Last updated on 2026-10-26 01:51:34 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 | 39.04 | 206.59 | 245.63 | 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 | 19.00 | 86.00 | 105.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 89.00 | 386.00 | 475.00 | 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)
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$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
Traceback:
22: eval(code, test_env) 1:
eval(c.expr, envir = args, enclos = envir)23:
withCallingHandlers({ 2: eval(code, test_env) new_expectations <- the$test_expectations > starting_expectationseval(c.expr, envir = args, enclos = envir) if (snapshot_skipped) { skip("On CRAN")
} 3: else if (!new_expectations && skip_on_empty) { skip_empty()doTryCatch(return(expr), name, parentenv, handler) }
}, expectation = handle_expectation, packageNotFoundError = function(e) { 4: tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed.")) } 5: }, snapshot_on_cran = function(cnd) {tryCatchList(expr, classes, parentenv, handlers) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot") 6: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) error = handle_error, interrupt = handle_interrupt)
7: FUN(X[[i]], ...)24:
doTryCatch(return(expr), name, parentenv, handler)
8: lapply(X = S, FUN = FUN, ...)25:
tryCatchOne(expr, names, parentenv, handlers[[1L]]) 9:
doTryCatch(return(expr), name, parentenv, handler)26:
tryCatchList(expr, classes, parentenv, handlers)10:
tryCatchOne(expr, names, parentenv, handlers[[1L]])27:
tryCatch(withCallingHandlers({11: eval(code, test_env)tryCatchList(expr, classes, parentenv, handlers) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {12: skip("On CRAN")tryCatch(expr, error = function(e) { } call <- conditionCall(e) else if (!new_expectations && skip_on_empty) { if (!is.null(call)) { skip_empty() if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) }}, expectation = handle_expectation, packageNotFoundError = function(e) { dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") if (on_cran()) { LONG <- 75L skip(paste0("{", e$package, "} is not installed.")) } sm <- strsplit(conditionMessage(e), "\n")[[1L]]}, snapshot_on_cran = function(cnd) { w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") snapshot_skipped <<- TRUE if (is.na(w)) invokeRestart("muffle_cran_snapshot") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], }, skip = handle_skip, warning = handle_warning, message = handle_message, type = "b") error = handle_error, interrupt = handle_interrupt), error = handle_fatal) if (w > LONG)
prefix <- paste0(prefix, "\n ")28: }doWithOneRestart(return(expr), restart)
else prefix <- "Error : "29: msg <- paste0(prefix, conditionMessage(e), "\n")withOneRestart(expr, restarts[[1L]]) .Internal(seterrmessage(msg[1L]))
30: if (!silent && isTRUE(getOption("show.error.messages"))) {withRestarts(tryCatch(withCallingHandlers({ cat(msg, file = outFile) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations .Internal(printDeferredWarnings()) if (snapshot_skipped) { } invisible(structure(msg, class = "try-error", condition = e)) skip("On CRAN")}) }
else if (!new_expectations && skip_on_empty) {13: skip_empty()try(lapply(X = S, FUN = FUN, ...), silent = TRUE) }
14: }, expectation = handle_expectation, packageNotFoundError = function(e) {sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))15: }FUN(X[[i]], ...)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE16: invokeRestart("muffle_cran_snapshot")lapply(seq_len(cores), inner.do)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 17: end_test = function() {mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, }) mc.silent = silent, mc.cores = cores)
31: 18: test_code(code, parent.frame())e$fun(obj, substitute(ex), parent.frame(), e$data)
32: 19: test_that("cross-sectional id le 400 binomial", {foreach(i = 1:ncores) %dopar% { plinkfiles <- strsplit(system.file("extdata", "geno.bed", if (!is.null(obj$P)) { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") infile, paste0(outfile, "_tmp.", i), center2, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") MAF.range[1], MAF.range[2], miss.cutoff, miss.method, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") nperbatch, select, threadInfo$begin[i], threadInfo$end[i], txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") 1) data(example) } else { suppressWarnings(RNGversion("3.5.0")) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, set.seed(123) infile, paste0(outfile, "_tmp.", i), center2, pheno <- rbind(example$pheno, example$pheno[1:100, ]) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, pheno$id <- 1:500 nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, pheno$disease[sample(1:500, 20)] <- NA 1) pheno$age[sample(1:500, 20)] <- NA } pheno$sex[sample(1:500, 20)] <- NA } pheno <- pheno[sample(1:500, 450), ] else { pheno <- pheno[pheno$id <= 400, ] if (bgenInfo$LayoutFlag == 2) { kins <- example$GRM .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj$Sigma_iX, obj$cov, infile, paste0(outfile, id = "id", family = binomial(link = "logit"), method = "REML", "_tmp.", i), center2, MAF.range[1], MAF.range[2], method.optim = "AI") miss.cutoff, miss.method, nperbatch, select, select <- match(1:400, unique(obj1$id_include)) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], select[is.na(select)] <- 0 bgenInfo$N, bgenInfo$CompressionFlag, 1) obj1.outfile.bed.noselect.1 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) else { obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, header = TRUE, as.is = TRUE) obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj1.outfile.bed.noselect.1.tmp <- tempfile() miss.cutoff, miss.method, nperbatch, select, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") bgenInfo$N, bgenInfo$CompressionFlag, 1) unlink(obj1.outfile.bed.noselect.1.tmp) } } obj1.outfile.bed.select.1 <- tempfile()} glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)
obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, 20: header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 21: outfile = obj1.outfile.bgen.noselect.1)eval(code, test_env) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1,
header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile()22: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, eval(code, test_env) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 23: header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) eval(code, test_env) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, new_expectations <- the$test_expectations > starting_expectations select = select, outfile = obj1.outfile.bgen.select.1) if (snapshot_skipped) { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, skip_empty() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, expectation = handle_expectation, packageNotFoundError = function(e) { quietly = TRUE)) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.gds.noselect.1 <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)}, snapshot_on_cran = function(cnd) { obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.noselect.1.tmp <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, error = handle_error, interrupt = handle_interrupt) ncores = 2)
obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, 24: header = TRUE, as.is = TRUE)doTryCatch(return(expr), name, parentenv, handler) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)
unlink(obj1.outfile.gds.noselect.1.tmp)25: obj1.outfile.gds.select.1 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)
obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, 26: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)
expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))27: expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, tryCatch(withCallingHandlers({ eval(code, test_env) 0.986534857))) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } skip("On CRAN") obj1.outfile.txt.select.1 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, else if (!new_expectations && skip_on_empty) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_empty() select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt.select.1.tmp <- tempfile() } expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, }, snapshot_on_cran = function(cnd) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", snapshot_skipped <<- TRUE "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") invokeRestart("muffle_cran_snapshot") unlink(obj1.outfile.txt.select.1.tmp)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.txt1.select.1 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal) 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", 28: "Allele2"))doWithOneRestart(return(expr), restart) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1,
header = TRUE, as.is = TRUE)29: expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile()withOneRestart(expr, restarts[[1L]]) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 30: withRestarts(tryCatch(withCallingHandlers({ select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2")) new_expectations <- the$test_expectations > starting_expectations obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, } obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, else if (!new_expectations && skip_on_empty) { obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, skip_empty() obj1.outfile.txt2.select.1)) skip_on_cran() } obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, }, expectation = handle_expectation, packageNotFoundError = function(e) { id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") if (on_cran()) { select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 skip(paste0("{", e$package, "} is not installed.")) 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, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE obj2.outfile.bed.select.1 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) end_test = function() { obj2.outfile.bgen.noselect.1 <- tempfile() }) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj2.outfile.bgen.noselect.1)31: test_code(code, parent.frame()) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1,
header = TRUE, as.is = TRUE)32: obj2.outfile.bgen.select.1 <- tempfile()test_that("cross-sectional id le 400 binomial", { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, plinkfiles <- strsplit(system.file("extdata", "geno.bed", select = select, outfile = obj2.outfile.bgen.select.1) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") "VAR", "PVAL")]) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", data(example) quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() suppressWarnings(RNGversion("3.5.0")) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) set.seed(123) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, pheno <- rbind(example$pheno, example$pheno[1:100, ]) header = TRUE, as.is = TRUE) pheno$id <- 1:500 obj2.outfile.gds.select.1 <- tempfile() pheno$disease[sample(1:500, 20)] <- NA glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, pheno <- pheno[sample(1:500, 450), ] header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) pheno <- pheno[pheno$id <= 400, ] expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) kins <- example$GRM expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 0.996996766))) id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI") obj2.outfile.txt.select.1 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) "Allele2")) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj2.outfile.txt1.select.1 <- tempfile() ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, unlink(obj1.outfile.bed.noselect.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt2.select.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, unlink(obj1.outfile.bgen.noselect.1.tmp) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bgen.select.1 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj1$id_include)) select = select, outfile = obj1.outfile.bgen.select.1) select[is.na(select)] <- 0 obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.outfile.bed.select.2 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) "VAR", "PVAL")]) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bed.select.1, obj1.bed.select.2) quietly = TRUE)) { obj1.outfile.bgen.noselect.2 <- tempfile() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) outfile = obj1.outfile.bgen.noselect.2) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj1.outfile.bgen.select.2 <- tempfile() ncores = 2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, 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.gds.noselect.1, obj1.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.gds.noselect.1.tmp) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.outfile.gds.noselect.2 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, 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(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.outfile.gds.select.2 <- tempfile() 0.986534857))) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.outfile.txt.select.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, 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"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, "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, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) "Allele2")) obj1.outfile.txt2.select.2 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", "Allele2")) method.optim = "AI") obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1)) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) skip_on_cran() obj2.outfile.bed.select.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bed.select.1, obj2.bed.select.2) select[is.na(select)] <- 0 obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bed.noselect.1 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.outfile.bed.select.1 <- tempfile() obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.2) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj2.outfile.bgen.noselect.1 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 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) outfile = obj2.outfile.bgen.noselect.1) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.outfile.bgen.select.1 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj2.gds.select.1, obj2.gds.select.2) 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) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "VAR", "PVAL")]) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.gds.noselect.1 <- tempfile() obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.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", obj2.outfile.gds.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.outfile.txt2.select.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, select = select, infile.header.print = c("SNP", "Allele1", 0.996996766))) "Allele2")) } obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.outfile.txt.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, idx <- sample(nrow(kins)) select = select, infile.header.print = c("SNP", "Allele1", kins <- kins[idx, idx] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, "Allele2")) id = "id", family = binomial(link = "logit"), method = "REML", obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) select[is.na(select)] <- 0 obj2.outfile.txt1.select.1 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) "Allele2")) obj1.outfile.bed.select.3 <- tempfile() obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj1.outfile.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, idx <- sample(nrow(pheno)) select = select, outfile = obj1.outfile.bgen.select.3) pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) select <- match(1:400, unique(obj1$id_include)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select[is.na(select)] <- 0 quietly = TRUE)) { obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, 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.gds.select.1, obj1.gds.select.3) obj1.outfile.bgen.noselect.2 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, outfile = obj1.outfile.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt1.select.3 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, 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.bgen.select.1, obj1.bgen.select.2) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.gds.noselect.2 <- tempfile() obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, 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.gds.noselect.1, obj1.gds.noselect.2) "Allele2")) obj1.outfile.gds.select.2 <- tempfile() obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, 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.gds.select.1, obj1.gds.select.2) id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI") obj1.outfile.txt.select.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) "Allele2")) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, 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(obj2.bed.select.1, obj2.bed.select.3) "Allele2")) obj2.outfile.bgen.noselect.3 <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj2.outfile.bgen.select.3) "Allele2")) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", id = "id", family = binomial(link = "logit"), method = "REML", quietly = TRUE)) { method.optim = "AI") obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) select <- match(1:400, unique(obj2$id_include)) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.outfile.gds.select.3 <- tempfile() obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.2 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) } obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, expect_equal(obj2.bed.select.1, obj2.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj2.outfile.bgen.noselect.2) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.outfile.txt1.select.3 <- tempfile() obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, outfile = obj2.outfile.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, 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.txt1.select.1, obj2.txt1.select.3) quietly = TRUE)) { obj2.outfile.txt2.select.3 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, } obj2.outfile.txt2.select.1)) obj2.outfile.txt.select.2 <- tempfile() unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.outfile.txt2.select.2)) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, "Allele2")) obj1.outfile.txt2.select.3)) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.2 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, "Allele2")) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))}) idx <- sample(nrow(kins))
kins <- kins[idx, idx]33: obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, eval(code, test_env) id = "id", family = binomial(link = "logit"), method = "REML",
34: method.optim = "AI")eval(code, test_env) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 035: obj1.outfile.bed.noselect.3 <- tempfile()withCallingHandlers({ glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) eval(code, test_env) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) skip("On CRAN") obj1.outfile.bed.select.3 <- tempfile() } glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) else if (!new_expectations && skip_on_empty) { obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1, obj1.bed.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj1.outfile.bgen.noselect.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.3)}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) error = handle_error, interrupt = handle_interrupt) obj1.outfile.bgen.select.3 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 36: select = select, outfile = obj1.outfile.bgen.select.3)doTryCatch(return(expr), name, parentenv, handler) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3,
header = TRUE, as.is = TRUE)37: expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {38: tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.gds.noselect.3 <- tempfile()
glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)39: obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.gds.select.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) skip("On CRAN") obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.gds.select.1, obj1.gds.select.3) skip_empty() } } obj1.outfile.txt.select.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, if (on_cran()) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip(paste0("{", e$package, "} is not installed.")) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, snapshot_on_cran = function(cnd) { obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.txt.select.1, obj1.txt.select.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.txt1.select.3 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 40: select = select, infile.header.print = c("SNP", "Allele1", doWithOneRestart(return(expr), restart) "Allele2"))
obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, 41: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)
obj1.outfile.txt2.select.3 <- tempfile()42: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, withRestarts(tryCatch(withCallingHandlers({ infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1", new_expectations <- the$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) else if (!new_expectations && skip_on_empty) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip_empty() id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI")}, expectation = handle_expectation, packageNotFoundError = function(e) { select <- match(1:400, unique(obj2$id_include)) if (on_cran()) { select[is.na(select)] <- 0 obj2.outfile.bed.noselect.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) } obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) invokeRestart("muffle_cran_snapshot") obj2.outfile.bed.select.3 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) end_test = function() { 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)43: test_code(code = exprs, env = env, reporter = get_reporter() %||% obj2.outfile.bgen.noselect.3 <- tempfile() StopReporter$new()) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj2.outfile.bgen.noselect.3)44: obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, source_file(path, env = env(env), desc = desc, shuffle = shuffle, header = TRUE, as.is = TRUE) error_call = error_call) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)
obj2.outfile.bgen.select.3 <- tempfile()45: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, FUN(X[[i]], ...) select = select, outfile = obj2.outfile.bgen.select.3)
obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, 46: header = TRUE, as.is = TRUE)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) error_call = error_call) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {47: obj2.outfile.gds.noselect.3 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)
obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, 48: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)
obj2.outfile.gds.select.3 <- tempfile()49: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)tryCatchList(expr, classes, parentenv, handlers) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3,
header = TRUE, as.is = TRUE)50: expect_equal(obj2.gds.select.1, obj2.gds.select.3)tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") } NULL obj2.outfile.txt.select.3 <- tempfile()}) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 51: select = select, infile.header.print = c("SNP", "Allele1", with_reporter(reporters$multi, lapply(test_paths, test_one_file, "Allele2")) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3,
header = TRUE, as.is = TRUE)52: expect_equal(obj2.txt.select.1, obj2.txt.select.3)test_files_serial(test_dir = test_dir, test_package = test_package, obj2.outfile.txt1.select.3 <- tempfile() test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, desc = desc, load_package = load_package, shuffle = shuffle, select = select, infile.header.print = c("SNP", "Allele1", error_call = error_call) "Allele2"))
obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, 53: header = TRUE, as.is = TRUE)test_files(test_dir = path, test_paths = test_paths, test_package = package, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) reporter = reporter, load_helpers = load_helpers, env = env, obj2.outfile.txt2.select.3 <- tempfile() stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, load_package = load_package, parallel = parallel, shuffle = shuffle) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))54: obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, test_dir("testthat", package = package, reporter = reporter, header = TRUE, as.is = TRUE) ..., load_package = "installed") expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)
unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, 55: obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, test_check("GMMAT") obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1,
obj2.outfile.txt2.select.1))An irrecoverable exception occurred. R is aborting now ...
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
Flavor: r-oldrel-macos-arm64