Last updated on 2026-11-12 02:55:05 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.11 | 246.51 | 291.62 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 36.78 | 213.88 | 250.66 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 26.00 | 141.72 | 167.72 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 72.00 | 391.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | OK | ||||
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | 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 | 75.00 | 318.00 | 393.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | ERROR | ||||
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 85.00 | 389.00 | 474.00 | 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)
Traceback:
1: 6: eval(c.expr, envir = args, enclos = envir)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
2: 7: eval(c.expr, envir = args, enclos = envir)FUN(X[[i]], ...)
3: 8: doTryCatch(return(expr), name, parentenv, handler)lapply(X = S, FUN = FUN, ...)
4: 9: tryCatchOne(expr, names, parentenv, handlers[[1L]])doTryCatch(return(expr), name, parentenv, handler)
5: 10: tryCatchList(expr, classes, parentenv, handlers)tryCatchOne(expr, names, parentenv, handlers[[1L]])
6: 11: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)tryCatchList(expr, classes, parentenv, handlers)
7: 12: FUN(X[[i]], ...)tryCatch(expr, error = function(e) {
call <- conditionCall(e) 8: if (!is.null(call)) {lapply(X = S, FUN = FUN, ...) if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L) 9: dcall <- deparse(call, nlines = 1L)doTryCatch(return(expr), name, parentenv, handler) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L10: sm <- strsplit(conditionMessage(e), "\n")[[1L]]tryCatchOne(expr, names, parentenv, handlers[[1L]]) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 11: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], tryCatchList(expr, classes, parentenv, handlers) type = "b")
if (w > LONG) 12: prefix <- paste0(prefix, "\n ")tryCatch(expr, error = function(e) { } call <- conditionCall(e) else prefix <- "Error : " if (!is.null(call)) { msg <- paste0(prefix, conditionMessage(e), "\n") if (identical(call[[1L]], quote(doTryCatch))) .Internal(seterrmessage(msg[1L])) call <- sys.call(-4L) if (!silent && isTRUE(getOption("show.error.messages"))) { dcall <- deparse(call, nlines = 1L) cat(msg, file = outFile) prefix <- paste("Error in", dcall, ": ") .Internal(printDeferredWarnings()) LONG <- 75L } sm <- strsplit(conditionMessage(e), "\n")[[1L]] invisible(structure(msg, class = "try-error", condition = e)) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")}) if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], 13: type = "b")try(lapply(X = S, FUN = FUN, ...), silent = TRUE) if (w > LONG)
prefix <- paste0(prefix, "\n ")14: }sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")15: .Internal(seterrmessage(msg[1L]))FUN(X[[i]], ...) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)16: .Internal(printDeferredWarnings())lapply(seq_len(cores), inner.do) }
invisible(structure(msg, class = "try-error", condition = e))17: })mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
13: 18: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)e$fun(obj, substitute(ex), parent.frame(), e$data)
14: 19: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) {15: if (bgenInfo$LayoutFlag == 2) {FUN(X[[i]], ...) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 16: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, lapply(seq_len(cores), inner.do) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 17: 1)mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, } mc.silent = silent, mc.cores = cores) else {
.Call(C_glmm_score_bgen11, as.numeric(res), obj$P, 18: infile, paste0(outfile, "_tmp.", i), center2, e$fun(obj, substitute(ex), parent.frame(), e$data) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 19: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, foreach(i = 1:ncores) %dopar% { 1) if (!is.null(obj$P)) { } if (bgenInfo$LayoutFlag == 2) { } .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, else { infile, paste0(outfile, "_tmp.", i), center2, if (bgenInfo$LayoutFlag == 2) { MAF.range[1], MAF.range[2], miss.cutoff, miss.method, .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj$Sigma_iX, obj$cov, infile, paste0(outfile, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, "_tmp.", i), center2, MAF.range[1], MAF.range[2], 1) miss.cutoff, miss.method, nperbatch, select, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], else { bgenInfo$N, bgenInfo$CompressionFlag, 1) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, } infile, paste0(outfile, "_tmp.", i), center2, else { MAF.range[1], MAF.range[2], miss.cutoff, miss.method, .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj$Sigma_iX, obj$cov, infile, paste0(outfile, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, "_tmp.", i), center2, MAF.range[1], MAF.range[2], 1) 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$cov, infile, paste0(outfile, } "_tmp.", i), center2, MAF.range[1], MAF.range[2],
miss.cutoff, miss.method, nperbatch, select, 20: threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, bgenInfo$N, bgenInfo$CompressionFlag, 1) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) }
else {21: .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, eval(code, test_env) obj$Sigma_iX, obj$cov, infile, paste0(outfile,
"_tmp.", i), center2, MAF.range[1], MAF.range[2], 22: miss.cutoff, miss.method, nperbatch, select, eval(code, test_env) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],
bgenInfo$N, bgenInfo$CompressionFlag, 1)23: }withCallingHandlers({ } eval(code, test_env)} new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {20: skip("On CRAN")glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) else if (!new_expectations && skip_on_empty) {
skip_empty()21: }eval(code, test_env)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {22: skip(paste0("{", e$package, "} is not installed."))eval(code, test_env) }
}, snapshot_on_cran = function(cnd) {23: snapshot_skipped <<- TRUEwithCallingHandlers({ invokeRestart("muffle_cran_snapshot") eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message, new_expectations <- the$test_expectations > starting_expectations error = handle_error, interrupt = handle_interrupt) if (snapshot_skipped) {
skip("On CRAN")24: }doTryCatch(return(expr), name, parentenv, handler) else if (!new_expectations && skip_on_empty) {
skip_empty()25: }tryCatchOne(expr, names, parentenv, handlers[[1L]])}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {26: skip(paste0("{", e$package, "} is not installed."))tryCatchList(expr, classes, parentenv, handlers) }
}, snapshot_on_cran = function(cnd) {27: snapshot_skipped <<- TRUEtryCatch(withCallingHandlers({ invokeRestart("muffle_cran_snapshot") eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message, new_expectations <- the$test_expectations > starting_expectations error = handle_error, interrupt = handle_interrupt) if (snapshot_skipped) {
skip("On CRAN")24: }doTryCatch(return(expr), name, parentenv, handler) else if (!new_expectations && skip_on_empty) {
skip_empty()25: }tryCatchOne(expr, names, parentenv, handlers[[1L]])}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {26: skip(paste0("{", e$package, "} is not installed."))tryCatchList(expr, classes, parentenv, handlers) }
}, snapshot_on_cran = function(cnd) {27: snapshot_skipped <<- TRUEtryCatch(withCallingHandlers({ invokeRestart("muffle_cran_snapshot") eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message, new_expectations <- the$test_expectations > starting_expectations error = handle_error, interrupt = handle_interrupt), error = handle_fatal) if (snapshot_skipped) {
skip("On CRAN")28: }doWithOneRestart(return(expr), restart) else if (!new_expectations && skip_on_empty) {
skip_empty()29: }withOneRestart(expr, restarts[[1L]])}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {30: skip(paste0("{", e$package, "} is not installed."))withRestarts(tryCatch(withCallingHandlers({ } eval(code, test_env)}, snapshot_on_cran = function(cnd) { new_expectations <- the$test_expectations > starting_expectations snapshot_skipped <<- TRUE if (snapshot_skipped) { invokeRestart("muffle_cran_snapshot") skip("On CRAN")}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal) else if (!new_expectations && skip_on_empty) {
skip_empty()28: }doWithOneRestart(return(expr), restart)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {29: skip(paste0("{", e$package, "} is not installed."))withOneRestart(expr, restarts[[1L]]) }
}, snapshot_on_cran = function(cnd) {30: snapshot_skipped <<- TRUEwithRestarts(tryCatch(withCallingHandlers({ invokeRestart("muffle_cran_snapshot") eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message, new_expectations <- the$test_expectations > starting_expectations error = handle_error, interrupt = handle_interrupt), error = handle_fatal), if (snapshot_skipped) { end_test = function() { skip("On CRAN") }) }
else if (!new_expectations && skip_on_empty) {31: skip_empty()test_code(code, parent.frame()) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {32: if (on_cran()) {test_that("cross-sectional id le 400 binomial", { skip(paste0("{", e$package, "} is not installed.")) plinkfiles <- strsplit(system.file("extdata", "geno.bed", } package = "GMMAT"), ".bed", fixed = TRUE)[[1]]}, snapshot_on_cran = function(cnd) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") snapshot_skipped <<- TRUE samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") invokeRestart("muffle_cran_snapshot") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")}, skip = handle_skip, warning = handle_warning, message = handle_message, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") end_test = function() { txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") }) data(example)
suppressWarnings(RNGversion("3.5.0"))31: set.seed(123)test_code(code, parent.frame()) pheno <- rbind(example$pheno, example$pheno[1:100, ])
pheno$id <- 1:50032: pheno$disease[sample(1:500, 20)] <- NAtest_that("cross-sectional id le 400 binomial", { pheno$age[sample(1:500, 20)] <- NA plinkfiles <- strsplit(system.file("extdata", "geno.bed", pheno$sex[sample(1:500, 20)] <- NA package = "GMMAT"), ".bed", fixed = TRUE)[[1]] pheno <- pheno[sample(1:500, 450), ] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") pheno <- pheno[pheno$id <= 400, ] samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") kins <- example$GRM gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") id = "id", family = binomial(link = "logit"), method = "REML", txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") method.optim = "AI") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") select <- match(1:400, unique(obj1$id_include)) data(example) select[is.na(select)] <- 0 suppressWarnings(RNGversion("3.5.0")) obj1.outfile.bed.noselect.1 <- tempfile() set.seed(123) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, pheno$id <- 1:500 header = TRUE, as.is = TRUE) pheno$disease[sample(1:500, 20)] <- NA obj1.outfile.bed.noselect.1.tmp <- tempfile() pheno$age[sample(1:500, 20)] <- NA expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, pheno$sex[sample(1:500, 20)] <- NA ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") pheno <- pheno[sample(1:500, 450), ] unlink(obj1.outfile.bed.noselect.1.tmp) pheno <- pheno[pheno$id <= 400, ] obj1.outfile.bed.select.1 <- tempfile() kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) select[is.na(select)] <- 0 obj1.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) outfile = obj1.outfile.bgen.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, unlink(obj1.outfile.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.1 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.outfile.bgen.noselect.1.tmp <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) "VAR", "PVAL")]) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.outfile.gds.noselect.1 <- tempfile() unlink(obj1.outfile.bgen.noselect.1.tmp) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.outfile.bgen.select.1 <- tempfile() obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.1) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", unlink(obj1.outfile.gds.noselect.1.tmp) "VAR", "PVAL")]) obj1.outfile.gds.select.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1.outfile.gds.noselect.1.tmp <- tempfile() expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, 0.986534857))) ncores = 2) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, unlink(obj1.outfile.gds.noselect.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) "Allele2")) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, 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"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.txt.select.1 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, 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")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt1.select.1) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.outfile.txt2.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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.txt2.select.1 <- read.table(obj1.outfile.txt2.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, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, "Allele2")) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1)) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) skip_on_cran() obj1.outfile.txt2.select.1 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, id = "id", family = binomial(link = "logit"), method = "REML", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, method.optim = "AI") select = select, infile.header.print = c("SNP", "Allele1", select <- match(1:400, unique(obj2$id_include)) "Allele2")) select[is.na(select)] <- 0 obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj2.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2.outfile.bed.select.1 <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj1.outfile.txt2.select.1)) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, skip_on_cran() header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bgen.noselect.1 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj2$id_include)) outfile = obj2.outfile.bgen.noselect.1) select[is.na(select)] <- 0 obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.outfile.bgen.select.1 <- tempfile() obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) obj2.outfile.bed.select.1 <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) header = TRUE, as.is = TRUE) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.outfile.bgen.noselect.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "VAR", "PVAL")]) outfile = obj2.outfile.bgen.noselect.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.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.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.bgen.noselect.1, obj2.bgen.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.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.gds.noselect.1, obj2.gds.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) "VAR", "PVAL")]) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 0.996996766))) quietly = TRUE)) { } obj2.outfile.gds.noselect.1 <- tempfile() obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.outfile.txt1.select.1 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 0.996996766))) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj2.outfile.txt.select.1 <- tempfile() obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj2.txt.select.1 <- read.table(obj2.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(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) "Allele2")) obj2.outfile.txt1.select.1 <- tempfile() obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) select = select, infile.header.print = c("SNP", "Allele1", idx <- sample(nrow(pheno)) "Allele2")) pheno <- pheno[idx, ] obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.txt.select.1, obj2.txt1.select.1) method.optim = "AI") obj2.outfile.txt2.select.1 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) "Allele2")) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.outfile.bed.select.2 <- tempfile() idx <- sample(nrow(pheno)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) pheno <- pheno[idx, ] obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bed.select.1, obj1.bed.select.2) method.optim = "AI") obj1.outfile.bgen.noselect.2 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select[is.na(select)] <- 0 outfile = obj1.outfile.bgen.noselect.2) obj1.outfile.bed.noselect.2 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.2 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.2 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, expect_equal(obj1.bgen.select.1, obj1.bgen.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.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) outfile = obj1.outfile.bgen.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.gds.select.2 <- tempfile() obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.gds.select.2 <- read.table(obj1.outfile.gds.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.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj1.outfile.txt.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) "Allele2")) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.gds.select.2 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.select.1, obj1.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj1.outfile.txt.select.2 <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.txt.select.2 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.2) "Allele2")) obj1.outfile.txt1.select.2 <- tempfile() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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, "Allele2")) id = "id", family = binomial(link = "logit"), method = "REML", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) select[is.na(select)] <- 0 obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj2.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bed.select.1, obj2.bed.select.2) method.optim = "AI") obj2.outfile.bgen.noselect.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select[is.na(select)] <- 0 outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.bed.noselect.2 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.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.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bed.select.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.bed.select.2 <- read.table(obj2.outfile.bed.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.select.1, obj2.bed.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bgen.noselect.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.noselect.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.bgen.select.2 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) select = select, outfile = obj2.outfile.bgen.select.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.outfile.gds.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.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.txt.select.1, obj2.txt.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, expect_equal(obj2.gds.select.1, obj2.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.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(obj2.txt1.select.1, obj2.txt1.select.2) "Allele2")) obj2.outfile.txt2.select.2 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, idx <- sample(nrow(kins)) header = TRUE, as.is = TRUE) kins <- kins[idx, idx] expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.txt2.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj1.outfile.bed.noselect.3 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) kins <- kins[idx, idx] obj1.outfile.bed.select.3 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.bed.select.1, obj1.bed.select.3) select[is.na(select)] <- 0 obj1.outfile.bgen.noselect.3 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) outfile = obj1.outfile.bgen.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.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.bed.noselect.1, obj1.bed.noselect.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, select = select, outfile = obj1.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj1.outfile.bgen.noselect.3) quietly = TRUE)) { obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1.outfile.gds.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) select = select, outfile = obj1.outfile.bgen.select.3) obj1.outfile.gds.select.3 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj1.outfile.gds.noselect.3 <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.outfile.txt.select.3 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.txt.select.3 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.3) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj1.outfile.txt1.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj1.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, expect_equal(obj1.txt.select.1, obj1.txt.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) method.optim = "AI") obj1.outfile.txt2.select.3 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) "Allele2")) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.outfile.bed.select.3 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bed.select.1, obj2.bed.select.3) select[is.na(select)] <- 0 obj2.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) outfile = obj2.outfile.bgen.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bed.select.3 <- tempfile() obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, select = select, outfile = obj2.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.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.bgen.select.1, obj2.bgen.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj2.outfile.bgen.noselect.3) quietly = TRUE)) { obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.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.bgen.noselect.1, obj2.bgen.noselect.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj2.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.gds.select.3 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj2.outfile.gds.noselect.3 <- tempfile() } glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.outfile.txt.select.3 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) "Allele2")) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.3) expect_equal(obj2.txt.select.1, obj2.txt.select.3) } obj2.outfile.txt1.select.3 <- tempfile() obj2.outfile.txt.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, glmm.score(obj2, infile = txtfile, outfile = obj2.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")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt2.select.3 <- tempfile() obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.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")) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.txt2.select.3 <- tempfile() obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.1)) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, "Allele2")) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2)) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.2)) obj2.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.3)) obj1.outfile.txt2.select.2)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.3)) obj2.outfile.txt2.select.2)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, quietly = TRUE)) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj1.outfile.txt2.select.3)) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, }) obj2.outfile.txt2.select.3))
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 33: quietly = TRUE)) eval(code, test_env) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1,
obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, 34: obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, eval(code, test_env) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3,
obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))35: })withCallingHandlers({
eval(code, test_env)33: new_expectations <- the$test_expectations > starting_expectationseval(code, test_env) if (snapshot_skipped) {
skip("On CRAN")34: }eval(code, test_env) else if (!new_expectations && skip_on_empty) {
skip_empty()35: }withCallingHandlers({}, expectation = handle_expectation, packageNotFoundError = function(e) { eval(code, test_env) if (on_cran()) { new_expectations <- the$test_expectations > starting_expectations skip(paste0("{", e$package, "} is not installed.")) if (snapshot_skipped) { } skip("On CRAN")}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE else if (!new_expectations && skip_on_empty) { invokeRestart("muffle_cran_snapshot") skip_empty()}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {36: skip(paste0("{", e$package, "} is not installed."))doTryCatch(return(expr), name, parentenv, handler) }
}, snapshot_on_cran = function(cnd) {37: snapshot_skipped <<- TRUEtryCatchOne(expr, names, parentenv, handlers[[1L]]) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 38: error = handle_error, interrupt = handle_interrupt)tryCatchList(expr, classes, parentenv, handlers)
36: 39: doTryCatch(return(expr), name, parentenv, handler)tryCatch(withCallingHandlers({
eval(code, test_env)37: new_expectations <- the$test_expectations > starting_expectationstryCatchOne(expr, names, parentenv, handlers[[1L]]) if (snapshot_skipped) {
skip("On CRAN")38: }tryCatchList(expr, classes, parentenv, handlers) else if (!new_expectations && skip_on_empty) {
skip_empty()39: }tryCatch(withCallingHandlers({}, expectation = handle_expectation, packageNotFoundError = function(e) { eval(code, test_env) if (on_cran()) { new_expectations <- the$test_expectations > starting_expectations skip(paste0("{", e$package, "} is not installed.")) if (snapshot_skipped) { } skip("On CRAN")}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE else if (!new_expectations && skip_on_empty) { invokeRestart("muffle_cran_snapshot") skip_empty()}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {40: skip(paste0("{", e$package, "} is not installed."))doWithOneRestart(return(expr), restart) }
}, snapshot_on_cran = function(cnd) {41: snapshot_skipped <<- TRUEwithOneRestart(expr, restarts[[1L]]) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 42: error = handle_error, interrupt = handle_interrupt), error = handle_fatal)withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)40: new_expectations <- the$test_expectations > starting_expectationsdoWithOneRestart(return(expr), restart) if (snapshot_skipped) {
skip("On CRAN")41: }withOneRestart(expr, restarts[[1L]]) else if (!new_expectations && skip_on_empty) {
skip_empty()42: }withRestarts(tryCatch(withCallingHandlers({}, expectation = handle_expectation, packageNotFoundError = function(e) { eval(code, test_env) if (on_cran()) { new_expectations <- the$test_expectations > starting_expectations skip(paste0("{", e$package, "} is not installed.")) if (snapshot_skipped) { } skip("On CRAN")}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE else if (!new_expectations && skip_on_empty) { invokeRestart("muffle_cran_snapshot") skip_empty()}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal), }, expectation = handle_expectation, packageNotFoundError = function(e) { end_test = function() { if (on_cran()) { }) skip(paste0("{", e$package, "} is not installed."))
}43: }, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUEtest_code(code = exprs, env = env, reporter = get_reporter() %||% invokeRestart("muffle_cran_snapshot") StopReporter$new())}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 44: end_test = function() {source_file(path, env = env(env), desc = desc, shuffle = shuffle, }) error_call = error_call)
43: 45: test_code(code = exprs, env = env, reporter = get_reporter() %||% FUN(X[[i]], ...) StopReporter$new())
46: 44: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call) error_call = error_call)
47: 45: doTryCatch(return(expr), name, parentenv, handler)FUN(X[[i]], ...)
48: 46: tryCatchOne(expr, names, parentenv, handlers[[1L]])lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,
error_call = error_call)49:
tryCatchList(expr, classes, parentenv, handlers)47:
doTryCatch(return(expr), name, parentenv, handler)50:
tryCatch(code, testthat_abort_reporter = function(cnd) {48: cat(conditionMessage(cnd), "\n")tryCatchOne(expr, names, parentenv, handlers[[1L]]) NULL
})49:
tryCatchList(expr, classes, parentenv, handlers)51:
with_reporter(reporters$multi, lapply(test_paths, test_one_file, 50: env = env, desc = desc, shuffle = shuffle, error_call = error_call))tryCatch(code, testthat_abort_reporter = function(cnd) {
cat(conditionMessage(cnd), "\n")52: NULLtest_files_serial(test_dir = test_dir, test_package = test_package, }) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 51: desc = desc, load_package = load_package, shuffle = shuffle, with_reporter(reporters$multi, lapply(test_paths, test_one_file, error_call = error_call) env = env, desc = desc, shuffle = shuffle, error_call = error_call))
53: 52: test_files(test_dir = path, test_paths = test_paths, test_package = package, test_files_serial(test_dir = test_dir, test_package = test_package, reporter = reporter, load_helpers = load_helpers, env = env, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, 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, load_package = load_package, parallel = parallel, shuffle = shuffle) desc = desc, load_package = load_package, shuffle = shuffle,
error_call = error_call)54:
test_dir("testthat", package = package, reporter = reporter, 53: ..., load_package = "installed")test_files(test_dir = path, test_paths = test_paths, test_package = package,
reporter = reporter, load_helpers = load_helpers, env = env, 55: stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, test_check("GMMAT") load_package = load_package, parallel = parallel, shuffle = shuffle)
An irrecoverable exception occurred. R is aborting now ...
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