Last updated on 2026-10-25 09:51:48 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 44.27 | 238.86 | 283.13 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.46 | 212.30 | 250.76 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 154.08 | 184.08 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 163.82 | 205.82 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 392.00 | 462.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.18 | 226.93 | 278.11 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 51.51 | 238.45 | 289.96 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 67.00 | 310.00 | 377.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 18.00 | 70.00 | 88.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 88.00 | 402.00 | 490.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
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)
Traceback:
1: 3: doTryCatch(return(expr), name, parentenv, handler)eval(c.expr, envir = args, enclos = envir)
2: 4: eval(c.expr, envir = args, enclos = envir)tryCatchOne(expr, names, parentenv, handlers[[1L]])
3: 5: doTryCatch(return(expr), name, parentenv, handler)tryCatchList(expr, classes, parentenv, handlers)
4: 6: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
5: 7: tryCatchList(expr, classes, parentenv, handlers)FUN(X[[i]], ...)
6: 8: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)lapply(X = S, FUN = FUN, ...)
7: 9: FUN(X[[i]], ...)doTryCatch(return(expr), name, parentenv, handler)
8: 10: lapply(X = S, FUN = FUN, ...)tryCatchOne(expr, names, parentenv, handlers[[1L]])
9: 11: doTryCatch(return(expr), name, parentenv, handler)
tryCatchList(expr, classes, parentenv, handlers)10:
tryCatchOne(expr, names, parentenv, handlers[[1L]])12:
tryCatch(expr, error = function(e) {11: call <- conditionCall(e)tryCatchList(expr, classes, parentenv, handlers) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 12: call <- sys.call(-4L)tryCatch(expr, error = function(e) { dcall <- deparse(call, nlines = 1L) call <- conditionCall(e) prefix <- paste("Error in", dcall, ": ") if (!is.null(call)) { LONG <- 75L if (identical(call[[1L]], quote(doTryCatch))) sm <- strsplit(conditionMessage(e), "\n")[[1L]] call <- sys.call(-4L) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") dcall <- deparse(call, nlines = 1L) if (is.na(w)) prefix <- paste("Error in", dcall, ": ") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], LONG <- 75L type = "b") sm <- strsplit(conditionMessage(e), "\n")[[1L]] if (w > LONG) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") prefix <- paste0(prefix, "\n ") if (is.na(w)) } w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], else prefix <- "Error : " type = "b") msg <- paste0(prefix, conditionMessage(e), "\n") if (w > LONG) .Internal(seterrmessage(msg[1L])) prefix <- paste0(prefix, "\n ") if (!silent && isTRUE(getOption("show.error.messages"))) { } cat(msg, file = outFile) else prefix <- "Error : " .Internal(printDeferredWarnings()) msg <- paste0(prefix, conditionMessage(e), "\n") } .Internal(seterrmessage(msg[1L])) invisible(structure(msg, class = "try-error", condition = e)) if (!silent && isTRUE(getOption("show.error.messages"))) {}) cat(msg, file = outFile)
.Internal(printDeferredWarnings())13: }try(lapply(X = S, FUN = FUN, ...), silent = TRUE) invisible(structure(msg, class = "try-error", condition = e))
})14:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))13:
try(lapply(X = S, FUN = FUN, ...), silent = TRUE)15:
FUN(X[[i]], ...)14:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
16: 15: lapply(seq_len(cores), inner.do)FUN(X[[i]], ...)
17: 16: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, lapply(seq_len(cores), inner.do)
mc.silent = silent, mc.cores = cores)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, 18: mc.silent = silent, mc.cores = cores)e$fun(obj, substitute(ex), parent.frame(), e$data)
18: 19: e$fun(obj, substitute(ex), parent.frame(), e$data)foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) {19: if (bgenInfo$LayoutFlag == 2) {foreach(i = 1:ncores) %dopar% { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, if (!is.null(obj$P)) { infile, paste0(outfile, "_tmp.", i), center2, if (bgenInfo$LayoutFlag == 2) { MAF.range[1], MAF.range[2], miss.cutoff, miss.method, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], infile, paste0(outfile, "_tmp.", i), center2, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 1) } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) else { } .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, else { infile, paste0(outfile, "_tmp.", i), center2, .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, infile, paste0(outfile, "_tmp.", i), center2, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], MAF.range[1], MAF.range[2], miss.cutoff, miss.method, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) 1) } } } } else { else { if (bgenInfo$LayoutFlag == 2) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj$Sigma_iX, obj$cov, infile, paste0(outfile, miss.cutoff, miss.method, nperbatch, select, "_tmp.", i), center2, MAF.range[1], MAF.range[2], threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], miss.cutoff, miss.method, nperbatch, select, bgenInfo$N, bgenInfo$CompressionFlag, 1) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, bgenInfo$N, bgenInfo$CompressionFlag, 1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], } miss.cutoff, miss.method, nperbatch, select, else { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, bgenInfo$N, bgenInfo$CompressionFlag, 1) } obj$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: 20: eval(code, test_env)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)22:
eval(code, test_env)21:
eval(code, test_env)23:
withCallingHandlers({22: eval(code, test_env)eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {23: skip("On CRAN")withCallingHandlers({ } eval(code, test_env) else if (!new_expectations && skip_on_empty) { new_expectations <- the$test_expectations > starting_expectations skip_empty() if (snapshot_skipped) { skip("On CRAN") } }}, expectation = handle_expectation, packageNotFoundError = function(e) { else if (!new_expectations && skip_on_empty) { if (on_cran()) { skip_empty() skip(paste0("{", e$package, "} is not installed.")) } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, snapshot_on_cran = function(cnd) { if (on_cran()) { snapshot_skipped <<- TRUE skip(paste0("{", e$package, "} is not installed.")) invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, }, snapshot_on_cran = function(cnd) { error = handle_error, interrupt = handle_interrupt) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")24: }, skip = handle_skip, warning = handle_warning, message = handle_message, doTryCatch(return(expr), name, parentenv, handler) error = handle_error, interrupt = handle_interrupt)
25: 24: tryCatchOne(expr, names, parentenv, handlers[[1L]])doTryCatch(return(expr), name, parentenv, handler)
26: 25: tryCatchList(expr, classes, parentenv, handlers)tryCatchOne(expr, names, parentenv, handlers[[1L]])
27: 26: tryCatch(withCallingHandlers({tryCatchList(expr, classes, parentenv, handlers) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations27: if (snapshot_skipped) {tryCatch(withCallingHandlers({ skip("On CRAN") eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations else if (!new_expectations && skip_on_empty) { if (snapshot_skipped) { skip_empty() skip("On CRAN") } }}, expectation = handle_expectation, packageNotFoundError = function(e) { else if (!new_expectations && skip_on_empty) { if (on_cran()) { skip_empty() skip(paste0("{", e$package, "} is not installed.")) } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, snapshot_on_cran = function(cnd) { if (on_cran()) { snapshot_skipped <<- TRUE skip(paste0("{", e$package, "} is not installed.")) invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE28: invokeRestart("muffle_cran_snapshot")doWithOneRestart(return(expr), restart)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal)29:
withOneRestart(expr, restarts[[1L]])28:
doWithOneRestart(return(expr), restart)30:
withRestarts(tryCatch(withCallingHandlers({29: eval(code, test_env)withOneRestart(expr, restarts[[1L]]) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {30: skip("On CRAN")withRestarts(tryCatch(withCallingHandlers({ } eval(code, test_env) else if (!new_expectations && skip_on_empty) { new_expectations <- the$test_expectations > starting_expectations skip_empty() if (snapshot_skipped) { } skip("On CRAN")}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { else if (!new_expectations && skip_on_empty) { skip(paste0("{", e$package, "} is not installed.")) skip_empty() } }}, snapshot_on_cran = function(cnd) {}, expectation = handle_expectation, packageNotFoundError = function(e) { snapshot_skipped <<- TRUE if (on_cran()) { invokeRestart("muffle_cran_snapshot") skip(paste0("{", e$package, "} is not installed."))}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal), }, snapshot_on_cran = function(cnd) { end_test = function() { snapshot_skipped <<- TRUE }) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 31: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), test_code(code, parent.frame()) end_test = function() {
})32:
test_that("cross-sectional id le 400 binomial", {31: plinkfiles <- strsplit(system.file("extdata", "geno.bed", test_code(code, parent.frame()) package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")32: samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")test_that("cross-sectional id le 400 binomial", { gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") plinkfiles <- strsplit(system.file("extdata", "geno.bed", txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") package = "GMMAT"), ".bed", fixed = TRUE)[[1]] txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") data(example) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") suppressWarnings(RNGversion("3.5.0")) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") set.seed(123) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") pheno <- rbind(example$pheno, example$pheno[1:100, ]) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") pheno$id <- 1:500 data(example) pheno$disease[sample(1:500, 20)] <- NA suppressWarnings(RNGversion("3.5.0")) pheno$age[sample(1:500, 20)] <- NA set.seed(123) pheno$sex[sample(1:500, 20)] <- NA pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno <- pheno[sample(1:500, 450), ] pheno$id <- 1:500 pheno <- pheno[pheno$id <= 400, ] pheno$disease[sample(1:500, 20)] <- NA kins <- example$GRM pheno$age[sample(1:500, 20)] <- NA obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, pheno$sex[sample(1:500, 20)] <- NA id = "id", family = binomial(link = "logit"), method = "REML", pheno <- pheno[sample(1:500, 450), ] method.optim = "AI") pheno <- pheno[pheno$id <= 400, ] select <- match(1:400, unique(obj1$id_include)) kins <- example$GRM select[is.na(select)] <- 0 obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.bed.noselect.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) method.optim = "AI") obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1.tmp <- tempfile() obj1.outfile.bed.noselect.1 <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, unlink(obj1.outfile.bed.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.1 <- tempfile() obj1.outfile.bed.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.bed.noselect.1.tmp) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bed.select.1 <- tempfile() obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, outfile = obj1.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(obj1.outfile.bgen.noselect.1.tmp) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.bgen.select.1 <- tempfile() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, unlink(obj1.outfile.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", select = select, outfile = obj1.outfile.bgen.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", quietly = TRUE)) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.outfile.gds.noselect.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) "VAR", "PVAL")]) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { obj1.outfile.gds.noselect.1.tmp <- tempfile() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) ncores = 2) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, unlink(obj1.outfile.gds.noselect.1.tmp) ncores = 2) obj1.outfile.gds.select.1 <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.gds.noselect.1.tmp) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) obj1.outfile.gds.select.1 <- tempfile() expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, 0.986534857))) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) } expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1.outfile.txt.select.1 <- tempfile() expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, 0.986534857))) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj1.outfile.txt.select.1 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.1.tmp <- tempfile() "Allele2")) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.txt.select.1.tmp <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.outfile.txt1.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.txt.select.1.tmp) "Allele2")) obj1.outfile.txt1.select.1 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) 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) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.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.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() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt2.select.1)) id = "id", family = binomial(link = "logit"), method = "REML", skip_on_cran() method.optim = "AI") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.1 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) select <- match(1:400, unique(obj2$id_include)) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.1 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.outfile.bgen.noselect.1 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.1) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.1 <- tempfile() outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, select = select, outfile = obj2.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "VAR", "PVAL")]) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", quietly = TRUE)) { "VAR", "PVAL")]) obj2.outfile.gds.noselect.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) quietly = TRUE)) { obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.gds.select.1 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) 0.996996766))) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, } 0.996996766))) obj2.outfile.txt.select.1 <- tempfile() } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.outfile.txt.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, idx <- sample(nrow(pheno)) id = "id", family = binomial(link = "logit"), method = "REML", pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(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) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.2 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.select.2 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj1.outfile.gds.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) quietly = TRUE)) { obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj1.txt.select.1, obj1.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2 <- tempfile() expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.outfile.txt2.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) method.optim = "AI") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select <- match(1:400, unique(obj2$id_include)) id = "id", family = binomial(link = "logit"), method = "REML", select[is.na(select)] <- 0 method.optim = "AI") obj2.outfile.bed.noselect.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select[is.na(select)] <- 0 obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj2.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.outfile.bed.select.2 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, outfile = obj2.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, select = select, outfile = obj2.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) quietly = TRUE)) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.gds.noselect.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.gds.noselect.2 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.outfile.gds.select.2 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, expect_equal(obj2.gds.select.1, obj2.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, 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, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) idx <- sample(nrow(kins)) idx <- sample(nrow(kins)) kins <- kins[idx, idx] kins <- kins[idx, idx] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) outfile = obj1.outfile.bgen.noselect.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3) select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { quietly = TRUE)) { obj1.outfile.gds.noselect.3 <- tempfile() obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) expect_equal(obj1.gds.select.1, obj1.gds.select.3) } } obj1.outfile.txt.select.3 <- tempfile() obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt2.select.3 <- tempfile() obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, 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", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, method.optim = "AI") id = "id", family = binomial(link = "logit"), method = "REML", select <- match(1:400, unique(obj2$id_include)) method.optim = "AI") select[is.na(select)] <- 0 obj2.outfile.bed.noselect.3 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) select[is.na(select)] <- 0 obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.bed.select.3 <- tempfile() obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, expect_equal(obj2.bed.select.1, obj2.bed.select.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.3 <- tempfile() outfile = obj2.outfile.bgen.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, outfile = obj2.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.bgen.select.3 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) select = select, outfile = obj2.outfile.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) quietly = TRUE)) { obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj2.outfile.gds.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj2.gds.select.1, obj2.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, obj2.outfile.txt.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj2.txt.select.1, obj2.txt.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj2.outfile.txt1.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, obj2.outfile.txt2.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt2.select.1)) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.outfile.txt2.select.1)) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt2.select.2)) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj1.outfile.txt2.select.2)) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt2.select.2)) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.outfile.txt2.select.2)) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.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)) 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, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt2.select.3)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, quietly = TRUE)) obj2.outfile.txt2.select.3)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, quietly = TRUE)) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) 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: 33: eval(code, test_env)
eval(code, test_env)34: eval(code, test_env)
35: 35: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectationswithCallingHandlers({ if (snapshot_skipped) { eval(code, test_env) skip("On CRAN") new_expectations <- the$test_expectations > starting_expectations } else if (!new_expectations && skip_on_empty) { if (snapshot_skipped) { skip("On CRAN") skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) { else if (!new_expectations && skip_on_empty) { if (on_cran()) { skip_empty() } skip(paste0("{", e$package, "} is not installed."))}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) {}, snapshot_on_cran = function(cnd) { skip(paste0("{", e$package, "} is not installed.")) } snapshot_skipped <<- TRUE}, snapshot_on_cran = function(cnd) { invokeRestart("muffle_cran_snapshot") snapshot_skipped <<- TRUE}, skip = handle_skip, warning = handle_warning, message = handle_message, invokeRestart("muffle_cran_snapshot") error = handle_error, interrupt = handle_interrupt)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt)36:
doTryCatch(return(expr), name, parentenv, handler)
36: 37: doTryCatch(return(expr), name, parentenv, handler)tryCatchOne(expr, names, parentenv, handlers[[1L]])
37: 38: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchList(expr, classes, parentenv, handlers)
38: 39: tryCatchList(expr, classes, parentenv, handlers)tryCatch(withCallingHandlers({
eval(code, test_env)39: new_expectations <- the$test_expectations > starting_expectationstryCatch(withCallingHandlers({ if (snapshot_skipped) { eval(code, test_env) skip("On CRAN") new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { } skip("On CRAN") else if (!new_expectations && skip_on_empty) { } skip_empty() else if (!new_expectations && skip_on_empty) { } skip_empty()}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) {}, expectation = handle_expectation, packageNotFoundError = function(e) { skip(paste0("{", e$package, "} is not installed.")) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed."))}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE}, snapshot_on_cran = function(cnd) { invokeRestart("muffle_cran_snapshot") snapshot_skipped <<- TRUE}, skip = handle_skip, warning = handle_warning, message = handle_message, invokeRestart("muffle_cran_snapshot") error = handle_error, interrupt = handle_interrupt), error = handle_fatal)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: 40: doWithOneRestart(return(expr), restart)doWithOneRestart(return(expr), restart)
41: 41: withOneRestart(expr, restarts[[1L]])withOneRestart(expr, restarts[[1L]])
42: 42: withRestarts(tryCatch(withCallingHandlers({withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { if (snapshot_skipped) { skip("On CRAN") skip("On CRAN") } } else if (!new_expectations && skip_on_empty) { else if (!new_expectations && skip_on_empty) { skip_empty() skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) skip(paste0("{", e$package, "} is not installed.")) } }}, snapshot_on_cran = function(cnd) {}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") snapshot_skipped <<- TRUE}, skip = handle_skip, warning = handle_warning, message = handle_message, invokeRestart("muffle_cran_snapshot") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), }, skip = handle_skip, warning = handle_warning, message = handle_message, end_test = function() { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), }) end_test = function() {
})43:
test_code(code = exprs, env = env, reporter = get_reporter() %||% 43: StopReporter$new())test_code(code = exprs, env = env, reporter = get_reporter() %||%
StopReporter$new())44:
source_file(path, env = env(env), desc = desc, shuffle = shuffle, 44: error_call = error_call)source_file(path, env = env(env), desc = desc, shuffle = shuffle,
error_call = error_call)
45: 45: FUN(X[[i]], ...)FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, 46: error_call = error_call)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,
error_call = error_call)47:
doTryCatch(return(expr), name, parentenv, handler)47:
doTryCatch(return(expr), name, parentenv, handler)48:
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: 49: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
50: 50: tryCatch(code, testthat_abort_reporter = function(cnd) {tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") cat(conditionMessage(cnd), "\n") NULL NULL})})
51: 51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: 52: test_files_serial(test_dir = test_dir, test_package = test_package, test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call) error_call = error_call)
53: 53: test_files(test_dir = path, test_paths = test_paths, test_package = package, test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle) load_package = load_package, parallel = parallel, shuffle = shuffle)
54: 54: test_dir("testthat", package = package, reporter = reporter, test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed") ..., load_package = "installed")
55: 55: test_check("GMMAT")test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
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
Flavor: r-oldrel-macos-arm64