Last updated on 2026-11-05 00:52:43 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.25 | 242.88 | 286.13 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 45.09 | 212.21 | 257.30 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 158.82 | 187.82 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 166.67 | 208.67 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 52.77 | 231.81 | 284.58 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 54.79 | 229.43 | 284.22 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 100.00 | 116.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 87.00 | 387.00 | 474.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
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:
Traceback:
1: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))eval(c.expr, envir = args, enclos = envir)
15: 2: FUN(X[[i]], ...)eval(c.expr, envir = args, enclos = envir)
16: 3: lapply(seq_len(cores), inner.do)doTryCatch(return(expr), name, parentenv, handler)
17: 4: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, tryCatchOne(expr, names, parentenv, handlers[[1L]]) mc.silent = silent, mc.cores = cores)
5: 18: tryCatchList(expr, classes, parentenv, handlers)e$fun(obj, substitute(ex), parent.frame(), e$data)
6: 19: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) { 7: if (bgenInfo$LayoutFlag == 2) {FUN(X[[i]], ...) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 8: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, lapply(X = S, FUN = FUN, ...) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 9: 1)doTryCatch(return(expr), name, parentenv, handler) }
else {10: .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, tryCatchOne(expr, names, parentenv, handlers[[1L]]) infile, paste0(outfile, "_tmp.", i), center2,
MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 11: nperbatch, select, threadInfo$begin[i], threadInfo$end[i], tryCatchList(expr, classes, parentenv, handlers) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,
1)12: }tryCatch(expr, error = function(e) { } call <- conditionCall(e) else { if (!is.null(call)) { if (bgenInfo$LayoutFlag == 2) { if (identical(call[[1L]], quote(doTryCatch))) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, call <- sys.call(-4L) obj$Sigma_iX, obj$cov, infile, paste0(outfile, dcall <- deparse(call, nlines = 1L) "_tmp.", i), center2, MAF.range[1], MAF.range[2], prefix <- paste("Error in", dcall, ": ") miss.cutoff, miss.method, nperbatch, select, LONG <- 75L threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], sm <- strsplit(conditionMessage(e), "\n")[[1L]] bgenInfo$N, bgenInfo$CompressionFlag, 1) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") } if (is.na(w)) else { w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, type = "b") obj$Sigma_iX, obj$cov, infile, paste0(outfile, if (w > LONG) "_tmp.", i), center2, MAF.range[1], MAF.range[2], prefix <- paste0(prefix, "\n ") miss.cutoff, miss.method, nperbatch, select, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], else prefix <- "Error : " bgenInfo$N, bgenInfo$CompressionFlag, 1) msg <- paste0(prefix, conditionMessage(e), "\n") } .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { } cat(msg, file = outFile)} .Internal(printDeferredWarnings())
}20: invisible(structure(msg, class = "try-error", condition = e))glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
13: 21: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)eval(code, test_env)
14: 22: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))eval(code, test_env)
15: 23: FUN(X[[i]], ...)withCallingHandlers({
eval(code, test_env)16: new_expectations <- the$test_expectations > starting_expectationslapply(seq_len(cores), inner.do) if (snapshot_skipped) {
skip("On CRAN")17: }mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, else if (!new_expectations && skip_on_empty) { mc.silent = silent, mc.cores = cores) skip_empty()
}18: }, expectation = handle_expectation, packageNotFoundError = function(e) {e$fun(obj, substitute(ex), parent.frame(), e$data) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))19: }foreach(i = 1:ncores) %dopar% {}, snapshot_on_cran = function(cnd) { if (!is.null(obj$P)) { snapshot_skipped <<- TRUE if (bgenInfo$LayoutFlag == 2) { invokeRestart("muffle_cran_snapshot") .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, }, skip = handle_skip, warning = handle_warning, message = handle_message, infile, paste0(outfile, "_tmp.", i), center2, error = handle_error, interrupt = handle_interrupt) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 24: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, doTryCatch(return(expr), name, parentenv, handler) 1)
}25: else {tryCatchOne(expr, names, parentenv, handlers[[1L]]) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 26: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, tryCatchList(expr, classes, parentenv, handlers) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 27: 1)tryCatch(withCallingHandlers({ } eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations else { if (snapshot_skipped) { if (bgenInfo$LayoutFlag == 2) { skip("On CRAN") .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, } obj$Sigma_iX, obj$cov, infile, paste0(outfile, else if (!new_expectations && skip_on_empty) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], skip_empty() miss.cutoff, miss.method, nperbatch, select, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], }, expectation = handle_expectation, packageNotFoundError = function(e) { bgenInfo$N, bgenInfo$CompressionFlag, 1) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) else { } .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, }, snapshot_on_cran = function(cnd) { obj$Sigma_iX, obj$cov, infile, paste0(outfile, snapshot_skipped <<- TRUE "_tmp.", i), center2, MAF.range[1], MAF.range[2], invokeRestart("muffle_cran_snapshot") miss.cutoff, miss.method, nperbatch, select, }, skip = handle_skip, warning = handle_warning, message = handle_message, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], error = handle_error, interrupt = handle_interrupt), error = handle_fatal) bgenInfo$N, bgenInfo$CompressionFlag, 1)
}28: }doWithOneRestart(return(expr), restart)}
29: 20: withOneRestart(expr, restarts[[1L]])glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)30:
withRestarts(tryCatch(withCallingHandlers({21: eval(code, test_env)eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {22: skip("On CRAN")eval(code, test_env) }
else if (!new_expectations && skip_on_empty) {23: skip_empty()withCallingHandlers({ } eval(code, test_env)}, expectation = handle_expectation, packageNotFoundError = function(e) { new_expectations <- the$test_expectations > starting_expectations if (on_cran()) { if (snapshot_skipped) { skip(paste0("{", e$package, "} is not installed.")) skip("On CRAN") } }}, snapshot_on_cran = function(cnd) { else if (!new_expectations && skip_on_empty) { snapshot_skipped <<- TRUE skip_empty() invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, }, expectation = handle_expectation, packageNotFoundError = function(e) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), if (on_cran()) { end_test = function() { skip(paste0("{", e$package, "} is not installed.")) }) }
}, snapshot_on_cran = function(cnd) {31: snapshot_skipped <<- TRUEtest_code(code, parent.frame()) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 32: error = handle_error, interrupt = handle_interrupt)test_that("cross-sectional id le 400 binomial", {
plinkfiles <- strsplit(system.file("extdata", "geno.bed", 24: package = "GMMAT"), ".bed", fixed = TRUE)[[1]]doTryCatch(return(expr), name, parentenv, handler) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")
samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")25: gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")tryCatchOne(expr, names, parentenv, handlers[[1L]]) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")
txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")26: txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")tryCatchList(expr, classes, parentenv, handlers) data(example)
suppressWarnings(RNGversion("3.5.0"))27: set.seed(123)tryCatch(withCallingHandlers({ pheno <- rbind(example$pheno, example$pheno[1:100, ]) eval(code, test_env) pheno$id <- 1:500 new_expectations <- the$test_expectations > starting_expectations pheno$disease[sample(1:500, 20)] <- NA if (snapshot_skipped) { pheno$age[sample(1:500, 20)] <- NA skip("On CRAN") pheno$sex[sample(1:500, 20)] <- NA } pheno <- pheno[sample(1:500, 450), ] else if (!new_expectations && skip_on_empty) { pheno <- pheno[pheno$id <= 400, ] skip_empty() kins <- example$GRM } obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, }, expectation = handle_expectation, packageNotFoundError = function(e) { id = "id", family = binomial(link = "logit"), method = "REML", if (on_cran()) { method.optim = "AI") skip(paste0("{", e$package, "} is not installed.")) select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0}, snapshot_on_cran = function(cnd) { obj1.outfile.bed.noselect.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) invokeRestart("muffle_cran_snapshot") obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.outfile.bed.noselect.1.tmp <- tempfile()
expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, 28: ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")doWithOneRestart(return(expr), restart) unlink(obj1.outfile.bed.noselect.1.tmp)
obj1.outfile.bed.select.1 <- tempfile()29: glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)withOneRestart(expr, restarts[[1L]]) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1,
header = TRUE, as.is = TRUE)30: expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)withRestarts(tryCatch(withCallingHandlers({ obj1.outfile.bgen.noselect.1 <- tempfile() eval(code, test_env) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, new_expectations <- the$test_expectations > starting_expectations outfile = obj1.outfile.bgen.noselect.1) if (snapshot_skipped) { obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, skip("On CRAN") header = TRUE, as.is = TRUE) } obj1.outfile.bgen.noselect.1.tmp <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip_empty() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) } obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) skip(paste0("{", e$package, "} is not installed.")) unlink(obj1.outfile.bgen.noselect.1.tmp) } obj1.outfile.bgen.select.1 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE select = select, outfile = obj1.outfile.bgen.select.1) invokeRestart("muffle_cran_snapshot") obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, }, 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(obj1.bgen.noselect.1, obj1.bgen.select.1) end_test = function() { expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", }) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[,
c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 31: "VAR", "PVAL")])test_code(code, parent.frame()) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {32: obj1.outfile.gds.noselect.1 <- tempfile()test_that("cross-sectional id le 400 binomial", { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj1.outfile.gds.noselect.1.tmp <- tempfile() samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") ncores = 2) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) data(example) unlink(obj1.outfile.gds.noselect.1.tmp) suppressWarnings(RNGversion("3.5.0")) obj1.outfile.gds.select.1 <- tempfile() set.seed(123) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, pheno$id <- 1:500 header = TRUE, as.is = TRUE) pheno$disease[sample(1:500, 20)] <- NA expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) pheno$age[sample(1:500, 20)] <- NA expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) pheno$sex[sample(1:500, 20)] <- NA expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, pheno <- pheno[sample(1:500, 450), ] 0.986534857))) pheno <- pheno[pheno$id <= 400, ] unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) kins <- example$GRM } obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj1.outfile.bed.noselect.1 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.bed.noselect.1.tmp) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.bed.select.1 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.outfile.txt1.select.1 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, outfile = obj1.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.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) obj1.txt2.select.1 <- read.table(obj1.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(obj1.txt.select.1, obj1.txt2.select.1) obj1.outfile.bgen.select.1 <- tempfile() unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, select = select, outfile = obj1.outfile.bgen.select.1) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", id = "id", family = binomial(link = "logit"), method = "REML", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.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) obj1.outfile.gds.noselect.1 <- tempfile() obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj2.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, unlink(obj1.outfile.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.1 <- tempfile() obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, select = select, outfile = obj2.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, 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(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", 0.986534857))) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", } "VAR", "PVAL")]) obj1.outfile.txt.select.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) "Allele2")) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1.outfile.txt.select.1.tmp <- tempfile() obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, unlink(obj1.outfile.txt.select.1.tmp) 0.996996766))) obj1.outfile.txt1.select.1 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj2.outfile.txt.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.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.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.outfile.txt1.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.outfile.txt2.select.1)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_on_cran() select = select, infile.header.print = c("SNP", "Allele1", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, "Allele2")) id = "id", family = binomial(link = "logit"), method = "REML", obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) select[is.na(select)] <- 0 idx <- sample(nrow(pheno)) obj2.outfile.bed.noselect.1 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") obj2.outfile.bed.select.1 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) select[is.na(select)] <- 0 obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, 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.noselect.1, obj2.bed.select.1) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) outfile = obj2.outfile.bgen.noselect.1) obj1.outfile.bed.select.2 <- tempfile() obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1, obj1.bed.select.2) select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.bgen.noselect.2 <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, 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(obj1.bgen.noselect.1, obj1.bgen.noselect.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.outfile.bgen.select.2 <- tempfile() "VAR", "PVAL")]) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, outfile = obj1.outfile.bgen.select.2) quietly = TRUE)) { obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj2.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { obj2.outfile.gds.select.1 <- tempfile() obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj1.outfile.gds.select.2 <- tempfile() expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) 0.996996766))) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.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.txt.select.1, obj1.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.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(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) outfile = obj2.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.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.bgen.noselect.1, obj1.bgen.noselect.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2) select = select, outfile = obj2.outfile.bgen.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.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(obj1.bgen.select.1, obj1.bgen.select.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) expect_equal(obj2.gds.select.1, obj2.gds.select.2) } } obj1.outfile.txt.select.2 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj1.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, 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, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, 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.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, idx <- sample(nrow(kins)) id = "id", family = binomial(link = "logit"), method = "REML", kins <- kins[idx, idx] method.optim = "AI") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 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(obj1$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, obj1.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, 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.bed.noselect.1, obj1.bed.noselect.3) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1, obj1.bed.select.3) outfile = obj2.outfile.bgen.noselect.2) obj1.outfile.bgen.noselect.3 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) select = select, outfile = obj2.outfile.bgen.select.2) obj1.outfile.bgen.select.3 <- tempfile() obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.3) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.outfile.gds.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) quietly = TRUE)) { obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj1.outfile.gds.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) } obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, expect_equal(obj1.gds.select.1, obj1.gds.select.3) 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.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt.select.2) "Allele2")) obj2.outfile.txt1.select.2 <- tempfile() obj1.txt.select.3 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt1.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) "Allele2")) obj2.outfile.txt2.select.2 <- tempfile() obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) "Allele2")) idx <- sample(nrow(kins)) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, kins <- kins[idx, idx] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, method.optim = "AI") id = "id", family = binomial(link = "logit"), method = "REML", select <- match(1:400, unique(obj1$id_include)) method.optim = "AI") select[is.na(select)] <- 0 select <- match(1:400, unique(obj2$id_include)) obj1.outfile.bed.noselect.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.bed.noselect.3 <- tempfile() obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) header = TRUE, as.is = TRUE) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.outfile.bed.select.3 <- tempfile() obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.3 <- tempfile() outfile = obj1.outfile.bgen.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, outfile = obj2.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.3 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.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(obj1.bgen.select.1, obj1.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) quietly = TRUE)) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.3 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.gds.noselect.3 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.outfile.gds.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, expect_equal(obj2.gds.select.1, obj2.gds.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj2.txt.select.1, obj2.txt.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj1.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) method.optim = "AI") unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, select <- match(1:400, unique(obj2$id_include)) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, select[is.na(select)] <- 0 obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.bed.noselect.3 <- tempfile() obj2.outfile.txt2.select.1)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.outfile.txt2.select.2)) obj2.outfile.bed.select.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2)) expect_equal(obj2.bed.select.1, obj2.bed.select.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.outfile.bgen.noselect.3 <- tempfile() obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, outfile = obj2.outfile.bgen.noselect.3) obj1.outfile.txt2.select.3)) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.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, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.bgen.select.3 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, outfile = obj2.outfile.bgen.select.3) quietly = TRUE)) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.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(obj2.bgen.select.1, obj2.bgen.select.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, quietly = TRUE)) { obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.outfile.gds.noselect.3 <- tempfile()}) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)
obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, 33: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)
obj2.outfile.gds.select.3 <- tempfile()34: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)eval(code, test_env) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3,
header = TRUE, as.is = TRUE)35: expect_equal(obj2.gds.select.1, obj2.gds.select.3)withCallingHandlers({ } eval(code, test_env) obj2.outfile.txt.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, if (snapshot_skipped) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip("On CRAN") select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) else if (!new_expectations && skip_on_empty) { obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.txt1.select.3 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, skip(paste0("{", e$package, "} is not installed.")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, snapshot_on_cran = function(cnd) { "Allele2")) snapshot_skipped <<- TRUE obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) error = handle_error, interrupt = handle_interrupt) obj2.outfile.txt2.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 36: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, doTryCatch(return(expr), name, parentenv, handler) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))37: obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)38: unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, tryCatchList(expr, classes, parentenv, handlers) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1,
obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, 39: obj2.outfile.txt2.select.1))tryCatch(withCallingHandlers({ unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, eval(code, test_env) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, if (snapshot_skipped) { obj1.outfile.txt2.select.2)) skip("On CRAN") unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, } obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, else if (!new_expectations && skip_on_empty) { obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, skip_empty() obj2.outfile.txt2.select.2)) } unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, if (on_cran()) { obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt2.select.3)) } unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, }, snapshot_on_cran = function(cnd) { obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, snapshot_skipped <<- TRUE obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, invokeRestart("muffle_cran_snapshot") obj2.outfile.txt2.select.3))}, skip = handle_skip, warning = handle_warning, message = handle_message, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", error = handle_error, interrupt = handle_interrupt), error = handle_fatal) quietly = TRUE))
unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, 40: obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, doWithOneRestart(return(expr), restart) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2,
obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, 41: obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))withOneRestart(expr, restarts[[1L]])})
42: 33: withRestarts(tryCatch(withCallingHandlers({eval(code, test_env) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations34: if (snapshot_skipped) {eval(code, test_env) skip("On CRAN")
}35: else if (!new_expectations && skip_on_empty) {withCallingHandlers({ skip_empty() eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations}, expectation = handle_expectation, packageNotFoundError = function(e) { if (snapshot_skipped) { if (on_cran()) { skip("On CRAN") skip(paste0("{", e$package, "} is not installed.")) } } else if (!new_expectations && skip_on_empty) {}, snapshot_on_cran = function(cnd) { skip_empty() snapshot_skipped <<- TRUE } invokeRestart("muffle_cran_snapshot")}, expectation = handle_expectation, packageNotFoundError = function(e) {}, skip = handle_skip, warning = handle_warning, message = handle_message, if (on_cran()) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), skip(paste0("{", e$package, "} is not installed.")) end_test = function() { } })}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE43: invokeRestart("muffle_cran_snapshot")test_code(code = exprs, env = env, reporter = get_reporter() %||% }, skip = handle_skip, warning = handle_warning, message = handle_message, StopReporter$new()) error = handle_error, interrupt = handle_interrupt)
44: 36: source_file(path, env = env(env), desc = desc, shuffle = shuffle, doTryCatch(return(expr), name, parentenv, handler) error_call = error_call)
37: 45: tryCatchOne(expr, names, parentenv, handlers[[1L]])FUN(X[[i]], ...)
38: 46: tryCatchList(expr, classes, parentenv, handlers)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,
error_call = error_call)39:
tryCatch(withCallingHandlers({47: eval(code, test_env)doTryCatch(return(expr), name, parentenv, handler) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {48: skip("On CRAN")tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
else if (!new_expectations && skip_on_empty) {49: skip_empty()tryCatchList(expr, classes, parentenv, handlers) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {50: if (on_cran()) {tryCatch(code, testthat_abort_reporter = function(cnd) { skip(paste0("{", e$package, "} is not installed.")) cat(conditionMessage(cnd), "\n") } NULL}, snapshot_on_cran = function(cnd) {}) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")51: }, skip = handle_skip, warning = handle_warning, message = handle_message, with_reporter(reporters$multi, lapply(test_paths, test_one_file, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) env = env, desc = desc, shuffle = shuffle, error_call = error_call))
40: 52: doWithOneRestart(return(expr), restart)test_files_serial(test_dir = test_dir, test_package = test_package,
test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, 41: env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, withOneRestart(expr, restarts[[1L]]) desc = desc, load_package = load_package, shuffle = shuffle,
error_call = error_call)42: withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)53: new_expectations <- the$test_expectations > starting_expectationstest_files(test_dir = path, test_paths = test_paths, test_package = package, if (snapshot_skipped) { reporter = reporter, load_helpers = load_helpers, env = env, skip("On CRAN") stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, } load_package = load_package, parallel = parallel, shuffle = shuffle)
else if (!new_expectations && skip_on_empty) {54: skip_empty()test_dir("testthat", package = package, reporter = reporter, } ..., load_package = "installed")}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {55: skip(paste0("{", e$package, "} is not installed."))test_check("GMMAT") }
}, snapshot_on_cran = function(cnd) {An irrecoverable exception occurred. R is aborting now ...
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