Last updated on 2026-10-29 13:58:08 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 42.90 | 242.49 | 285.39 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.08 | 204.75 | 243.83 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 158.82 | 187.82 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 165.37 | 207.37 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 52.77 | 231.81 | 284.58 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 51.51 | 238.45 | 289.96 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 15.00 | 77.00 | 92.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 89.00 | 386.00 | 475.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/1s]
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:
Traceback:
mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, 1: mc.silent = silent, mc.cores = cores)eval(c.expr, envir = args, enclos = envir)
18: 2: e$fun(obj, substitute(ex), parent.frame(), e$data)eval(c.expr, envir = args, enclos = envir)
19: 3: foreach(i = 1:ncores) %dopar% {doTryCatch(return(expr), name, parentenv, handler) if (!is.null(obj$P)) {
if (bgenInfo$LayoutFlag == 2) { 4: .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, tryCatchOne(expr, names, parentenv, handlers[[1L]]) infile, paste0(outfile, "_tmp.", i), center2,
MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 5: nperbatch, select, threadInfo$begin[i], threadInfo$end[i], tryCatchList(expr, classes, parentenv, handlers) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,
1) 6: }tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) else {
.Call(C_glmm_score_bgen11, as.numeric(res), obj$P, 7: infile, paste0(outfile, "_tmp.", i), center2, FUN(X[[i]], ...) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 8: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, lapply(X = S, FUN = FUN, ...) 1)
} 9: }doTryCatch(return(expr), name, parentenv, handler) else {
if (bgenInfo$LayoutFlag == 2) {10: .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj$Sigma_iX, obj$cov, infile, paste0(outfile,
"_tmp.", i), center2, MAF.range[1], MAF.range[2], 11: miss.cutoff, miss.method, nperbatch, select, tryCatchList(expr, classes, parentenv, handlers)
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 12: bgenInfo$N, bgenInfo$CompressionFlag, 1)tryCatch(expr, error = function(e) { } call <- conditionCall(e) else { if (!is.null(call)) { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, if (identical(call[[1L]], quote(doTryCatch))) obj$Sigma_iX, obj$cov, infile, paste0(outfile, call <- sys.call(-4L) "_tmp.", i), center2, MAF.range[1], MAF.range[2], dcall <- deparse(call, nlines = 1L) miss.cutoff, miss.method, nperbatch, select, prefix <- paste("Error in", dcall, ": ") threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], LONG <- 75L bgenInfo$N, bgenInfo$CompressionFlag, 1) sm <- strsplit(conditionMessage(e), "\n")[[1L]] } w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") } if (is.na(w)) } w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")20: if (w > LONG) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, prefix <- paste0(prefix, "\n ") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) }
else prefix <- "Error : "21: msg <- paste0(prefix, conditionMessage(e), "\n")eval(code, test_env) .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {22: cat(msg, file = outFile)eval(code, test_env) .Internal(printDeferredWarnings())
}23: invisible(structure(msg, class = "try-error", condition = e))withCallingHandlers({}) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations13: if (snapshot_skipped) {try(lapply(X = S, FUN = FUN, ...), silent = TRUE) skip("On CRAN")
}14: else if (!new_expectations && skip_on_empty) {sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) skip_empty()
}}, expectation = handle_expectation, packageNotFoundError = function(e) {15: if (on_cran()) {FUN(X[[i]], ...) skip(paste0("{", e$package, "} is not installed."))
}16: }, snapshot_on_cran = function(cnd) {lapply(seq_len(cores), inner.do) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")17: }, skip = handle_skip, warning = handle_warning, message = handle_message, mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, error = handle_error, interrupt = handle_interrupt) mc.silent = silent, mc.cores = cores)
24: 18: doTryCatch(return(expr), name, parentenv, handler)e$fun(obj, substitute(ex), parent.frame(), e$data)
25: 19: tryCatchOne(expr, names, parentenv, handlers[[1L]])foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) {26: if (bgenInfo$LayoutFlag == 2) {tryCatchList(expr, classes, parentenv, handlers) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 27: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, tryCatch(withCallingHandlers({ nperbatch, select, threadInfo$begin[i], threadInfo$end[i], eval(code, test_env) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, new_expectations <- the$test_expectations > starting_expectations 1) if (snapshot_skipped) { } skip("On CRAN") else { } .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, else if (!new_expectations && skip_on_empty) { infile, paste0(outfile, "_tmp.", i), center2, skip_empty() MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], }, expectation = handle_expectation, packageNotFoundError = function(e) { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, if (on_cran()) { 1) skip(paste0("{", e$package, "} is not installed.")) } } }}, snapshot_on_cran = function(cnd) { else { snapshot_skipped <<- TRUE if (bgenInfo$LayoutFlag == 2) { invokeRestart("muffle_cran_snapshot") .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj$Sigma_iX, obj$cov, infile, paste0(outfile, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) "_tmp.", i), center2, MAF.range[1], MAF.range[2],
miss.cutoff, miss.method, nperbatch, select, 28: doWithOneRestart(return(expr), restart)
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 29: bgenInfo$N, bgenInfo$CompressionFlag, 1)withOneRestart(expr, restarts[[1L]]) }
30: else {withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, new_expectations <- the$test_expectations > starting_expectations obj$Sigma_iX, obj$cov, infile, paste0(outfile, if (snapshot_skipped) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], skip("On CRAN") miss.cutoff, miss.method, nperbatch, select, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], else if (!new_expectations && skip_on_empty) { bgenInfo$N, bgenInfo$CompressionFlag, 1) skip_empty() } } }}, expectation = handle_expectation, packageNotFoundError = function(e) {} if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))20: }glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }, snapshot_on_cran = function(cnd) { outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")21: }, skip = handle_skip, warning = handle_warning, message = handle_message, eval(code, test_env) error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
end_test = function() {22: })eval(code, test_env)
31: 23: test_code(code, parent.frame())withCallingHandlers({ eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations32: if (snapshot_skipped) {test_that("cross-sectional id le 400 binomial", { skip("On CRAN") plinkfiles <- strsplit(system.file("extdata", "geno.bed", } package = "GMMAT"), ".bed", fixed = TRUE)[[1]] else if (!new_expectations && skip_on_empty) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") skip_empty() samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") } gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")}, expectation = handle_expectation, packageNotFoundError = function(e) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") if (on_cran()) { txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") skip(paste0("{", e$package, "} is not installed.")) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") } data(example)}, snapshot_on_cran = function(cnd) { suppressWarnings(RNGversion("3.5.0")) snapshot_skipped <<- TRUE set.seed(123) invokeRestart("muffle_cran_snapshot") pheno <- rbind(example$pheno, example$pheno[1:100, ])}, skip = handle_skip, warning = handle_warning, message = handle_message, pheno$id <- 1:500 error = handle_error, interrupt = handle_interrupt) pheno$disease[sample(1:500, 20)] <- NA
pheno$age[sample(1:500, 20)] <- NA24: pheno$sex[sample(1:500, 20)] <- NAdoTryCatch(return(expr), name, parentenv, handler)
pheno <- pheno[sample(1:500, 450), ]25: pheno <- pheno[pheno$id <= 400, ]tryCatchOne(expr, names, parentenv, handlers[[1L]]) kins <- example$GRM
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 26: id = "id", family = binomial(link = "logit"), method = "REML", tryCatchList(expr, classes, parentenv, handlers) method.optim = "AI")
select <- match(1:400, unique(obj1$id_include))27: select[is.na(select)] <- 0tryCatch(withCallingHandlers({ obj1.outfile.bed.noselect.1 <- tempfile() eval(code, test_env) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) new_expectations <- the$test_expectations > starting_expectations obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") obj1.outfile.bed.noselect.1.tmp <- tempfile() } expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, else if (!new_expectations && skip_on_empty) { ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") skip_empty() unlink(obj1.outfile.bed.noselect.1.tmp) } obj1.outfile.bed.select.1 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) if (on_cran()) { obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)}, snapshot_on_cran = function(cnd) { obj1.outfile.bgen.noselect.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, invokeRestart("muffle_cran_snapshot") outfile = obj1.outfile.bgen.noselect.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) header = TRUE, as.is = TRUE)
obj1.outfile.bgen.noselect.1.tmp <- tempfile()28: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, doWithOneRestart(return(expr), restart) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 29: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)
unlink(obj1.outfile.bgen.noselect.1.tmp)30: obj1.outfile.bgen.select.1 <- tempfile()withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, eval(code, test_env) select = select, outfile = obj1.outfile.bgen.select.1) new_expectations <- the$test_expectations > starting_expectations obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) } expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", else if (!new_expectations && skip_on_empty) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, skip_empty() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", } "VAR", "PVAL")])}, expectation = handle_expectation, packageNotFoundError = function(e) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (on_cran()) { quietly = TRUE)) { skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.gds.noselect.1 <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)}, snapshot_on_cran = function(cnd) { obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.noselect.1.tmp <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), ncores = 2) end_test = function() { obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, }) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)31: unlink(obj1.outfile.gds.noselect.1.tmp)test_code(code, parent.frame()) obj1.outfile.gds.select.1 <- tempfile()
32: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)test_that("cross-sectional id le 400 binomial", { obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, plinkfiles <- strsplit(system.file("extdata", "geno.bed", header = TRUE, as.is = TRUE) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") 0.986534857))) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") } txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj1.outfile.txt.select.1 <- tempfile() data(example) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, suppressWarnings(RNGversion("3.5.0")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, set.seed(123) select = select, infile.header.print = c("SNP", "Allele1", pheno <- rbind(example$pheno, example$pheno[1:100, ]) "Allele2")) pheno$id <- 1:500 obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, pheno$disease[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) pheno <- pheno[sample(1:500, 450), ] obj1.outfile.txt.select.1.tmp <- tempfile() pheno <- pheno[pheno$id <= 400, ] expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, kins <- example$GRM infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select = select, infile.header.print = c("SNP", "Allele1", id = "id", family = binomial(link = "logit"), method = "REML", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") method.optim = "AI") unlink(obj1.outfile.txt.select.1.tmp) select <- match(1:400, unique(obj1$id_include)) obj1.outfile.txt1.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = txtfile1, outfile = obj1.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) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") expect_equal(obj1.txt.select.1, obj1.txt1.select.1) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.txt2.select.1 <- tempfile() obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) "Allele2")) obj1.outfile.bgen.noselect.1 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.txt2.select.1)) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, skip_on_cran() header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) id = "id", family = binomial(link = "logit"), method = "REML", unlink(obj1.outfile.bgen.noselect.1.tmp) method.optim = "AI") obj1.outfile.bgen.select.1 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select[is.na(select)] <- 0 select = select, outfile = obj1.outfile.bgen.select.1) obj2.outfile.bed.noselect.1 <- tempfile() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj2.outfile.bed.select.1 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, "VAR", "PVAL")]) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) quietly = TRUE)) { obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) outfile = obj2.outfile.bgen.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, ncores = 2) select = select, outfile = obj2.outfile.bgen.select.1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) unlink(obj1.outfile.gds.noselect.1.tmp) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.outfile.gds.select.1 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, "VAR", "PVAL")]) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) quietly = TRUE)) { expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) 0.986534857))) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) header = TRUE, as.is = TRUE) } obj2.outfile.gds.select.1 <- tempfile() obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) } obj1.outfile.txt.select.1.tmp <- tempfile() obj2.outfile.txt.select.1 <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") "Allele2")) unlink(obj1.outfile.txt.select.1.tmp) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt.select.1, obj1.txt2.select.1) "Allele2")) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.outfile.txt2.select.1)) idx <- sample(nrow(pheno)) skip_on_cran() pheno <- pheno[idx, ] obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 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(obj2$id_include)) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj1.outfile.bed.select.2 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.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, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.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, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", quietly = TRUE)) { "VAR", "PVAL")]) obj1.outfile.gds.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) quietly = TRUE)) { obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.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.gds.noselect.1, obj1.gds.noselect.2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.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) obj2.outfile.gds.select.1 <- tempfile() obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_equal(obj1.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj1.outfile.txt.select.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 0.996996766))) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj2.outfile.txt.select.1 <- tempfile() obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt.select.1, obj1.txt.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt1.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) "Allele2")) obj2.outfile.txt1.select.1 <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt1.select.1) "Allele2")) obj2.outfile.txt2.select.1 <- tempfile() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, "Allele2")) id = "id", family = binomial(link = "logit"), method = "REML", obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$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.2 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.bed.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.outfile.bed.noselect.2 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj2.bed.select.1, obj2.bed.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj2.outfile.bgen.select.2 <- tempfile() obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.2) outfile = obj1.outfile.bgen.noselect.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bgen.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.noselect.2 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.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.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.outfile.gds.noselect.2 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.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.noselect.1, obj1.gds.noselect.2) expect_equal(obj2.gds.select.1, obj2.gds.select.2) } obj1.outfile.gds.select.2 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.gds.select.1, obj1.gds.select.2) "Allele2")) } obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, idx <- sample(nrow(kins)) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) kins <- kins[idx, idx] obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, 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.txt2.select.1, obj1.txt2.select.2) method.optim = "AI") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select <- match(1:400, unique(obj1$id_include)) id = "id", family = binomial(link = "logit"), method = "REML", select[is.na(select)] <- 0 method.optim = "AI") obj1.outfile.bed.noselect.3 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) select[is.na(select)] <- 0 obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.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.noselect.1, obj2.bed.noselect.2) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.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.select.1, obj1.bed.select.3) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1, obj2.bed.select.2) outfile = obj1.outfile.bgen.noselect.3) obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, outfile = obj2.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, 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.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.2 <- 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.2) header = TRUE, as.is = TRUE) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.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(obj2.bgen.select.1, obj2.bgen.select.2) 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.2 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.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.noselect.1, obj2.gds.noselect.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj2.outfile.gds.select.2 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.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.select.1, obj1.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.outfile.txt.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj2.outfile.txt.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", "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.2 <- read.table(obj2.outfile.txt.select.2, 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.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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.2 <- read.table(obj2.outfile.txt1.select.2, 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.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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.2 <- read.table(obj2.outfile.txt2.select.2, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) method.optim = "AI") idx <- sample(nrow(kins)) select <- match(1:400, unique(obj2$id_include)) kins <- kins[idx, idx] select[is.na(select)] <- 0 obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.bed.noselect.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) method.optim = "AI") obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.outfile.bgen.noselect.3 <- tempfile() obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) outfile = obj1.outfile.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj1.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) select = select, outfile = obj1.outfile.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(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.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.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 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.3 <- read.table(obj2.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt.select.3) "Allele2")) obj2.outfile.txt1.select.3 <- tempfile() obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(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.3 <- read.table(obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) "Allele2")) obj2.outfile.txt2.select.3 <- tempfile() obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 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.3 <- read.table(obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) "Allele2")) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.outfile.txt2.select.1)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, method.optim = "AI") obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, select <- match(1:400, unique(obj2$id_include)) obj1.outfile.txt2.select.2)) select[is.na(select)] <- 0 unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.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.noselect.1, obj2.bed.noselect.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj1.outfile.txt2.select.3)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt2.select.3)) outfile = obj2.outfile.bgen.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) quietly = TRUE)) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj2.outfile.bgen.select.3 <- tempfile() obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, select = select, outfile = obj2.outfile.bgen.select.3) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) header = TRUE, as.is = TRUE)}) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 33: quietly = TRUE)) {eval(code, test_env) obj2.outfile.gds.noselect.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)34: obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)35: obj2.outfile.gds.select.3 <- tempfile()withCallingHandlers({ 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, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj2.gds.select.1, obj2.gds.select.3) skip("On CRAN") } } obj2.outfile.txt.select.3 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, skip_empty() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, expectation = handle_expectation, packageNotFoundError = function(e) { "Allele2")) if (on_cran()) { obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt.select.3)}, snapshot_on_cran = function(cnd) { obj2.outfile.txt1.select.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, invokeRestart("muffle_cran_snapshot") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2")) error = handle_error, interrupt = handle_interrupt) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3,
header = TRUE, as.is = TRUE)36: expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.outfile.txt2.select.3 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 37: select = select, infile.header.print = c("SNP", "Allele1", tryCatchOne(expr, names, parentenv, handlers[[1L]]) "Allele2"))
obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, 38: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)
unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, 39: obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, tryCatch(withCallingHandlers({ obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, eval(code, test_env) obj2.outfile.txt2.select.1)) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, if (snapshot_skipped) { obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, skip("On CRAN") obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, } obj1.outfile.txt2.select.2)) else if (!new_expectations && skip_on_empty) { unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, skip_empty() obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, } obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.txt2.select.2)) if (on_cran()) { unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, } obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, }, snapshot_on_cran = function(cnd) { obj1.outfile.txt2.select.3)) snapshot_skipped <<- TRUE unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, invokeRestart("muffle_cran_snapshot") obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.3)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) 40: unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, doWithOneRestart(return(expr), restart) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2,
obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, 41: obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, withOneRestart(expr, restarts[[1L]]) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
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:
doWithOneRestart(return(expr), restart)52:
test_files_serial(test_dir = test_dir, test_package = test_package, 41: test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, withOneRestart(expr, restarts[[1L]]) env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,
desc = desc, load_package = load_package, shuffle = shuffle, 42: error_call = error_call)withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)53: new_expectations <- the$test_expectations > starting_expectationstest_files(test_dir = path, test_paths = test_paths, test_package = package, if (snapshot_skipped) { reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, skip("On CRAN") load_package = load_package, parallel = parallel, shuffle = shuffle) }
else if (!new_expectations && skip_on_empty) {54: skip_empty()test_dir("testthat", package = package, reporter = reporter, } ..., load_package = "installed")}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {55: skip(paste0("{", e$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