Last updated on 2026-11-01 19:53:41 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.44 | 234.31 | 279.75 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.34 | 215.07 | 253.41 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 158.82 | 187.82 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 165.37 | 207.37 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 52.77 | 231.81 | 284.58 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 54.79 | 229.43 | 284.22 | 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 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 79.00 | 96.00 | 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 | 382.00 | 471.00 | 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:
Traceback:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) 1:
eval(c.expr, envir = args, enclos = envir) 7:
FUN(X[[i]], ...) 2:
eval(c.expr, envir = args, enclos = envir) 8:
lapply(X = S, FUN = FUN, ...) 3:
doTryCatch(return(expr), name, parentenv, handler) 9:
doTryCatch(return(expr), name, parentenv, handler) 4:
tryCatchOne(expr, names, parentenv, handlers[[1L]])10:
tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: 11: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
6: 12: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)tryCatch(expr, error = function(e) {
call <- conditionCall(e) 7: if (!is.null(call)) {FUN(X[[i]], ...) if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L) 8: dcall <- deparse(call, nlines = 1L)lapply(X = S, FUN = FUN, ...) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L 9: doTryCatch(return(expr), name, parentenv, handler) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")10: if (is.na(w)) tryCatchOne(expr, names, parentenv, handlers[[1L]]) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")11: if (w > LONG) tryCatchList(expr, classes, parentenv, handlers) prefix <- paste0(prefix, "\n ")
}12: else prefix <- "Error : "tryCatch(expr, error = function(e) { msg <- paste0(prefix, conditionMessage(e), "\n") call <- conditionCall(e) .Internal(seterrmessage(msg[1L])) if (!is.null(call)) { if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) .Internal(printDeferredWarnings()) dcall <- deparse(call, nlines = 1L) } prefix <- paste("Error in", dcall, ": ") invisible(structure(msg, class = "try-error", condition = e)) LONG <- 75L}) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")13: if (is.na(w)) try(lapply(X = S, FUN = FUN, ...), silent = TRUE) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")14: if (w > LONG) sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) prefix <- paste0(prefix, "\n ")
}15: else prefix <- "Error : "FUN(X[[i]], ...) msg <- paste0(prefix, conditionMessage(e), "\n")
16: .Internal(seterrmessage(msg[1L]))lapply(seq_len(cores), inner.do) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)17: .Internal(printDeferredWarnings())mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, } mc.silent = silent, mc.cores = cores)
invisible(structure(msg, class = "try-error", condition = e))18: })e$fun(obj, substitute(ex), parent.frame(), e$data)
13: 19: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) {14: if (bgenInfo$LayoutFlag == 2) {sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 15: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, FUN(X[[i]], ...) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 16: 1)lapply(seq_len(cores), inner.do) }
else {17: .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, infile, paste0(outfile, "_tmp.", i), center2, mc.silent = silent, mc.cores = cores) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 18: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, e$fun(obj, substitute(ex), parent.frame(), e$data) 1)
}19: }foreach(i = 1:ncores) %dopar% { else { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, obj$Sigma_iX, obj$cov, infile, paste0(outfile, infile, paste0(outfile, "_tmp.", i), center2, "_tmp.", i), center2, MAF.range[1], MAF.range[2], MAF.range[1], MAF.range[2], miss.cutoff, miss.method, miss.cutoff, miss.method, nperbatch, select, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, bgenInfo$N, bgenInfo$CompressionFlag, 1) 1) } } else { else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, obj$Sigma_iX, obj$cov, infile, paste0(outfile, infile, paste0(outfile, "_tmp.", i), center2, "_tmp.", i), center2, MAF.range[1], MAF.range[2], MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], miss.cutoff, miss.method, nperbatch, select, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 1) bgenInfo$N, bgenInfo$CompressionFlag, 1) } } } } else {} if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, 20: obj$Sigma_iX, obj$cov, infile, paste0(outfile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 21: bgenInfo$N, bgenInfo$CompressionFlag, 1)eval(code, test_env) }
else {22: .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, eval(code, test_env) obj$Sigma_iX, obj$cov, infile, paste0(outfile,
"_tmp.", i), center2, MAF.range[1], MAF.range[2], 23: miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], withCallingHandlers({ bgenInfo$N, bgenInfo$CompressionFlag, 1) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) {} skip("On CRAN")
}20: else if (!new_expectations && skip_on_empty) {glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip_empty() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {21: if (on_cran()) {eval(code, test_env) skip(paste0("{", e$package, "} is not installed."))
}22: }, snapshot_on_cran = function(cnd) {eval(code, test_env) snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 23: error = handle_error, interrupt = handle_interrupt)withCallingHandlers({
eval(code, test_env)24: doTryCatch(return(expr), name, parentenv, handler) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {25: skip("On CRAN")tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
else if (!new_expectations && skip_on_empty) {26: skip_empty()tryCatchList(expr, classes, parentenv, handlers) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {27: if (on_cran()) {tryCatch(withCallingHandlers({ skip(paste0("{", e$package, "} is not installed.")) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) {}, snapshot_on_cran = function(cnd) { skip("On CRAN") snapshot_skipped <<- TRUE } invokeRestart("muffle_cran_snapshot") else if (!new_expectations && skip_on_empty) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip_empty() error = handle_error, interrupt = handle_interrupt) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {24: if (on_cran()) {doTryCatch(return(expr), name, parentenv, handler) skip(paste0("{", e$package, "} is not installed."))
}25: }, snapshot_on_cran = function(cnd) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")26: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatchList(expr, classes, parentenv, handlers) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
27: 28: tryCatch(withCallingHandlers({ eval(code, test_env)doWithOneRestart(return(expr), restart) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {29: skip("On CRAN")withOneRestart(expr, restarts[[1L]]) }
else if (!new_expectations && skip_on_empty) {30: skip_empty()withRestarts(tryCatch(withCallingHandlers({ } eval(code, test_env)}, expectation = handle_expectation, packageNotFoundError = function(e) { new_expectations <- the$test_expectations > starting_expectations if (on_cran()) { if (snapshot_skipped) { skip(paste0("{", e$package, "} is not installed.")) skip("On CRAN") } }}, snapshot_on_cran = function(cnd) { else if (!new_expectations && skip_on_empty) { snapshot_skipped <<- TRUE skip_empty() invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, }, expectation = handle_expectation, packageNotFoundError = function(e) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))28: }doWithOneRestart(return(expr), restart)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE29: invokeRestart("muffle_cran_snapshot")withOneRestart(expr, restarts[[1L]])}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 30: end_test = function() {withRestarts(tryCatch(withCallingHandlers({ }) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations31: if (snapshot_skipped) {test_code(code, parent.frame()) skip("On CRAN")
} else if (!new_expectations && skip_on_empty) {32: skip_empty()test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", }, expectation = handle_expectation, packageNotFoundError = function(e) { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] if (on_cran()) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") skip(paste0("{", e$package, "} is not installed.")) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") } gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")}, snapshot_on_cran = function(cnd) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") snapshot_skipped <<- TRUE txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") invokeRestart("muffle_cran_snapshot") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")}, skip = handle_skip, warning = handle_warning, message = handle_message, data(example) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), suppressWarnings(RNGversion("3.5.0")) end_test = function() { set.seed(123) }) pheno <- rbind(example$pheno, example$pheno[1:100, ])
pheno$id <- 1:50031: pheno$disease[sample(1:500, 20)] <- NAtest_code(code, parent.frame()) pheno$age[sample(1:500, 20)] <- NA
pheno$sex[sample(1:500, 20)] <- NA32: pheno <- pheno[sample(1:500, 450), ]test_that("cross-sectional id le 400 binomial", { pheno <- pheno[pheno$id <= 400, ] plinkfiles <- strsplit(system.file("extdata", "geno.bed", kins <- example$GRM package = "GMMAT"), ".bed", fixed = TRUE)[[1]] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") id = "id", family = binomial(link = "logit"), method = "REML", samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") method.optim = "AI") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") select <- match(1:400, unique(obj1$id_include)) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") select[is.na(select)] <- 0 txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj1.outfile.bed.noselect.1 <- tempfile() txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) data(example) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, suppressWarnings(RNGversion("3.5.0")) header = TRUE, as.is = TRUE) set.seed(123) obj1.outfile.bed.noselect.1.tmp <- tempfile() pheno <- rbind(example$pheno, example$pheno[1:100, ]) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, pheno$id <- 1:500 ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") pheno$disease[sample(1:500, 20)] <- NA unlink(obj1.outfile.bed.noselect.1.tmp) pheno$age[sample(1:500, 20)] <- NA obj1.outfile.bed.select.1 <- tempfile() pheno$sex[sample(1:500, 20)] <- NA glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) pheno <- pheno[sample(1:500, 450), ] obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, pheno <- pheno[pheno$id <= 400, ] header = TRUE, as.is = TRUE) kins <- example$GRM expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.bgen.noselect.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, method.optim = "AI") outfile = obj1.outfile.bgen.noselect.1) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.bed.noselect.1.tmp <- tempfile() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bed.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, select = select, outfile = obj1.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", outfile = obj1.outfile.bgen.noselect.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) obj1.outfile.bgen.noselect.1.tmp <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.gds.noselect.1 <- tempfile() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 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.bgen.noselect.1, obj1.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, ncores = 2) select = select, outfile = obj1.outfile.bgen.select.1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) 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.noselect.1, obj1.bgen.select.1) obj1.outfile.gds.select.1 <- tempfile() expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) quietly = TRUE)) { expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.outfile.gds.noselect.1 <- tempfile() 0.986534857))) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, ncores = 2) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.gds.noselect.1.tmp) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.gds.select.1 <- tempfile() obj1.outfile.txt.select.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) unlink(obj1.outfile.txt.select.1.tmp) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.outfile.txt1.select.1 <- tempfile() 0.986534857))) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(obj1.outfile.txt.select.1.tmp) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.txt1.select.1 <- tempfile() obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.1)) select = select, infile.header.print = c("SNP", "Allele1", skip_on_cran() "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.outfile.bed.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj2.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.txt2.select.1)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) skip_on_cran() obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bgen.select.1 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj2$id_include)) select = select, outfile = obj2.outfile.bgen.select.1) select[is.na(select)] <- 0 obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.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(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.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[, obj2.outfile.bed.select.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) "VAR", "PVAL")]) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.bgen.noselect.1 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.1) obj2.outfile.gds.select.1 <- tempfile() obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) } obj2.outfile.txt.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", select = select, infile.header.print = c("SNP", "Allele1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "Allele2")) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, "VAR", "PVAL")]) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) quietly = TRUE)) { obj2.outfile.txt1.select.1 <- tempfile() obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, "Allele2")) 0.996996766))) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, idx <- sample(nrow(pheno)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- pheno[idx, ] select = select, infile.header.print = c("SNP", "Allele1", 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.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj1.bed.select.1, obj1.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj1.outfile.bgen.noselect.2) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) idx <- sample(nrow(pheno)) obj1.outfile.bgen.select.2 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select = select, outfile = obj1.outfile.bgen.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) select[is.na(select)] <- 0 if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bed.noselect.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.outfile.gds.noselect.2 <- tempfile() obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, 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, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.outfile.gds.select.2 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, 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, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt.select.2 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.noselect.2) "Allele2")) obj1.outfile.bgen.select.2 <- tempfile() obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.2) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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", obj1.outfile.gds.noselect.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.txt2.select.2 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") "Allele2")) select <- match(1:400, unique(obj2$id_include)) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.outfile.txt1.select.2 <- tempfile() obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.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(obj2.bed.noselect.1, obj2.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.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(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj2.outfile.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select = select, outfile = obj2.outfile.bgen.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select[is.na(select)] <- 0 if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bed.noselect.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.outfile.gds.noselect.2 <- tempfile() obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.2 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.outfile.gds.select.2 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.txt.select.2 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.txt2.select.2 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) "Allele2")) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) idx <- sample(nrow(kins)) } kins <- kins[idx, idx] obj2.outfile.txt.select.2 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, 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(obj1$id_include)) "Allele2")) select[is.na(select)] <- 0 obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, 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.2 <- read.table(obj2.outfile.txt2.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, idx <- sample(nrow(kins)) select = select, outfile = obj1.outfile.bgen.select.3) kins <- kins[idx, idx] obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) id = "id", family = binomial(link = "logit"), method = "REML", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", method.optim = "AI") quietly = TRUE)) { select <- match(1:400, unique(obj1$id_include)) obj1.outfile.gds.noselect.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.outfile.bed.noselect.3 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 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(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.bed.select.3 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.txt.select.3 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, 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) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() 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.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", quietly = TRUE)) { "Allele2")) obj1.outfile.gds.noselect.3 <- tempfile() obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) "Allele2")) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, expect_equal(obj1.gds.select.1, obj1.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.outfile.txt.select.3 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, 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.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.outfile.txt1.select.3 <- tempfile() obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) "Allele2")) obj2.outfile.bed.select.3 <- tempfile() obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.bed.select.3 <- read.table(obj2.outfile.bed.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(obj2.bed.select.1, obj2.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj2.outfile.bgen.noselect.3) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.bgen.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) method.optim = "AI") obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.gds.noselect.3 <- tempfile() obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.gds.select.3 <- tempfile() obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, outfile = obj2.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, expect_equal(obj2.gds.select.1, obj2.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.txt.select.3 <- tempfile() obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.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.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt.select.1, obj2.txt.select.3) quietly = TRUE)) { obj2.outfile.txt1.select.3 <- tempfile() obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.outfile.txt2.select.3 <- tempfile() obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, expect_equal(obj2.gds.select.1, obj2.gds.select.3) } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) "Allele2")) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.1)) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt1.select.3 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.2)) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, "Allele2")) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2.outfile.txt2.select.3 <- tempfile() obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.3)) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, quietly = TRUE)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.outfile.txt2.select.1))}) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2,
obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, 33: obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2))eval(code, test_env) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,
obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, 34: obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, eval(code, test_env) obj2.outfile.txt2.select.2))
unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, 35: obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, withCallingHandlers({ obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, if (snapshot_skipped) { obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, skip("On CRAN") obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, } obj2.outfile.txt2.select.3)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", else if (!new_expectations && skip_on_empty) { quietly = TRUE)) skip_empty() unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, } obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) }})}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE33: invokeRestart("muffle_cran_snapshot")eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt)34:
eval(code, test_env)36:
35: doTryCatch(return(expr), name, parentenv, handler)withCallingHandlers({
eval(code, test_env)37: new_expectations <- the$test_expectations > starting_expectationstryCatchOne(expr, names, parentenv, handlers[[1L]]) if (snapshot_skipped) { skip("On CRAN")
}38: else if (!new_expectations && skip_on_empty) {tryCatchList(expr, classes, parentenv, handlers) skip_empty()
}39: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatch(withCallingHandlers({ if (on_cran()) { eval(code, test_env) skip(paste0("{", e$package, "} is not installed.")) } new_expectations <- the$test_expectations > starting_expectations}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE if (snapshot_skipped) { invokeRestart("muffle_cran_snapshot") skip("On CRAN")}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt) else if (!new_expectations && skip_on_empty) {
skip_empty()36: }doTryCatch(return(expr), name, parentenv, handler)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {37: skip(paste0("{", e$package, "} is not installed."))tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
}, snapshot_on_cran = function(cnd) {38: snapshot_skipped <<- TRUEtryCatchList(expr, classes, parentenv, handlers) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 39: error = handle_error, interrupt = handle_interrupt), error = handle_fatal)tryCatch(withCallingHandlers({
eval(code, test_env)40: new_expectations <- the$test_expectations > starting_expectationsdoWithOneRestart(return(expr), restart) if (snapshot_skipped) {
skip("On CRAN")41: } else if (!new_expectations && skip_on_empty) {withOneRestart(expr, restarts[[1L]]) skip_empty() }
}, expectation = handle_expectation, packageNotFoundError = function(e) {42: if (on_cran()) {withRestarts(tryCatch(withCallingHandlers({ skip(paste0("{", e$package, "} is not installed.")) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations}, snapshot_on_cran = function(cnd) { if (snapshot_skipped) { snapshot_skipped <<- TRUE skip("On CRAN") invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, else if (!new_expectations && skip_on_empty) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal) skip_empty()
40: }doWithOneRestart(return(expr), restart)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {41: skip(paste0("{", e$package, "} is not installed."))withOneRestart(expr, restarts[[1L]]) }
}, snapshot_on_cran = function(cnd) {42: snapshot_skipped <<- TRUEwithRestarts(tryCatch(withCallingHandlers({ invokeRestart("muffle_cran_snapshot") eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { new_expectations <- the$test_expectations > starting_expectations }) if (snapshot_skipped) {
skip("On CRAN")43: }test_code(code = exprs, env = env, reporter = get_reporter() %||% else if (!new_expectations && skip_on_empty) { StopReporter$new()) skip_empty()
}44: }, expectation = handle_expectation, packageNotFoundError = function(e) {source_file(path, env = env(env), desc = desc, shuffle = shuffle, if (on_cran()) { error_call = error_call) skip(paste0("{", e$package, "} is not installed.")) }
}, snapshot_on_cran = function(cnd) {45: snapshot_skipped <<- TRUEFUN(X[[i]], ...) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 46: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, end_test = function() { error_call = error_call) })
47: 43: doTryCatch(return(expr), name, parentenv, handler)test_code(code = exprs, env = env, reporter = get_reporter() %||%
StopReporter$new())48:
tryCatchOne(expr, names, parentenv, handlers[[1L]])44:
source_file(path, env = env(env), desc = desc, shuffle = shuffle, 49: error_call = error_call)tryCatchList(expr, classes, parentenv, handlers)
45:
FUN(X[[i]], ...)50:
tryCatch(code, testthat_abort_reporter = function(cnd) {46: cat(conditionMessage(cnd), "\n")lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, NULL error_call = error_call)})
47: 51: doTryCatch(return(expr), name, parentenv, handler)with_reporter(reporters$multi, lapply(test_paths, test_one_file,
env = env, desc = desc, shuffle = shuffle, error_call = error_call))48:
tryCatchOne(expr, names, parentenv, handlers[[1L]])
52: 49: test_files_serial(test_dir = test_dir, test_package = test_package, tryCatchList(expr, classes, parentenv, handlers) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 50: desc = desc, load_package = load_package, shuffle = shuffle, tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") error_call = error_call) NULL
53: })test_files(test_dir = path, test_paths = test_paths, test_package = package,
reporter = reporter, load_helpers = load_helpers, env = env, 51: stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)with_reporter(reporters$multi, lapply(test_paths, test_one_file,
env = env, desc = desc, shuffle = shuffle, error_call = error_call))54:
test_dir("testthat", package = package, reporter = reporter, 52: ..., load_package = "installed")test_files_serial(test_dir = test_dir, test_package = test_package,
test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, 55: env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, test_check("GMMAT") desc = desc, load_package = load_package, shuffle = shuffle,
error_call = error_call)An irrecoverable exception occurred. R is aborting now ...
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