Last updated on 2026-10-25 00:50:57 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 44.27 | 238.86 | 283.13 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.46 | 212.30 | 250.76 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 154.08 | 184.08 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 163.82 | 205.82 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 392.00 | 462.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.18 | 226.93 | 278.11 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 52.75 | 232.73 | 285.48 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 67.00 | 310.00 | 377.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 18.00 | 70.00 | 88.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 88.00 | 402.00 | 490.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
Traceback:
1: 5: tryCatchList(expr, classes, parentenv, handlers)eval(c.expr, envir = args, enclos = envir)
6: 2: eval(c.expr, envir = args, enclos = envir)
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
3: doTryCatch(return(expr), name, parentenv, handler) 7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...) 4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
9:
5: doTryCatch(return(expr), name, parentenv, handler)
tryCatchList(expr, classes, parentenv, handlers)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
11: 7: FUN(X[[i]], ...)tryCatchList(expr, classes, parentenv, handlers)
8:
12: lapply(X = S, FUN = FUN, ...)
tryCatch(expr, error = function(e) { 9: doTryCatch(return(expr), name, parentenv, handler) call <- conditionCall(e)
10: if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) tryCatchOne(expr, names, parentenv, handlers[[1L]])
call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L)11: tryCatchList(expr, classes, parentenv, handlers) prefix <- paste("Error in", dcall, ": ") LONG <- 75L
12: sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")tryCatch(expr, error = function(e) { if (is.na(w)) call <- conditionCall(e) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], if (!is.null(call)) { type = "b") if (identical(call[[1L]], quote(doTryCatch))) if (w > LONG) call <- sys.call(-4L) prefix <- paste0(prefix, "\n ") dcall <- deparse(call, nlines = 1L) } prefix <- paste("Error in", dcall, ": ") else prefix <- "Error : " LONG <- 75L msg <- paste0(prefix, conditionMessage(e), "\n") sm <- strsplit(conditionMessage(e), "\n")[[1L]] .Internal(seterrmessage(msg[1L])) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (!silent && isTRUE(getOption("show.error.messages"))) { if (is.na(w)) cat(msg, file = outFile) .Internal(printDeferredWarnings()) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b") invisible(structure(msg, class = "try-error", condition = e)) if (w > LONG) }) prefix <- paste0(prefix, "\n ")
}13: else prefix <- "Error : "try(lapply(X = S, FUN = FUN, ...), silent = TRUE) msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))14: if (!silent && isTRUE(getOption("show.error.messages"))) {sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) cat(msg, file = outFile)
.Internal(printDeferredWarnings())15: }FUN(X[[i]], ...) invisible(structure(msg, class = "try-error", condition = e))
})
16: 13: lapply(seq_len(cores), inner.do)try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: 14: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) mc.silent = silent, mc.cores = cores)
18: 15: e$fun(obj, substitute(ex), parent.frame(), e$data)FUN(X[[i]], ...)
19: 16: foreach(i = 1:ncores) %dopar% {lapply(seq_len(cores), inner.do) if (!is.null(obj$P)) {
if (bgenInfo$LayoutFlag == 2) {17: .Call(C_glmm_score_bgen13, 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) }
else {19: .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, foreach(i = 1:ncores) %dopar% { infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, if (!is.null(obj$P)) { nperbatch, select, threadInfo$begin[i], threadInfo$end[i], if (bgenInfo$LayoutFlag == 2) { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, 1) infile, paste0(outfile, "_tmp.", i), center2, } MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], else { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, if (bgenInfo$LayoutFlag == 2) { 1) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, } obj$Sigma_iX, obj$cov, infile, paste0(outfile, else { "_tmp.", i), center2, MAF.range[1], MAF.range[2], .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, miss.cutoff, miss.method, nperbatch, select, infile, paste0(outfile, "_tmp.", i), center2, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], MAF.range[1], MAF.range[2], miss.cutoff, miss.method, bgenInfo$N, bgenInfo$CompressionFlag, 1) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], } else { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, 1) } obj$Sigma_iX, obj$cov, infile, paste0(outfile, } "_tmp.", i), center2, MAF.range[1], MAF.range[2], else { miss.cutoff, miss.method, nperbatch, select, if (bgenInfo$LayoutFlag == 2) { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, bgenInfo$N, bgenInfo$CompressionFlag, 1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, } "_tmp.", i), center2, MAF.range[1], MAF.range[2], } miss.cutoff, miss.method, nperbatch, select, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],
bgenInfo$N, bgenInfo$CompressionFlag, 1)20: }glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, else { outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 21: "_tmp.", i), center2, MAF.range[1], MAF.range[2], eval(code, test_env) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 22: eval(code, test_env) bgenInfo$N, bgenInfo$CompressionFlag, 1)
}23: }withCallingHandlers({} eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations20: if (snapshot_skipped) {glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip("On CRAN") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) }
else if (!new_expectations && skip_on_empty) {21: skip_empty()eval(code, test_env) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {22: if (on_cran()) {eval(code, test_env) skip(paste0("{", e$package, "} is not installed."))
}23: }, snapshot_on_cran = function(cnd) {withCallingHandlers({ snapshot_skipped <<- TRUE eval(code, test_env) invokeRestart("muffle_cran_snapshot") new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip("On CRAN") error = handle_error, interrupt = handle_interrupt) }
else if (!new_expectations && skip_on_empty) {24: skip_empty()doTryCatch(return(expr), name, parentenv, handler)
25: tryCatchOne(expr, names, parentenv, handlers[[1L]]) }}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {26: skip(paste0("{", e$package, "} is not installed.")) }tryCatchList(expr, classes, parentenv, handlers)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE27: invokeRestart("muffle_cran_snapshot")tryCatch(withCallingHandlers({}, skip = handle_skip, warning = handle_warning, message = handle_message, eval(code, test_env) error = handle_error, interrupt = handle_interrupt) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {24: skip("On CRAN")doTryCatch(return(expr), name, parentenv, handler) }
else if (!new_expectations && skip_on_empty) {25: skip_empty() }tryCatchOne(expr, names, parentenv, handlers[[1L]])}, expectation = handle_expectation, packageNotFoundError = function(e) {
26: if (on_cran()) {tryCatchList(expr, classes, parentenv, handlers) skip(paste0("{", e$package, "} is not installed."))
}}, snapshot_on_cran = function(cnd) {27: snapshot_skipped <<- TRUEtryCatch(withCallingHandlers({ invokeRestart("muffle_cran_snapshot") eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message, new_expectations <- the$test_expectations > starting_expectations error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
if (snapshot_skipped) {28: skip("On CRAN")doWithOneRestart(return(expr), restart)
}29: else if (!new_expectations && skip_on_empty) { skip_empty()withOneRestart(expr, restarts[[1L]]) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {30: if (on_cran()) {withRestarts(tryCatch(withCallingHandlers({ skip(paste0("{", e$package, "} is not installed.")) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) {}, snapshot_on_cran = function(cnd) { skip("On CRAN") snapshot_skipped <<- TRUE } else if (!new_expectations && skip_on_empty) { invokeRestart("muffle_cran_snapshot") skip_empty() }}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) {
skip(paste0("{", e$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$test_expectations > starting_expectations31: if (snapshot_skipped) {test_code(code, parent.frame()) skip("On CRAN")
}32: else if (!new_expectations && skip_on_empty) {test_that("cross-sectional id le 400 binomial", { skip_empty() plinkfiles <- strsplit(system.file("extdata", "geno.bed", } package = "GMMAT"), ".bed", fixed = TRUE)[[1]]}, expectation = handle_expectation, packageNotFoundError = function(e) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") if (on_cran()) { samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") skip(paste0("{", e$package, "} is not installed.")) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") } txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")}, snapshot_on_cran = function(cnd) { txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") snapshot_skipped <<- TRUE txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") invokeRestart("muffle_cran_snapshot")}, 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$id_include)) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") select[is.na(select)] <- 0 txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj1.outfile.bed.noselect.1 <- tempfile() txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) data(example) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) suppressWarnings(RNGversion("3.5.0")) obj1.outfile.bed.noselect.1.tmp <- tempfile() set.seed(123) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, pheno <- rbind(example$pheno, example$pheno[1:100, ]) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") pheno$id <- 1:500 unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() pheno$disease[sample(1:500, 20)] <- NA glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) pheno$age[sample(1:500, 20)] <- NA obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, pheno$sex[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno <- pheno[sample(1:500, 450), ] expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) pheno <- pheno[pheno$id <= 400, ] obj1.outfile.bgen.noselect.1 <- tempfile() kins <- example$GRM glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, outfile = obj1.outfile.bgen.noselect.1) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) obj1.outfile.bgen.noselect.1.tmp <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.noselect.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.outfile.bed.noselect.1.tmp <- tempfile() unlink(obj1.outfile.bgen.noselect.1.tmp) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj1.outfile.bgen.select.1 <- tempfile() ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(obj1.outfile.bed.noselect.1.tmp) select = select, outfile = obj1.outfile.bgen.select.1) obj1.outfile.bed.select.1 <- tempfile() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) header = TRUE, as.is = TRUE) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.outfile.bgen.noselect.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "VAR", "PVAL")]) outfile = obj1.outfile.bgen.noselect.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1 <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 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.bgen.noselect.1, obj1.bgen.noselect.1.tmp) ncores = 2) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) 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$PVAL)), signif(c(0.003804942, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, 0.986534857))) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) "Allele2")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, ncores = 2) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.txt.select.1.tmp) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj1.outfile.gds.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.outfile.txt2.select.1 <- tempfile() 0.986534857))) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.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.txt.select.1, obj1.txt2.select.1) "Allele2")) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.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, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt2.select.1)) obj1.outfile.txt.select.1.tmp <- tempfile() skip_on_cran() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, 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"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select <- match(1:400, unique(obj2$id_include)) unlink(obj1.outfile.txt.select.1.tmp) select[is.na(select)] <- 0 obj1.outfile.txt1.select.1 <- tempfile() obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) obj2.outfile.bed.select.1 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) header = TRUE, as.is = TRUE) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.outfile.bgen.noselect.1 <- 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", outfile = obj2.outfile.bgen.noselect.1) "Allele2")) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", id = "id", family = binomial(link = "logit"), method = "REML", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, method.optim = "AI") c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", select <- match(1:400, unique(obj2$id_include)) "VAR", "PVAL")]) select[is.na(select)] <- 0 if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bed.noselect.1 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.outfile.gds.noselect.1 <- tempfile() obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.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) obj2.outfile.gds.select.1 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.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.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) outfile = obj2.outfile.bgen.noselect.1) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, 0.996996766))) header = TRUE, as.is = TRUE) } obj2.outfile.bgen.select.1 <- tempfile() obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, select = select, outfile = obj2.outfile.bgen.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.txt1.select.1 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.noselect.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.gds.select.1 <- tempfile() obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) } idx <- sample(nrow(pheno)) obj2.outfile.txt.select.1 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 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(obj1$id_include)) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) "Allele2")) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, outfile = obj1.outfile.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) select = select, outfile = obj1.outfile.bgen.select.2) idx <- sample(nrow(pheno)) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, pheno <- pheno[idx, ] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) 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.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.bed.noselect.2 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.2 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.bed.select.2 <- tempfile() obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.bgen.noselect.2 <- 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", outfile = obj1.outfile.bgen.noselect.2) "Allele2")) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, select = select, outfile = obj1.outfile.bgen.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.gds.noselect.2 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.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.txt2.select.1, obj1.txt2.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.gds.select.1, obj1.gds.select.2) method.optim = "AI") } select <- match(1:400, unique(obj2$id_include)) obj1.outfile.txt.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj2.outfile.bed.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.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.txt.select.1, obj1.txt.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(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.txt1.select.2 <- read.table(obj1.outfile.txt1.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.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.bgen.select.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, select = select, outfile = obj2.outfile.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.outfile.gds.noselect.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) select[is.na(select)] <- 0 obj2.outfile.gds.select.2 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) } obj2.outfile.bed.select.2 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.select.1, obj2.bed.select.2) "Allele2")) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj2.outfile.bgen.select.2) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.outfile.txt2.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) "Allele2")) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) idx <- sample(nrow(kins)) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, kins <- kins[idx, idx] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.gds.select.1, obj2.gds.select.2) method.optim = "AI") } select <- match(1:400, unique(obj1$id_include)) obj2.outfile.txt.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj1.outfile.bed.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.outfile.txt1.select.2 <- tempfile() obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, 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(obj1.bed.select.1, obj1.bed.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, 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, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) quietly = TRUE)) { idx <- sample(nrow(kins)) obj1.outfile.gds.noselect.3 <- tempfile() kins <- kins[idx, idx] glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) select <- match(1:400, unique(obj1$id_include)) obj1.outfile.gds.select.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.bed.noselect.3 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.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.select.1, obj1.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.txt.select.3 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, 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) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt.select.1, obj1.txt.select.3) outfile = obj1.outfile.bgen.noselect.3) obj1.outfile.txt1.select.3 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.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, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, select = select, outfile = obj1.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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", obj1.outfile.gds.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.gds.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) method.optim = "AI") obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.bed.noselect.3 <- tempfile() } glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.outfile.txt.select.3 <- tempfile() obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj1.outfile.txt1.select.3 <- tempfile() obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) select = select, outfile = obj2.outfile.bgen.select.3) obj1.outfile.txt2.select.3 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.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, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.outfile.gds.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) method.optim = "AI") obj2.outfile.gds.select.3 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) select[is.na(select)] <- 0 obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj2.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt.select.1, obj2.txt.select.3) outfile = obj2.outfile.bgen.noselect.3) obj2.outfile.txt1.select.3 <- tempfile() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.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.txt1.select.3 <- read.table(obj2.outfile.txt1.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.txt1.select.1, obj2.txt1.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.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.txt2.select.3 <- read.table(obj2.outfile.txt2.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.txt2.select.1, obj2.txt2.select.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.gds.select.3 <- tempfile() obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, expect_equal(obj2.gds.select.1, obj2.gds.select.3) } obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj2.outfile.txt.select.3 <- tempfile() obj1.outfile.txt2.select.2)) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, "Allele2")) obj2.outfile.txt2.select.2)) obj2.txt.select.3 <- read.table(obj2.outfile.txt.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.txt.select.1, obj2.txt.select.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2.outfile.txt1.select.3 <- tempfile() obj1.outfile.txt2.select.3)) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, "Allele2")) obj2.outfile.txt2.select.3)) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj2.outfile.txt2.select.3 <- tempfile() obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) "Allele2"))}) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,
header = TRUE, as.is = TRUE)33: expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)eval(code, test_env) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,
obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, 34: obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, eval(code, test_env) obj2.outfile.txt2.select.1))
unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, 35: obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, withCallingHandlers({ obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, eval(code, test_env) obj1.outfile.txt2.select.2)) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, if (snapshot_skipped) { obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, skip("On CRAN") obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, } obj2.outfile.txt2.select.2)) else if (!new_expectations && skip_on_empty) { unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, skip_empty() obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, } obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, if (on_cran()) { obj2.outfile.txt2.select.3)) skip(paste0("{", e$package, "} is not installed.")) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, snapshot_on_cran = function(cnd) { quietly = TRUE)) snapshot_skipped <<- TRUE unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, error = handle_error, interrupt = handle_interrupt) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3,
obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))36: })doTryCatch(return(expr), name, parentenv, handler)
33: 37: eval(code, test_env)tryCatchOne(expr, names, parentenv, handlers[[1L]])
34: 38: eval(code, test_env)tryCatchList(expr, classes, parentenv, handlers)
35: 39: withCallingHandlers({tryCatch(withCallingHandlers({ eval(code, test_env) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { if (snapshot_skipped) { skip("On CRAN") skip("On CRAN") } } else if (!new_expectations && skip_on_empty) { else if (!new_expectations && skip_on_empty) { skip_empty() skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) skip(paste0("{", e$package, "} is not installed.")) } }}, snapshot_on_cran = function(cnd) {}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
36: 40: doTryCatch(return(expr), name, parentenv, handler)doWithOneRestart(return(expr), restart)
37:
41: tryCatchOne(expr, names, parentenv, handlers[[1L]])withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({38: tryCatchList(expr, classes, parentenv, handlers) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations
39: if (snapshot_skipped) { skip("On CRAN")tryCatch(withCallingHandlers({ } eval(code, test_env) else if (!new_expectations && skip_on_empty) { new_expectations <- the$test_expectations > starting_expectations skip_empty() if (snapshot_skipped) { skip("On CRAN") } } else if (!new_expectations && skip_on_empty) {}, expectation = handle_expectation, packageNotFoundError = function(e) { skip_empty() if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE }}, snapshot_on_cran = function(cnd) { invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") end_test = function() { })}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% 40: doWithOneRestart(return(expr), restart) StopReporter$new())
41: 44: withOneRestart(expr, restarts[[1L]])
source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)42: withRestarts(tryCatch(withCallingHandlers({
45: eval(code, test_env) new_expectations <- the$test_expectations > starting_expectationsFUN(X[[i]], ...) if (snapshot_skipped) {
skip("On CRAN") }46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, else if (!new_expectations && skip_on_empty) { error_call = error_call) skip_empty()
}}, expectation = handle_expectation, packageNotFoundError = function(e) {47: if (on_cran()) {doTryCatch(return(expr), name, parentenv, handler)
skip(paste0("{", e$package, "} is not installed."))48: }}, snapshot_on_cran = function(cnd) {tryCatchOne(expr, names, parentenv, handlers[[1L]])
snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")49: tryCatchList(expr, classes, parentenv, handlers)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
50: end_test = function() { })tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n")
43: NULLtest_code(code = exprs, env = env, reporter = get_reporter() %||% }) StopReporter$new())
51: 44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) error_call = error_call)
45: FUN(X[[i]], ...)52: test_files_serial(test_dir = test_dir, test_package = test_package,
test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, 46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call) error_call = error_call)
53:
test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, 47: doTryCatch(return(expr), name, parentenv, handler) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
48:
54: tryCatchOne(expr, names, parentenv, handlers[[1L]])test_dir("testthat", package = package, reporter = reporter,
..., load_package = "installed")
55: 49: test_check("GMMAT")
tryCatchList(expr, classes, parentenv, handlers)
An irrecoverable exception occurred. R is aborting now ...
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64