:     obj2.outfile.bgen.noselect.2 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj2.outfile.bgen.noselect.2)    obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2)    obj2.outfile.bgen.select.2 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, test_check("GMMAT")
  An irrecoverable exception occurred. R is aborting now ...
          select = select, outfile = obj2.outfile.bgen.select.2)    obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.select.1, obj2.bgen.select.2)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj2.outfile.gds.noselect.2 <- tempfile()        glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2)        obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2)        obj2.outfile.gds.select.2 <- tempfile()        glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2)        obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.select.1, obj2.gds.select.2)    }    obj2.outfile.txt.select.2 <- tempfile()    glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt.select.1, obj2.txt.select.2)    obj2.outfile.txt1.select.2 <- tempfile()    glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt1.select.1, obj2.txt1.select.2)    obj2.outfile.txt2.select.2 <- tempfile()    glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)    idx <- sample(nrow(kins))    kins <- kins[idx, idx]    obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj1$id_include))    select[is.na(select)] <- 0    obj1.outfile.bed.noselect.3 <- tempfile()    glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3)    obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3)    obj1.outfile.bed.select.3 <- tempfile()    glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3)    obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bed.select.1, obj1.bed.select.3)    obj1.outfile.bgen.noselect.3 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj1.outfile.bgen.noselect.3)    obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)    obj1.outfile.bgen.select.3 <- tempfile()    glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj1.outfile.bgen.select.3)    obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj1.outfile.gds.noselect.3 <- tempfile()        glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)        obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)        obj1.outfile.gds.select.3 <- tempfile()        glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)        obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3,             header = TRUE, as.is = TRUE)        expect_equal(obj1.gds.select.1, obj1.gds.select.3)    }    obj1.outfile.txt.select.3 <- tempfile()    glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt.select.1, obj1.txt.select.3)    obj1.outfile.txt1.select.3 <- tempfile()    glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)    obj1.outfile.txt2.select.3 <- tempfile()    glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)    obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL,         id = "id", family = binomial(link = "logit"), method = "REML",         method.optim = "AI")    select <- match(1:400, unique(obj2$id_include))    select[is.na(select)] <- 0    obj2.outfile.bed.noselect.3 <- tempfile()    glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)    obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)    obj2.outfile.bed.select.3 <- tempfile()    glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3)    obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bed.select.1, obj2.bed.select.3)    obj2.outfile.bgen.noselect.3 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         outfile = obj2.outfile.bgen.noselect.3)    obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)    obj2.outfile.bgen.select.3 <- tempfile()    glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,         select = select, outfile = obj2.outfile.bgen.select.3)    obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE)) {        obj2.outfile.gds.noselect.3 <- tempfile()        glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)        obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)        obj2.outfile.gds.select.3 <- tempfile()        glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)        obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3,             header = TRUE, as.is = TRUE)        expect_equal(obj2.gds.select.1, obj2.gds.select.3)    }    obj2.outfile.txt.select.3 <- tempfile()    glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt.select.1, obj2.txt.select.3)    obj2.outfile.txt1.select.3 <- tempfile()    glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)    obj2.outfile.txt2.select.3 <- tempfile()    glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3,         infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,         select = select, infile.header.print = c("SNP", "Allele1",             "Allele2"))    obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,         header = TRUE, as.is = TRUE)    expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)    unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,         obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1,         obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1,         obj2.outfile.txt2.select.1))    unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2,         obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2,         obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2,         obj1.outfile.txt2.select.2))    unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,         obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2,         obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2,         obj2.outfile.txt2.select.2))    unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3,         obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3,         obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3,         obj1.outfile.txt2.select.3))    unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3,         obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3,         obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3,         obj2.outfile.txt2.select.3))    if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",         quietly = TRUE))         unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1,             obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2,             obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2,             obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3,             obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
  33: eval(code, test_env)
  34: eval(code, test_env)
  35: withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt)
  36: doTryCatch(return(expr), name, parentenv, handler)
  37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  38: tryCatchList(expr, classes, parentenv, handlers)
  39: tryCatch(withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
  40: doWithOneRestart(return(expr), restart)
  41: withOneRestart(expr, restarts[[1L]])
  42: withRestarts(tryCatch(withCallingHandlers({    eval(code, test_env)    new_expectations <- the$test_expectations > starting_expectations    if (snapshot_skipped) {        skip("On CRAN")    }    else if (!new_expectations && skip_on_empty) {        skip_empty()    }}, expectation = handle_expectation, packageNotFoundError = function(e) {    if (on_cran()) {        skip(paste0("{", e$package, "} is not installed."))    }}, snapshot_on_cran = function(cnd) {    snapshot_skipped <<- TRUE    invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,     error = handle_error, interrupt = handle_interrupt), error = handle_fatal),     end_test = function() {    })
  43: test_code(code = exprs, env = env, reporter = get_reporter() %||%     StopReporter$new())
  44: source_file(path, env = env(env), desc = desc, shuffle = shuffle,     error_call = error_call)
  45: FUN(X[[i]], ...)
  46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,     error_call = error_call)
  47: doTryCatch(return(expr), name, parentenv, handler)
  48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  49: tryCatchList(expr, classes, parentenv, handlers)
  50: tryCatch(code, testthat_abort_reporter = function(cnd) {    cat(conditionMessage(cnd), "\n")    NULL})
  51: with_reporter(reporters$multi, lapply(test_paths, test_one_file,     env = env, desc = desc, shuffle = shuffle, error_call = error_call))
  52: test_files_serial(test_dir = test_dir, test_package = test_package,     test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,     env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,     desc = desc, load_package = load_package, shuffle = shuffle,     error_call = error_call)
  53: test_files(test_dir = path, test_paths = test_paths, test_package = package,     reporter = reporter, load_helpers = load_helpers, env = env,     stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,     load_package = load_package, parallel = parallel, shuffle = shuffle)
  54: test_dir("testthat", package = package, reporter = reporter,     ..., load_package = "installed")
  55: test_check("GMMAT")
  An irrecoverable exception occurred. R is aborting now ...
  Saving _problems/test_glmm.score-37.R
  The following SNPs have been removed due to inconsistent alleles across studies:
  [1] "L10" "L12" "L15"
  [ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
  
  ══ Skipped tests (30) ══════════════════════════════════════════════════════════
  • On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
    'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
    'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
    'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
    'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
    'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
    'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
    'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
    'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
    'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
    'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
  • {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
  Error in `file(outfile, "w")`: cannot open the connection
  Backtrace:
      ▆
   1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
   2.   └─base::file(outfile, "w")
  
  [ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
  Error:
  ! Test failures.
  Execution halted