Last updated on 2026-10-13 09:50:29 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 44.57 | 239.79 | 284.36 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.29 | 207.61 | 246.90 | 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 | 18.00 | 79.00 | 97.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 402.00 | 488.00 | 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$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
22: eval(code, test_env)
23: withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed."))
Traceback:
} 1: }, snapshot_on_cran = function(cnd) {eval(c.expr, envir = args, enclos = envir) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot") 2: }, skip = handle_skip, warning = handle_warning, message = handle_message, eval(c.expr, envir = args, enclos = envir) error = handle_error, interrupt = handle_interrupt)
3: 24: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
4: 25: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: 26: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
6: 27: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)tryCatch(withCallingHandlers({
eval(code, test_env) 7: new_expectations <- the$test_expectations > starting_expectationsFUN(X[[i]], ...) if (snapshot_skipped) {
skip("On CRAN") 8: }lapply(X = S, FUN = FUN, ...) else if (!new_expectations && skip_on_empty) {
skip_empty() 9: }}, expectation = handle_expectation, packageNotFoundError = function(e) {doTryCatch(return(expr), name, parentenv, handler) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))10: }tryCatchOne(expr, names, parentenv, handlers[[1L]])}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")11: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatchList(expr, classes, parentenv, handlers)
error = handle_error, interrupt = handle_interrupt), error = handle_fatal)12:
tryCatch(expr, error = function(e) {28: call <- conditionCall(e)doWithOneRestart(return(expr), restart) if (!is.null(call)) {
29: if (identical(call[[1L]], quote(doTryCatch))) withOneRestart(expr, restarts[[1L]]) call <- sys.call(-4L)
dcall <- deparse(call, nlines = 1L)30: prefix <- paste("Error in", dcall, ": ") LONG <- 75LwithRestarts(tryCatch(withCallingHandlers({ sm <- strsplit(conditionMessage(e), "\n")[[1L]] eval(code, test_env) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") new_expectations <- the$test_expectations > starting_expectations if (is.na(w)) if (snapshot_skipped) { w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], skip("On CRAN") type = "b") if (w > LONG) } else if (!new_expectations && skip_on_empty) { prefix <- paste0(prefix, "\n ") skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) { else prefix <- "Error : " if (on_cran()) { msg <- paste0(prefix, conditionMessage(e), "\n") skip(paste0("{", e$package, "} is not installed.")) .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) {}, snapshot_on_cran = function(cnd) { cat(msg, file = outFile) snapshot_skipped <<- TRUE .Internal(printDeferredWarnings()) invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, invisible(structure(msg, class = "try-error", condition = e)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), }) end_test = function() {
})13:
try(lapply(X = S, FUN = FUN, ...), silent = TRUE)31:
test_code(code, parent.frame())14:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))32:
test_that("cross-sectional id le 400 binomial", {15: plinkfiles <- strsplit(system.file("extdata", "geno.bed", FUN(X[[i]], ...) package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")16: samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")lapply(seq_len(cores), inner.do) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")
txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")17: txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, data(example) mc.silent = silent, mc.cores = cores) suppressWarnings(RNGversion("3.5.0"))
set.seed(123)18: pheno <- rbind(example$pheno, example$pheno[1:100, ])e$fun(obj, substitute(ex), parent.frame(), e$data) pheno$id <- 1:500
pheno$disease[sample(1:500, 20)] <- NA19: pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NAforeach(i = 1:ncores) %dopar% { pheno <- pheno[sample(1:500, 450), ] if (!is.null(obj$P)) { pheno <- pheno[pheno$id <= 400, ] if (bgenInfo$LayoutFlag == 2) { kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, id = "id", family = binomial(link = "logit"), method = "REML", infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], method.optim = "AI") threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, select <- match(1:400, unique(obj1$id_include)) 1) } select[is.na(select)] <- 0 else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, obj1.outfile.bed.noselect.1 <- tempfile() infile, paste0(outfile, "_tmp.", i), center2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() nperbatch, select, threadInfo$begin[i], threadInfo$end[i], expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, unlink(obj1.outfile.bed.noselect.1.tmp) 1) obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) } 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) obj1.outfile.bgen.noselect.1 <- tempfile() else { if (bgenInfo$LayoutFlag == 2) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, outfile = obj1.outfile.bgen.noselect.1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, bgenInfo$N, bgenInfo$CompressionFlag, 1) header = TRUE, as.is = TRUE) } obj1.outfile.bgen.noselect.1.tmp <- tempfile() else { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj$Sigma_iX, obj$cov, infile, paste0(outfile, header = TRUE, as.is = TRUE) "_tmp.", i), center2, MAF.range[1], MAF.range[2], expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) miss.cutoff, miss.method, nperbatch, select, obj1.outfile.bgen.select.1 <- tempfile() threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, bgenInfo$N, bgenInfo$CompressionFlag, 1) select = select, outfile = obj1.outfile.bgen.select.1) } obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, }} header = TRUE, as.is = TRUE)
expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)20: expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 21: "VAR", "PVAL")])eval(code, test_env) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {22: obj1.outfile.gds.noselect.1 <- tempfile()eval(code, test_env)
glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, 23: header = TRUE, as.is = TRUE)withCallingHandlers({ obj1.outfile.gds.noselect.1.tmp <- tempfile() eval(code, test_env) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, new_expectations <- the$test_expectations > starting_expectations ncores = 2) if (snapshot_skipped) { obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) else if (!new_expectations && skip_on_empty) { unlink(obj1.outfile.gds.noselect.1.tmp) skip_empty() obj1.outfile.gds.select.1 <- tempfile() } glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) } expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))}, snapshot_on_cran = function(cnd) { expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, snapshot_skipped <<- TRUE 0.986534857))) invokeRestart("muffle_cran_snapshot") unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt) obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 24: select = select, infile.header.print = c("SNP", "Allele1", doTryCatch(return(expr), name, parentenv, handler) "Allele2"))
obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, 25: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)
26: obj1.outfile.txt.select.1.tmp <- tempfile()tryCatchList(expr, classes, parentenv, handlers) 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, 27: select = select, infile.header.print = c("SNP", "Allele1", tryCatch(withCallingHandlers({ "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") eval(code, test_env) unlink(obj1.outfile.txt.select.1.tmp) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt1.select.1 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() if (on_cran()) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, skip(paste0("{", e$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 obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1,
obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, 28: obj1.outfile.txt2.select.1))doWithOneRestart(return(expr), restart) skip_on_cran()
obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, 29: id = "id", family = binomial(link = "logit"), method = "REML", withOneRestart(expr, restarts[[1L]]) method.optim = "AI")
select <- match(1:400, unique(obj2$id_include))30: select[is.na(select)] <- 0withRestarts(tryCatch(withCallingHandlers({ obj2.outfile.bed.noselect.1 <- tempfile() eval(code, test_env) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) new_expectations <- the$test_expectations > starting_expectations obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") obj2.outfile.bed.select.1 <- tempfile() } glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) else if (!new_expectations && skip_on_empty) { skip_empty() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, }}, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.bgen.noselect.1 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, }, snapshot_on_cran = function(cnd) { outfile = obj2.outfile.bgen.noselect.1) snapshot_skipped <<- TRUE obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.bgen.select.1 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, end_test = function() { select = select, outfile = obj2.outfile.bgen.select.1) }) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1,
header = TRUE, as.is = TRUE)31: expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)test_code(code, parent.frame()) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1",
"A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, 32: c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", test_that("cross-sectional id le 400 binomial", { "VAR", "PVAL")]) plinkfiles <- strsplit(system.file("extdata", "geno.bed", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj2.outfile.gds.select.1 <- tempfile() txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, data(example) header = TRUE, as.is = TRUE) suppressWarnings(RNGversion("3.5.0")) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) set.seed(123) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) pheno <- rbind(example$pheno, example$pheno[1:100, ]) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, pheno$id <- 1:500 0.996996766))) pheno$disease[sample(1:500, 20)] <- NA } pheno$age[sample(1:500, 20)] <- NA obj2.outfile.txt.select.1 <- tempfile() pheno$sex[sample(1:500, 20)] <- NA glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, pheno <- pheno[sample(1:500, 450), ] infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- pheno[pheno$id <= 400, ] select = select, infile.header.print = c("SNP", "Allele1", kins <- example$GRM "Allele2")) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.txt1.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.bed.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.bed.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj2.outfile.txt2.select.1 <- tempfile() unlink(obj1.outfile.bed.noselect.1.tmp) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.outfile.bed.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, pheno <- pheno[idx, ] outfile = obj1.outfile.bgen.noselect.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() method.optim = "AI") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj1$id_include)) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) select[is.na(select)] <- 0 obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, unlink(obj1.outfile.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.1 <- 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() select = select, outfile = obj1.outfile.bgen.select.1) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.outfile.bgen.noselect.2 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", outfile = obj1.outfile.bgen.noselect.2) "VAR", "PVAL")]) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.gds.noselect.1 <- tempfile() obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.outfile.gds.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) ncores = 2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_equal(obj1.gds.select.1, obj1.gds.select.2) 0.986534857))) } unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.outfile.txt.select.2 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.txt.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, expect_equal(obj1.txt.select.1, obj1.txt.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.txt.select.1.tmp <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.txt.select.1.tmp) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt1.select.1 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt2.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.outfile.bed.noselect.2 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, 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.bed.noselect.1, obj2.bed.noselect.2) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2.outfile.bed.select.2 <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.outfile.txt2.select.1)) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, skip_on_cran() header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.bed.select.1, obj2.bed.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bgen.noselect.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj2$id_include)) outfile = obj2.outfile.bgen.noselect.2) select[is.na(select)] <- 0 obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.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.bgen.noselect.1, obj2.bgen.noselect.2) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bed.select.1 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.bed.select.1 <- read.table(obj2.outfile.bed.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.bed.noselect.1, obj2.bed.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bgen.noselect.1 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.noselect.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.select.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", } "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.outfile.txt.select.2 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, "VAR", "PVAL")]) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", quietly = TRUE)) { "Allele2")) obj2.outfile.gds.noselect.1 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, expect_equal(obj2.txt.select.1, obj2.txt.select.2) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, } 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", obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) kins <- kins[idx, idx] obj2.outfile.txt1.select.1 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") "Allele2")) select <- match(1:400, unique(obj1$id_include)) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) "Allele2")) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.outfile.bgen.noselect.3 <- tempfile() idx <- sample(nrow(pheno)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) pheno <- pheno[idx, ] obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) method.optim = "AI") obj1.outfile.bgen.select.3 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select[is.na(select)] <- 0 select = select, outfile = obj1.outfile.bgen.select.3) obj1.outfile.bed.noselect.2 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.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.bgen.select.1, obj1.bgen.select.3) 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.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.bed.select.2 <- read.table(obj1.outfile.bed.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.bed.select.1, obj1.bed.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj1.outfile.bgen.noselect.2 <- tempfile() obj1.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) outfile = obj1.outfile.bgen.noselect.2) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) } obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, select = select, outfile = obj1.outfile.bgen.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.txt1.select.3 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.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.gds.select.1, obj1.gds.select.2) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt2.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj1.outfile.txt.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.txt.select.1, obj1.txt.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt1.select.2 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, select <- match(1:400, unique(obj2$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.noselect.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, outfile = obj2.outfile.bgen.noselect.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.2 <- tempfile() obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, select = select, outfile = obj2.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.2 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, 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(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.select.1, obj2.gds.select.3) select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.noselect.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.txt1.select.3 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.outfile.txt2.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, "Allele2")) obj2.outfile.txt2.select.1)) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj2.outfile.txt2.select.2 <- tempfile() obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj1.outfile.txt2.select.2)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2)) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, idx <- sample(nrow(kins)) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, kins <- kins[idx, idx] obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt2.select.3)) id = "id", family = binomial(link = "logit"), method = "REML", unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, method.optim = "AI") obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, select <- match(1:400, unique(obj1$id_include)) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.3)) select[is.na(select)] <- 0 if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bed.noselect.3 <- tempfile() quietly = TRUE)) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj1.outfile.bed.select.3 <- tempfile() obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3)}) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3,
header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3)33: obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, eval(code, test_env) outfile = obj1.outfile.bgen.noselect.3)
obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, 34: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)
obj1.outfile.bgen.select.3 <- tempfile()35: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, withCallingHandlers({ select = select, outfile = obj1.outfile.bgen.select.3) eval(code, test_env) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) skip("On CRAN") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) { else if (!new_expectations && skip_on_empty) { obj1.outfile.gds.noselect.3 <- tempfile() skip_empty() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) } obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.gds.select.3 <- tempfile() } glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)}, snapshot_on_cran = function(cnd) { obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.gds.select.1, obj1.gds.select.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, } obj1.outfile.txt.select.3 <- tempfile() error = handle_error, interrupt = handle_interrupt) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 36: select = select, infile.header.print = c("SNP", "Allele1", doTryCatch(return(expr), name, parentenv, handler) "Allele2"))
obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, 37: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.txt.select.1, obj1.txt.select.3)
obj1.outfile.txt1.select.3 <- tempfile()38: glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, tryCatchList(expr, classes, parentenv, handlers) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
39: select = select, infile.header.print = c("SNP", "Allele1", tryCatch(withCallingHandlers({ "Allele2")) eval(code, test_env) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) skip("On CRAN") obj1.outfile.txt2.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, else if (!new_expectations && skip_on_empty) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_empty() select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) } obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, }, snapshot_on_cran = function(cnd) { id = "id", family = binomial(link = "logit"), method = "REML", snapshot_skipped <<- TRUE method.optim = "AI") invokeRestart("muffle_cran_snapshot") select <- match(1:400, unique(obj2$id_include))}, skip = handle_skip, warning = handle_warning, message = handle_message, select[is.na(select)] <- 0 error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj2.outfile.bed.noselect.3 <- tempfile()
glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)40: obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, doWithOneRestart(return(expr), restart)
header = TRUE, as.is = TRUE)41: withOneRestart(expr, restarts[[1L]]) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)
obj2.outfile.bed.select.3 <- tempfile()42: glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3)withRestarts(tryCatch(withCallingHandlers({ obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, eval(code, test_env) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.3) new_expectations <- the$test_expectations > starting_expectations obj2.outfile.bgen.noselect.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, skip("On CRAN") outfile = obj2.outfile.bgen.noselect.3) } obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) } obj2.outfile.bgen.select.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (on_cran()) { select = select, outfile = obj2.outfile.bgen.select.3) skip(paste0("{", e$package, "} is not installed.")) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, skip = handle_skip, warning = handle_warning, message = handle_message, quietly = TRUE)) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj2.outfile.gds.noselect.3 <- tempfile() end_test = function() { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) }) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3,
header = TRUE, as.is = TRUE)43: expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)test_code(code = exprs, env = env, reporter = get_reporter() %||% obj2.outfile.gds.select.3 <- tempfile() StopReporter$new()) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)
obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, 44: header = TRUE, as.is = TRUE)source_file(path, env = env(env), desc = desc, shuffle = shuffle, expect_equal(obj2.gds.select.1, obj2.gds.select.3) error_call = error_call) }
obj2.outfile.txt.select.3 <- tempfile()45: glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, FUN(X[[i]], ...) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 46: "Allele2"))lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, error_call = error_call) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt.select.1, obj2.txt.select.3)47: obj2.outfile.txt1.select.3 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", "Allele2"))48: obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)49: obj2.outfile.txt2.select.3 <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 50: select = select, infile.header.print = c("SNP", "Allele1", tryCatch(code, testthat_abort_reporter = function(cnd) { "Allele2")) cat(conditionMessage(cnd), "\n") obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, NULL header = TRUE, as.is = TRUE)})
expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1,
obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, 52: obj2.outfile.txt2.select.1))test_files_serial(test_dir = test_dir, test_package = test_package, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call) obj1.outfile.txt2.select.2))
unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, 53: obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, test_files(test_dir = path, test_paths = test_paths, test_package = package, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, reporter = reporter, load_helpers = load_helpers, env = env, obj2.outfile.txt2.select.2)) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3,
obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, 54: obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed") obj1.outfile.txt2.select.3))
unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, 55: obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, test_check("GMMAT") obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3,
obj2.outfile.txt2.select.3))An irrecoverable exception occurred. R is aborting now ...
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
33: eval(code, test_env)
34: eval(code, test_env)
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