Last updated on 2026-10-31 22:53:13 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 | 51.51 | 238.45 | 289.96 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 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: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
Traceback:
1:
eval(c.expr, envir = args, enclos = envir) 9:
doTryCatch(return(expr), name, parentenv, handler) 2:
eval(c.expr, envir = args, enclos = envir)10:
tryCatchOne(expr, names, parentenv, handlers[[1L]]) 3:
doTryCatch(return(expr), name, parentenv, handler)11:
tryCatchList(expr, classes, parentenv, handlers) 4:
tryCatchOne(expr, names, parentenv, handlers[[1L]])12:
tryCatch(expr, error = function(e) { 5: call <- conditionCall(e)tryCatchList(expr, classes, parentenv, handlers) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 6: call <- sys.call(-4L)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) dcall <- deparse(call, nlines = 1L)
prefix <- paste("Error in", dcall, ": ") 7: LONG <- 75LFUN(X[[i]], ...) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") 8: if (is.na(w)) lapply(X = S, FUN = FUN, ...) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b") 9: if (w > LONG) doTryCatch(return(expr), name, parentenv, handler) prefix <- paste0(prefix, "\n ")
}10: else prefix <- "Error : "tryCatchOne(expr, names, parentenv, handlers[[1L]]) msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))11: if (!silent && isTRUE(getOption("show.error.messages"))) {tryCatchList(expr, classes, parentenv, handlers) cat(msg, file = outFile)
.Internal(printDeferredWarnings())12: }tryCatch(expr, error = function(e) { invisible(structure(msg, class = "try-error", condition = e)) call <- conditionCall(e)}) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 13: call <- sys.call(-4L)try(lapply(X = S, FUN = FUN, ...), silent = TRUE) dcall <- deparse(call, nlines = 1L)
prefix <- paste("Error in", dcall, ": ")14: LONG <- 75LsendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")15: if (is.na(w)) FUN(X[[i]], ...) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")16: if (w > LONG) lapply(seq_len(cores), inner.do) prefix <- paste0(prefix, "\n ")
}17: else prefix <- "Error : "mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, msg <- paste0(prefix, conditionMessage(e), "\n") mc.silent = silent, mc.cores = cores) .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {18: cat(msg, file = outFile)e$fun(obj, substitute(ex), parent.frame(), e$data)
.Internal(printDeferredWarnings())19: }foreach(i = 1:ncores) %dopar% { invisible(structure(msg, class = "try-error", condition = e)) if (!is.null(obj$P)) {}) if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13, as.numeric(res), obj$P, 13: infile, paste0(outfile, "_tmp.", i), center2, try(lapply(X = S, FUN = FUN, ...), silent = TRUE) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 14: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) 1)
}15: else {FUN(X[[i]], ...) .Call(C_glmm_score_bgen11, 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$end[i],
17: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, 1) mc.silent = silent, mc.cores = cores) }
}18: else {e$fun(obj, substitute(ex), parent.frame(), e$data) if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, 19: obj$Sigma_iX, obj$cov, infile, paste0(outfile, foreach(i = 1:ncores) %dopar% { "_tmp.", i), center2, MAF.range[1], MAF.range[2], if (!is.null(obj$P)) { miss.cutoff, miss.method, nperbatch, select, if (bgenInfo$LayoutFlag == 2) { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, bgenInfo$N, bgenInfo$CompressionFlag, 1) infile, paste0(outfile, "_tmp.", i), center2, } MAF.range[1], MAF.range[2], miss.cutoff, miss.method, else { nperbatch, select, threadInfo$begin[i], threadInfo$end[i], .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, obj$Sigma_iX, obj$cov, infile, paste0(outfile, 1) "_tmp.", i), center2, MAF.range[1], MAF.range[2], } miss.cutoff, miss.method, nperbatch, select, else { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, bgenInfo$N, bgenInfo$CompressionFlag, 1) infile, paste0(outfile, "_tmp.", i), center2, } MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], } threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,
1)20: } }glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, else { outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, 21: obj$Sigma_iX, obj$cov, infile, paste0(outfile, eval(code, test_env) "_tmp.", i), center2, MAF.range[1], MAF.range[2],
miss.cutoff, miss.method, nperbatch, select, 22: threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], eval(code, test_env) bgenInfo$N, bgenInfo$CompressionFlag, 1)
}23: else {withCallingHandlers({ .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, eval(code, test_env) obj$Sigma_iX, obj$cov, infile, paste0(outfile, new_expectations <- the$test_expectations > starting_expectations "_tmp.", i), center2, MAF.range[1], MAF.range[2], if (snapshot_skipped) { miss.cutoff, miss.method, nperbatch, select, skip("On CRAN") threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], } bgenInfo$N, bgenInfo$CompressionFlag, 1) else if (!new_expectations && skip_on_empty) { } skip_empty() } }}}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {20: skip(paste0("{", e$package, "} is not installed."))glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE21: invokeRestart("muffle_cran_snapshot")eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt)22:
eval(code, test_env)24:
doTryCatch(return(expr), name, parentenv, handler)23:
withCallingHandlers({25: eval(code, test_env)tryCatchOne(expr, names, parentenv, handlers[[1L]]) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {26: skip("On CRAN")tryCatchList(expr, classes, parentenv, handlers) }
else if (!new_expectations && skip_on_empty) {27: skip_empty()tryCatch(withCallingHandlers({ } eval(code, test_env)}, expectation = handle_expectation, packageNotFoundError = function(e) { new_expectations <- the$test_expectations > starting_expectations if (on_cran()) { if (snapshot_skipped) { skip(paste0("{", e$package, "} is not installed.")) skip("On CRAN") } }}, snapshot_on_cran = function(cnd) { else if (!new_expectations && skip_on_empty) { snapshot_skipped <<- TRUE skip_empty() invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, }, expectation = handle_expectation, packageNotFoundError = function(e) { error = handle_error, interrupt = handle_interrupt) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))24: }doTryCatch(return(expr), name, parentenv, handler)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE25: invokeRestart("muffle_cran_snapshot")tryCatchOne(expr, names, parentenv, handlers[[1L]])}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal)26:
tryCatchList(expr, classes, parentenv, handlers)28:
doWithOneRestart(return(expr), restart)27:
tryCatch(withCallingHandlers({29: eval(code, test_env)withOneRestart(expr, restarts[[1L]]) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) { skip("On CRAN")30: }withRestarts(tryCatch(withCallingHandlers({ else if (!new_expectations && skip_on_empty) { eval(code, test_env) skip_empty() new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) {}, expectation = handle_expectation, packageNotFoundError = function(e) { skip("On CRAN") if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) else if (!new_expectations && skip_on_empty) { } skip_empty()}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE}, expectation = handle_expectation, packageNotFoundError = function(e) { invokeRestart("muffle_cran_snapshot") if (on_cran()) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip(paste0("{", e$package, "} is not installed.")) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) }
}, snapshot_on_cran = function(cnd) {28: snapshot_skipped <<- TRUEdoWithOneRestart(return(expr), restart) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 29: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), withOneRestart(expr, restarts[[1L]]) end_test = function() {
})30:
withRestarts(tryCatch(withCallingHandlers({31: eval(code, test_env)test_code(code, parent.frame()) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {32: skip("On CRAN")test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", else if (!new_expectations && skip_on_empty) { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] skip_empty() bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") } samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")}, expectation = handle_expectation, packageNotFoundError = function(e) { gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") if (on_cran()) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") skip(paste0("{", e$package, "} is not installed.")) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") } txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")}, snapshot_on_cran = function(cnd) { data(example) snapshot_skipped <<- TRUE suppressWarnings(RNGversion("3.5.0")) invokeRestart("muffle_cran_snapshot") set.seed(123)}, skip = handle_skip, warning = handle_warning, message = handle_message, pheno <- rbind(example$pheno, example$pheno[1:100, ]) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), pheno$id <- 1:500 end_test = function() { pheno$disease[sample(1:500, 20)] <- NA }) pheno$age[sample(1:500, 20)] <- NA
pheno$sex[sample(1:500, 20)] <- NA31: pheno <- pheno[sample(1:500, 450), ]test_code(code, parent.frame()) pheno <- pheno[pheno$id <= 400, ]
kins <- example$GRM32: obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, test_that("cross-sectional id le 400 binomial", { id = "id", family = binomial(link = "logit"), method = "REML", plinkfiles <- strsplit(system.file("extdata", "geno.bed", method.optim = "AI") package = "GMMAT"), ".bed", fixed = TRUE)[[1]] select <- match(1:400, unique(obj1$id_include)) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") select[is.na(select)] <- 0 samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj1.outfile.bed.noselect.1 <- tempfile() gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj1.outfile.bed.noselect.1.tmp <- tempfile() data(example) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, suppressWarnings(RNGversion("3.5.0")) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") set.seed(123) unlink(obj1.outfile.bed.noselect.1.tmp) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.outfile.bed.select.1 <- tempfile() pheno$id <- 1:500 glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) pheno$disease[sample(1:500, 20)] <- NA obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) pheno$age[sample(1:500, 20)] <- NA expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) pheno$sex[sample(1:500, 20)] <- NA obj1.outfile.bgen.noselect.1 <- tempfile() pheno <- pheno[sample(1:500, 450), ] glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, pheno <- pheno[pheno$id <= 400, ] outfile = obj1.outfile.bgen.noselect.1) kins <- example$GRM obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bgen.noselect.1.tmp <- tempfile() method.optim = "AI") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj1$id_include)) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) select[is.na(select)] <- 0 obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj1.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.noselect.1.tmp <- tempfile() select = select, outfile = obj1.outfile.bgen.select.1) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.bed.noselect.1.tmp) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj1.outfile.bed.select.1 <- tempfile() expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bgen.noselect.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) select = select, outfile = obj1.outfile.bgen.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, 0.986534857))) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.outfile.gds.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.outfile.gds.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) ncores = 2) obj1.outfile.txt.select.1.tmp <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.gds.noselect.1.tmp) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.gds.select.1 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.outfile.txt1.select.1 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) "Allele2")) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, 0.986534857))) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) } obj1.outfile.txt.select.1 <- tempfile() obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1.outfile.txt.select.1.tmp <- tempfile() unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.1)) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") skip_on_cran() unlink(obj1.outfile.txt.select.1.tmp) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt1.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.outfile.bed.noselect.1 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) outfile = obj2.outfile.bgen.noselect.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj2.outfile.bgen.select.1 <- tempfile() obj1.outfile.txt2.select.1)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, skip_on_cran() select = select, outfile = obj2.outfile.bgen.select.1) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", select[is.na(select)] <- 0 "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.outfile.bed.noselect.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) "VAR", "PVAL")]) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { obj2.outfile.bed.select.1 <- tempfile() obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.gds.select.1 <- tempfile() obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, outfile = obj2.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.outfile.bgen.select.1 <- tempfile() expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, select = select, outfile = obj2.outfile.bgen.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) "Allele2")) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, header = TRUE, as.is = TRUE) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt1.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj2.outfile.gds.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(signif(range(obj2.gds.select.1$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, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, pheno <- pheno[idx, ] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.outfile.bed.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj1.bed.select.1, obj1.bed.select.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, idx <- sample(nrow(pheno)) outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, pheno <- pheno[idx, ] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bgen.select.2 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj1$id_include)) select = select, outfile = obj1.outfile.bgen.select.2) select[is.na(select)] <- 0 obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.gds.noselect.2 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.gds.select.2 <- tempfile() obj1.outfile.bgen.noselect.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, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, expect_equal(obj1.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.txt.select.2 <- tempfile() obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, outfile = obj1.outfile.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.2) quietly = TRUE)) { obj1.outfile.txt1.select.2 <- tempfile() obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj1.gds.select.1, obj1.gds.select.2) 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.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.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.txt2.select.1, obj1.txt2.select.2) "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, 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.txt.select.2) select[is.na(select)] <- 0 obj1.outfile.txt1.select.2 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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.txt1.select.2 <- read.table(obj1.outfile.txt1.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.txt1.select.1, obj1.txt1.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj2.outfile.bgen.noselect.2) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.bgen.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, method.optim = "AI") select = select, outfile = obj2.outfile.bgen.select.2) select <- match(1:400, unique(obj2$id_include)) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.bed.select.2 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.outfile.bgen.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj2.outfile.bgen.select.2) "Allele2")) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) expect_equal(obj2.txt.select.1, obj2.txt.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt1.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.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.txt1.select.2 <- read.table(obj2.outfile.txt1.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.txt1.select.1, obj2.txt1.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.outfile.txt2.select.2 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.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.gds.select.1, obj2.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj2.outfile.txt.select.2 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.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.txt2.select.1, obj2.txt2.select.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) idx <- sample(nrow(kins)) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, kins <- kins[idx, idx] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj2.txt.select.1, obj2.txt.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt1.select.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, select <- match(1:400, unique(obj1$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj2.outfile.txt2.select.2 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) outfile = obj1.outfile.bgen.noselect.3) idx <- sample(nrow(kins)) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, kins <- kins[idx, idx] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bgen.select.3 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj1$id_include)) select = select, outfile = obj1.outfile.bgen.select.3) select[is.na(select)] <- 0 obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.gds.noselect.3 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.3) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.3 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj1.outfile.bgen.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj1.outfile.bgen.select.3) "Allele2")) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) expect_equal(obj1.txt.select.1, obj1.txt.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt1.select.3 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj1.outfile.gds.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.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.txt1.select.1, obj1.txt1.select.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.txt2.select.3 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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(obj1.gds.select.1, obj1.gds.select.3) "Allele2")) } obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj1.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select = select, infile.header.print = c("SNP", "Allele1", id = "id", family = binomial(link = "logit"), method = "REML", "Allele2")) method.optim = "AI") obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.outfile.bed.noselect.3 <- tempfile() obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.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) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.3) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj2.outfile.bgen.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.3 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select = select, outfile = obj2.outfile.bgen.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, 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.3) select[is.na(select)] <- 0 if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bed.noselect.3 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.outfile.gds.noselect.3 <- tempfile() obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.3 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.gds.select.3 <- tempfile() obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj2.bed.select.1, obj2.bed.select.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj2.outfile.bgen.noselect.3) obj2.outfile.txt.select.3 <- tempfile() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 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.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt.select.3 <- read.table(obj2.outfile.txt.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.txt.select.1, obj2.txt.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, 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", obj2.outfile.gds.noselect.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.txt2.select.3 <- tempfile() obj2.outfile.gds.select.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, expect_equal(obj2.gds.select.1, obj2.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.outfile.txt.select.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.1)) "Allele2")) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj1.outfile.txt2.select.2)) obj2.outfile.txt1.select.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.2)) "Allele2")) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj1.outfile.txt2.select.3)) obj2.outfile.txt2.select.3 <- tempfile() 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, eval(code, test_env) obj1.outfile.txt2.select.2))
unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, 34: obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, eval(code, test_env) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2,
obj2.outfile.txt2.select.2))35: unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, withCallingHandlers({ obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, eval(code, test_env) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt2.select.3)) if (snapshot_skipped) { unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, skip("On CRAN") obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, } obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, else if (!new_expectations && skip_on_empty) { obj2.outfile.txt2.select.3)) skip_empty() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) }, expectation = handle_expectation, packageNotFoundError = function(e) { unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, if (on_cran()) { obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, } obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, }, snapshot_on_cran = function(cnd) { obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) snapshot_skipped <<- TRUE}) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 33: error = handle_error, interrupt = handle_interrupt)eval(code, test_env)
36: 34: eval(code, test_env)doTryCatch(return(expr), name, parentenv, handler)
35: 37: withCallingHandlers({tryCatchOne(expr, names, parentenv, handlers[[1L]])
eval(code, test_env)38: new_expectations <- the$test_expectations > starting_expectationstryCatchList(expr, classes, parentenv, handlers) if (snapshot_skipped) {
skip("On CRAN")39: }tryCatch(withCallingHandlers({ else if (!new_expectations && skip_on_empty) { eval(code, test_env) skip_empty() new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) {}, expectation = handle_expectation, packageNotFoundError = function(e) { skip("On CRAN") if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) else if (!new_expectations && skip_on_empty) { } skip_empty()}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE}, expectation = handle_expectation, packageNotFoundError = function(e) { invokeRestart("muffle_cran_snapshot") if (on_cran()) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip(paste0("{", e$package, "} is not installed.")) error = handle_error, interrupt = handle_interrupt) }
}, snapshot_on_cran = function(cnd) {36: snapshot_skipped <<- TRUEdoTryCatch(return(expr), name, parentenv, handler) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 37: error = handle_error, interrupt = handle_interrupt), error = handle_fatal)tryCatchOne(expr, names, parentenv, handlers[[1L]])
40: 38: doWithOneRestart(return(expr), restart)tryCatchList(expr, classes, parentenv, handlers)
41: 39: withOneRestart(expr, restarts[[1L]])tryCatch(withCallingHandlers({
eval(code, test_env)42: new_expectations <- the$test_expectations > starting_expectationswithRestarts(tryCatch(withCallingHandlers({ if (snapshot_skipped) { eval(code, test_env) skip("On CRAN") new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) { else if (!new_expectations && skip_on_empty) { skip("On CRAN") skip_empty() } } else if (!new_expectations && skip_on_empty) {}, expectation = handle_expectation, packageNotFoundError = function(e) { skip_empty() if (on_cran()) { } skip(paste0("{", e$package, "} is not installed."))}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) {}, snapshot_on_cran = function(cnd) { skip(paste0("{", e$package, "} is not installed.")) snapshot_skipped <<- TRUE } invokeRestart("muffle_cran_snapshot")}, snapshot_on_cran = function(cnd) {}, skip = handle_skip, warning = handle_warning, message = handle_message, snapshot_skipped <<- TRUE error = handle_error, interrupt = handle_interrupt), error = handle_fatal) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 40: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), doWithOneRestart(return(expr), restart) end_test = function() {
})41:
withOneRestart(expr, restarts[[1L]])43:
test_code(code = exprs, env = env, reporter = get_reporter() %||% 42: StopReporter$new())withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)44: new_expectations <- the$test_expectations > starting_expectationssource_file(path, env = env(env), desc = desc, shuffle = shuffle, if (snapshot_skipped) { error_call = error_call) skip("On CRAN")
}45: else if (!new_expectations && skip_on_empty) {FUN(X[[i]], ...) skip_empty()
}46: }, expectation = handle_expectation, packageNotFoundError = function(e) {lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, if (on_cran()) { error_call = error_call) skip(paste0("{", e$package, "} is not installed."))
}47: }, snapshot_on_cran = function(cnd) {doTryCatch(return(expr), name, parentenv, handler) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")48: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatchOne(expr, names, parentenv, handlers[[1L]]) error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
end_test = function() {49: })tryCatchList(expr, classes, parentenv, handlers)
43: 50: test_code(code = exprs, env = env, reporter = get_reporter() %||% tryCatch(code, testthat_abort_reporter = function(cnd) { StopReporter$new()) cat(conditionMessage(cnd), "\n")
NULL44: })source_file(path, env = env(env), desc = desc, shuffle = shuffle,
error_call = error_call)51:
with_reporter(reporters$multi, lapply(test_paths, test_one_file, 45: env = env, desc = desc, shuffle = shuffle, error_call = error_call))FUN(X[[i]], ...)
52: 46: test_files_serial(test_dir = test_dir, test_package = test_package, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, error_call = error_call) env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,
desc = desc, load_package = load_package, shuffle = shuffle, 47: error_call = error_call)doTryCatch(return(expr), name, parentenv, handler)
53: 48: test_files(test_dir = path, test_paths = test_paths, test_package = package, tryCatchOne(expr, names, parentenv, handlers[[1L]]) reporter = reporter, load_helpers = load_helpers, env = env,
stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 49: load_package = load_package, parallel = parallel, shuffle = shuffle)tryCatchList(expr, classes, parentenv, handlers)
54: 50: test_dir("testthat", package = package, reporter = reporter, tryCatch(code, testthat_abort_reporter = function(cnd) { ..., load_package = "installed") cat(conditionMessage(cnd), "\n")
NULL55: })test_check("GMMAT")
51: An irrecoverable exception occurred. R is aborting now ...
with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64