Last updated on 2026-10-14 08:00:06 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.06 | 240.59 | 283.65 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.79 | 212.40 | 251.19 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 156.48 | 186.48 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 168.79 | 210.79 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.86 | 231.73 | 283.59 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 50.71 | 234.78 | 285.49 | 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 | 70.00 | 313.00 | 383.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 79.00 | 95.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 90.00 | 396.00 | 486.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
22: eval(code, test_env)
Traceback:
1: 23: eval(c.expr, envir = args, enclos = envir)withCallingHandlers({
eval(code, test_env) 2: new_expectations <- the$test_expectations > starting_expectationseval(c.expr, envir = args, enclos = envir) if (snapshot_skipped) {
skip("On CRAN") 3: } else if (!new_expectations && skip_on_empty) {doTryCatch(return(expr), name, parentenv, handler) skip_empty()
} 4: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed.")) 5: }tryCatchList(expr, classes, parentenv, handlers)}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot") 6: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) error = handle_error, interrupt = handle_interrupt)
7: 24: doTryCatch(return(expr), name, parentenv, handler)FUN(X[[i]], ...)
25: 8: lapply(X = S, FUN = FUN, ...)tryCatchOne(expr, names, parentenv, handlers[[1L]])
9: 26: doTryCatch(return(expr), name, parentenv, handler)tryCatchList(expr, classes, parentenv, handlers)
10: 27: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatch(withCallingHandlers({
eval(code, test_env)11: new_expectations <- the$test_expectations > starting_expectationstryCatchList(expr, classes, parentenv, handlers) if (snapshot_skipped) {
skip("On CRAN")12: }tryCatch(expr, error = function(e) { else if (!new_expectations && skip_on_empty) { call <- conditionCall(e) skip_empty() if (!is.null(call)) { } if (identical(call[[1L]], quote(doTryCatch))) }, expectation = handle_expectation, packageNotFoundError = function(e) { call <- sys.call(-4L) if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) dcall <- deparse(call, nlines = 1L) }}, snapshot_on_cran = function(cnd) { prefix <- paste("Error in", dcall, ": ") snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") LONG <- 75L}, skip = handle_skip, warning = handle_warning, message = handle_message, sm <- strsplit(conditionMessage(e), "\n")[[1L]] error = handle_error, interrupt = handle_interrupt), error = handle_fatal) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 28: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], doWithOneRestart(return(expr), restart) type = "b")
if (w > LONG) 29: prefix <- paste0(prefix, "\n ")withOneRestart(expr, restarts[[1L]]) }
else prefix <- "Error : "30: msg <- paste0(prefix, conditionMessage(e), "\n")withRestarts(tryCatch(withCallingHandlers({ .Internal(seterrmessage(msg[1L])) eval(code, test_env) if (!silent && isTRUE(getOption("show.error.messages"))) { new_expectations <- the$test_expectations > starting_expectations cat(msg, file = outFile) .Internal(printDeferredWarnings()) if (snapshot_skipped) { } skip("On CRAN") invisible(structure(msg, class = "try-error", condition = e)) }}) else if (!new_expectations && skip_on_empty) {
skip_empty()13: }try(lapply(X = S, FUN = FUN, ...), silent = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))14: }sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")15: }, skip = handle_skip, warning = handle_warning, message = handle_message, FUN(X[[i]], ...) error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
16: end_test = function() {lapply(seq_len(cores), inner.do) })
17: 31: test_code(code, parent.frame())mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed,
mc.silent = silent, mc.cores = cores)32:
test_that("cross-sectional id le 400 binomial", {18: plinkfiles <- strsplit(system.file("extdata", "geno.bed", e$fun(obj, substitute(ex), parent.frame(), e$data) package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")19: samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")foreach(i = 1:ncores) %dopar% { gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") if (!is.null(obj$P)) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") if (bgenInfo$LayoutFlag == 2) { txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") infile, paste0(outfile, "_tmp.", i), center2, data(example) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, suppressWarnings(RNGversion("3.5.0")) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], set.seed(123) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, pheno <- rbind(example$pheno, example$pheno[1:100, ]) 1) pheno$id <- 1:500 } pheno$disease[sample(1:500, 20)] <- NA else { pheno$age[sample(1:500, 20)] <- NA .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, pheno$sex[sample(1:500, 20)] <- NA infile, paste0(outfile, "_tmp.", i), center2, pheno <- pheno[sample(1:500, 450), ] MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], pheno <- pheno[pheno$id <= 400, ] threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, kins <- example$GRM 1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, } id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI") else { if (bgenInfo$LayoutFlag == 2) { select <- match(1:400, unique(obj1$id_include)) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() obj$Sigma_iX, obj$cov, infile, paste0(outfile, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, miss.cutoff, miss.method, nperbatch, select, header = TRUE, as.is = TRUE) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, bgenInfo$N, bgenInfo$CompressionFlag, 1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") } unlink(obj1.outfile.bed.noselect.1.tmp) else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj1.outfile.bed.select.1 <- tempfile() obj$Sigma_iX, obj$cov, infile, paste0(outfile, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, miss.cutoff, miss.method, nperbatch, select, header = TRUE, as.is = TRUE) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) } obj1.outfile.bgen.noselect.1 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }
outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, 20: header = TRUE, as.is = TRUE)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.1.tmp <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)21: obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)
unlink(obj1.outfile.bgen.noselect.1.tmp)22: obj1.outfile.bgen.select.1 <- tempfile()eval(code, test_env)
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 23: select = select, outfile = obj1.outfile.bgen.select.1)withCallingHandlers({ obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", if (snapshot_skipped) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, skip("On CRAN") c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", else if (!new_expectations && skip_on_empty) { quietly = TRUE)) { skip_empty() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) } obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) } obj1.outfile.gds.noselect.1.tmp <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, snapshot_skipped <<- TRUE ncores = 2) invokeRestart("muffle_cran_snapshot") obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)
unlink(obj1.outfile.gds.noselect.1.tmp)24: obj1.outfile.gds.select.1 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)
obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, 25: tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)26: expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))tryCatchList(expr, classes, parentenv, handlers) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942,
27: 0.986534857)))tryCatch(withCallingHandlers({ unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt.select.1 <- tempfile() if (snapshot_skipped) { skip("On CRAN") glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { "Allele2")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) } obj1.outfile.txt.select.1.tmp <- tempfile()}, snapshot_on_cran = function(cnd) { expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, snapshot_skipped <<- TRUE infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot") select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") unlink(obj1.outfile.txt.select.1.tmp) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.outfile.txt1.select.1 <- tempfile()
glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, 28: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", doWithOneRestart(return(expr), restart) "Allele2"))
obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, 29: withOneRestart(expr, restarts[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt.select.1, obj1.txt1.select.1)30: obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, withRestarts(tryCatch(withCallingHandlers({ infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1", new_expectations <- the$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, skip("On CRAN") header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, } obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, else if (!new_expectations && skip_on_empty) { obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, skip_empty() obj1.outfile.txt2.select.1)) } skip_on_cran()}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI")}, snapshot_on_cran = function(cnd) { select <- match(1:400, unique(obj2$id_include)) snapshot_skipped <<- TRUE select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj2.outfile.bed.select.1 <- tempfile() end_test = function() { glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, }) header = TRUE, as.is = TRUE)
expect_equal(obj2.bed.noselect.1, obj2.bed.select.1)31: obj2.outfile.bgen.noselect.1 <- tempfile()test_code(code, parent.frame()) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1)
obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, 32: header = TRUE, as.is = TRUE)test_that("cross-sectional id le 400 binomial", { obj2.outfile.bgen.select.1 <- tempfile() plinkfiles <- strsplit(system.file("extdata", "geno.bed", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] select = select, outfile = obj2.outfile.bgen.select.1) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") header = TRUE, as.is = TRUE) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", data(example) suppressWarnings(RNGversion("3.5.0")) "VAR", "PVAL")]) set.seed(123) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", pheno <- rbind(example$pheno, example$pheno[1:100, ]) quietly = TRUE)) { pheno$id <- 1:500 obj2.outfile.gds.noselect.1 <- tempfile() pheno$disease[sample(1:500, 20)] <- NA glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, pheno <- pheno[sample(1:500, 450), ] header = TRUE, as.is = TRUE) pheno <- pheno[pheno$id <= 400, ] obj2.outfile.gds.select.1 <- tempfile() kins <- example$GRM glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) select[is.na(select)] <- 0 expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) obj1.outfile.bed.noselect.1 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj2.outfile.txt.select.1 <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.noselect.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, "Allele2")) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, unlink(obj1.outfile.bed.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.1 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj2.outfile.txt1.select.1 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.bgen.noselect.1 <- 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", "Allele2")) outfile = obj1.outfile.bgen.noselect.1) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.bgen.noselect.1.tmp) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.outfile.bgen.select.1 <- tempfile() idx <- sample(nrow(pheno)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, pheno <- pheno[idx, ] select = select, outfile = obj1.outfile.bgen.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) select[is.na(select)] <- 0 expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.outfile.bed.noselect.2 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, "VAR", "PVAL")]) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) quietly = TRUE)) { obj1.outfile.bed.select.2 <- tempfile() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, expect_equal(obj1.bed.select.1, obj1.bed.select.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2 <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, 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.noselect.1, obj1.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) select = select, outfile = obj1.outfile.bgen.select.2) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) 0.986534857))) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) quietly = TRUE)) { } obj1.outfile.gds.noselect.2 <- tempfile() obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) "Allele2")) obj1.outfile.gds.select.2 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.txt.select.1.tmp) "Allele2")) obj1.outfile.txt1.select.1 <- tempfile() obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt.select.1, obj1.txt.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt1.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.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.txt1.select.1) "Allele2")) obj1.outfile.txt2.select.1 <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.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.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, 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.txt2.select.1, obj1.txt2.select.2) obj1.outfile.txt2.select.1)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip_on_cran() id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select <- match(1:400, unique(obj2$id_include)) id = "id", family = binomial(link = "logit"), method = "REML", select[is.na(select)] <- 0 method.optim = "AI") obj2.outfile.bed.noselect.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select[is.na(select)] <- 0 obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj2.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.outfile.bed.select.1 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) header = TRUE, as.is = TRUE) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, expect_equal(obj2.bed.select.1, obj2.bed.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.1 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, 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.bgen.noselect.1, obj2.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.2) select = select, outfile = obj2.outfile.bgen.select.1) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", quietly = TRUE)) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.outfile.gds.noselect.2 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) } obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, 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.txt.select.2 <- read.table(obj2.outfile.txt.select.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.outfile.txt1.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, idx <- sample(nrow(kins)) select = select, infile.header.print = c("SNP", "Allele1", kins <- kins[idx, idx] "Allele2")) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.txt.select.1, obj2.txt2.select.1) select <- match(1:400, unique(obj1$id_include)) idx <- sample(nrow(pheno)) select[is.na(select)] <- 0 pheno <- pheno[idx, ] obj1.outfile.bed.noselect.3 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.outfile.bed.noselect.2 <- tempfile() obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, 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.bed.select.1, obj1.bed.select.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.2 <- tempfile() outfile = obj1.outfile.bgen.noselect.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.2 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bgen.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.3 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.select.3 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.gds.noselect.2 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, expect_equal(obj1.gds.select.1, obj1.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.txt.select.3 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.3) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.txt1.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.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.2 <- read.table(obj1.outfile.txt.select.2, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt1.select.2 <- tempfile() obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.outfile.txt2.select.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, id = "id", family = binomial(link = "logit"), method = "REML", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, method.optim = "AI") select = select, infile.header.print = c("SNP", "Allele1", select <- match(1:400, unique(obj2$id_include)) "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.3 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) select <- match(1:400, unique(obj2$id_include)) obj2.outfile.bed.select.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.bed.noselect.2 <- tempfile() obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.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.bed.select.1, obj2.bed.select.3) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) outfile = obj2.outfile.bgen.noselect.3) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj2.outfile.bgen.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { select = select, outfile = obj2.outfile.bgen.select.2) obj2.outfile.gds.noselect.3 <- tempfile() obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.noselect.2 <- tempfile() obj2.outfile.gds.select.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) } obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, expect_equal(obj2.gds.select.1, obj2.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt.select.3) "Allele2")) obj2.outfile.txt1.select.3 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, expect_equal(obj2.txt.select.1, obj2.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, idx <- sample(nrow(kins)) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, kins <- kins[idx, idx] obj2.outfile.txt2.select.1)) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, method.optim = "AI") obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, select <- match(1:400, unique(obj1$id_include)) obj1.outfile.txt2.select.2)) select[is.na(select)] <- 0 unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj1.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.outfile.txt2.select.3)) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj1.outfile.bgen.noselect.3) quietly = TRUE)) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.bgen.select.3 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) select = select, outfile = obj1.outfile.bgen.select.3)}) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3,
header = TRUE, as.is = TRUE)33: expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)eval(code, test_env) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {34: obj1.outfile.gds.noselect.3 <- tempfile()eval(code, test_env) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)
obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 35: header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) eval(code, test_env) obj1.outfile.gds.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) if (snapshot_skipped) { obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } else if (!new_expectations && skip_on_empty) { expect_equal(obj1.gds.select.1, obj1.gds.select.3) skip_empty() } } obj1.outfile.txt.select.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, if (on_cran()) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip(paste0("{", e$package, "} is not installed.")) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, snapshot_on_cran = function(cnd) { obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.txt.select.1, obj1.txt.select.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.txt1.select.3 <- tempfile() error = handle_error, interrupt = handle_interrupt) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 36: select = select, infile.header.print = c("SNP", "Allele1", doTryCatch(return(expr), name, parentenv, handler) "Allele2"))
obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, 37: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)
obj1.outfile.txt2.select.3 <- tempfile()38: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, tryCatchList(expr, classes, parentenv, handlers) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 39: "Allele2"))tryCatch(withCallingHandlers({ obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) if (snapshot_skipped) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip("On CRAN") id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI") else if (!new_expectations && skip_on_empty) { select <- match(1:400, unique(obj2$id_include)) skip_empty() select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) if (on_cran()) { obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)}, snapshot_on_cran = function(cnd) { obj2.outfile.bed.select.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) invokeRestart("muffle_cran_snapshot") obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) expect_equal(obj2.bed.select.1, obj2.bed.select.3)
obj2.outfile.bgen.noselect.3 <- tempfile()40: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, doWithOneRestart(return(expr), restart) outfile = obj2.outfile.bgen.noselect.3)
obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 41: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)
obj2.outfile.bgen.select.3 <- tempfile()42: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, withRestarts(tryCatch(withCallingHandlers({ select = select, outfile = obj2.outfile.bgen.select.3) eval(code, test_env) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) skip("On CRAN") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) { else if (!new_expectations && skip_on_empty) { obj2.outfile.gds.noselect.3 <- tempfile() skip_empty() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) } obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.gds.select.3 <- tempfile() } glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)}, snapshot_on_cran = function(cnd) { obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj2.gds.select.1, obj2.gds.select.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj2.outfile.txt.select.3 <- tempfile() end_test = function() { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, }) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 43: "Allele2"))test_code(code = exprs, env = env, reporter = get_reporter() %||% obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) StopReporter$new()) expect_equal(obj2.txt.select.1, obj2.txt.select.3)
obj2.outfile.txt1.select.3 <- tempfile()44: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, source_file(path, env = env(env), desc = desc, shuffle = shuffle, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, error_call = error_call) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))45: obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, FUN(X[[i]], ...) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)46: obj2.outfile.txt2.select.3 <- tempfile()lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, error_call = error_call) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 47: "Allele2"))doTryCatch(return(expr), name, parentenv, handler) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,
48: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)
unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, 49: obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, tryCatchList(expr, classes, parentenv, handlers) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1,
obj2.outfile.txt2.select.1))50: unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, tryCatch(code, testthat_abort_reporter = function(cnd) { obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, cat(conditionMessage(cnd), "\n") obj1.outfile.txt2.select.2)) NULL unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, }) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2,
obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, 51: obj2.outfile.txt2.select.2))with_reporter(reporters$multi, lapply(test_paths, test_one_file, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3,
obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, 52: obj1.outfile.txt2.select.3))test_files_serial(test_dir = test_dir, test_package = test_package, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, desc = desc, load_package = load_package, shuffle = shuffle, obj2.outfile.txt2.select.3)) error_call = error_call) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) 53: unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, test_files(test_dir = path, test_paths = test_paths, test_package = package, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, reporter = reporter, load_helpers = load_helpers, env = env, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, load_package = load_package, parallel = parallel, shuffle = shuffle) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))
})54:
test_dir("testthat", package = package, reporter = reporter, 33: ..., load_package = "installed")eval(code, test_env)
55: 34: test_check("GMMAT")
eval(code, test_env)An irrecoverable exception occurred. R is aborting now ...
35: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64