Last updated on 2026-11-13 02:59:30 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.11 | 246.51 | 291.62 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 37.72 | 233.26 | 270.98 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 158.84 | 188.84 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 72.00 | 391.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 53.75 | 246.68 | 300.43 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | 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 | 68.00 | 314.00 | 382.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 75.00 | 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 | 86.00 | 391.00 | 477.00 | 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,
Traceback:
1) } 1: else {eval(c.expr, envir = args, enclos = envir) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 2: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, eval(c.expr, envir = args, enclos = envir) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 3: 1)doTryCatch(return(expr), name, parentenv, handler) }
} 4: else { if (bgenInfo$LayoutFlag == 2) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], 5: miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], tryCatchList(expr, classes, parentenv, handlers) bgenInfo$N, bgenInfo$CompressionFlag, 1)
} else { 6: .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) obj$Sigma_iX, obj$cov, infile, paste0(outfile,
"_tmp.", i), center2, MAF.range[1], MAF.range[2], 7: miss.cutoff, miss.method, nperbatch, select, FUN(X[[i]], ...) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],
bgenInfo$N, bgenInfo$CompressionFlag, 1) 8: }lapply(X = S, FUN = FUN, ...) }
} 9:
doTryCatch(return(expr), name, parentenv, handler)20:
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 10: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)tryCatchOne(expr, names, parentenv, handlers[[1L]])
21: 11: eval(code, test_env)tryCatchList(expr, classes, parentenv, handlers)
22: 12: eval(code, test_env)tryCatch(expr, error = function(e) {
call <- conditionCall(e)23: if (!is.null(call)) {withCallingHandlers({ if (identical(call[[1L]], quote(doTryCatch))) eval(code, test_env) call <- sys.call(-4L) new_expectations <- the$test_expectations > starting_expectations dcall <- deparse(call, nlines = 1L) if (snapshot_skipped) { skip("On CRAN") prefix <- paste("Error in", dcall, ": ") } LONG <- 75L else if (!new_expectations && skip_on_empty) { sm <- strsplit(conditionMessage(e), "\n")[[1L]] skip_empty() } w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { if (is.na(w)) skip(paste0("{", e$package, "} is not installed.")) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b")}, snapshot_on_cran = function(cnd) { if (w > LONG) snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") prefix <- paste0(prefix, "\n ")}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt) else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")24: .Internal(seterrmessage(msg[1L]))doTryCatch(return(expr), name, parentenv, handler) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)25: .Internal(printDeferredWarnings())tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
invisible(structure(msg, class = "try-error", condition = e))26: })tryCatchList(expr, classes, parentenv, handlers)
13: 27: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)tryCatch(withCallingHandlers({
eval(code, test_env)14: new_expectations <- the$test_expectations > starting_expectationssendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) if (snapshot_skipped) {
skip("On CRAN")15: } else if (!new_expectations && skip_on_empty) {FUN(X[[i]], ...) skip_empty()
}16: }, expectation = handle_expectation, packageNotFoundError = function(e) {lapply(seq_len(cores), inner.do) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))17: }}, snapshot_on_cran = function(cnd) {mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") mc.silent = silent, mc.cores = cores)
18: }, skip = handle_skip, warning = handle_warning, message = handle_message, e$fun(obj, substitute(ex), parent.frame(), e$data) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
19: 28: doWithOneRestart(return(expr), restart)foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) {29: if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, withOneRestart(expr, restarts[[1L]]) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 30: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, withRestarts(tryCatch(withCallingHandlers({ 1) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations else { if (snapshot_skipped) { skip("On CRAN") .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, } else if (!new_expectations && skip_on_empty) { MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], skip_empty() threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}, expectation = handle_expectation, packageNotFoundError = function(e) { } else { if (on_cran()) { if (bgenInfo$LayoutFlag == 2) { skip(paste0("{", e$package, "} is not installed.")) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, } "_tmp.", i), center2, MAF.range[1], MAF.range[2], }, snapshot_on_cran = function(cnd) { miss.cutoff, miss.method, nperbatch, select, snapshot_skipped <<- TRUE threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], invokeRestart("muffle_cran_snapshot") bgenInfo$N, bgenInfo$CompressionFlag, 1)}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal), else { end_test = function() { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, }) obj$Sigma_iX, obj$cov, infile, paste0(outfile,
"_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, 31: threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], test_code(code, parent.frame())
bgenInfo$N, bgenInfo$CompressionFlag, 1)32: }test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", } package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")20: samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")
txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")21: txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")eval(code, test_env) data(example)
suppressWarnings(RNGversion("3.5.0"))22: set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ])eval(code, test_env) pheno$id <- 1:500
pheno$disease[sample(1:500, 20)] <- NA23: pheno$age[sample(1:500, 20)] <- NAwithCallingHandlers({ pheno$sex[sample(1:500, 20)] <- NA eval(code, test_env) pheno <- pheno[sample(1:500, 450), ] new_expectations <- the$test_expectations > starting_expectations pheno <- pheno[pheno$id <= 400, ] if (snapshot_skipped) { kins <- example$GRM skip("On CRAN") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, } id = "id", family = binomial(link = "logit"), method = "REML", else if (!new_expectations && skip_on_empty) { method.optim = "AI") skip_empty() select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.bed.noselect.1 <- tempfile() if (on_cran()) { glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) skip(paste0("{", e$package, "} is not installed.")) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { obj1.outfile.bed.noselect.1.tmp <- tempfile() snapshot_skipped <<- TRUE expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, invokeRestart("muffle_cran_snapshot") ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")}, skip = handle_skip, warning = handle_warning, message = handle_message, unlink(obj1.outfile.bed.noselect.1.tmp) error = handle_error, interrupt = handle_interrupt) obj1.outfile.bed.select.1 <- tempfile()
glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)24: obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)25: obj1.outfile.bgen.noselect.1 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]])
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE)26: obj1.outfile.bgen.noselect.1.tmp <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 27: header = TRUE, as.is = TRUE)tryCatch(withCallingHandlers({ expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) eval(code, test_env) unlink(obj1.outfile.bgen.noselect.1.tmp) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { obj1.outfile.bgen.select.1 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj1.outfile.bgen.select.1) else if (!new_expectations && skip_on_empty) { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", if (on_cran()) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", skip(paste0("{", e$package, "} is not installed.")) "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, snapshot_on_cran = function(cnd) { quietly = TRUE)) { snapshot_skipped <<- TRUE obj1.outfile.gds.noselect.1 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, 28: ncores = 2)doWithOneRestart(return(expr), restart) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp,
header = TRUE, as.is = TRUE)29: expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)withOneRestart(expr, restarts[[1L]]) unlink(obj1.outfile.gds.noselect.1.tmp)
obj1.outfile.gds.select.1 <- tempfile()30: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)withRestarts(tryCatch(withCallingHandlers({ obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) if (snapshot_skipped) { expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) skip("On CRAN") expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, } 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } obj1.outfile.txt.select.1 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, skip_empty() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }}, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, infile.header.print = c("SNP", "Allele1", if (on_cran()) { "Allele2")) skip(paste0("{", e$package, "} is not installed.")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) snapshot_skipped <<- TRUE obj1.outfile.txt.select.1.tmp <- tempfile() invokeRestart("muffle_cran_snapshot") expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, }, skip = handle_skip, warning = handle_warning, message = handle_message, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() end_test = function() { glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, }) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 31: "Allele2"))test_code(code, parent.frame())
obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, 32: header = TRUE, as.is = TRUE)test_that("cross-sectional id le 400 binomial", { expect_equal(obj1.txt.select.1, obj1.txt1.select.1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.outfile.txt2.select.1 <- tempfile() package = "GMMAT"), ".bed", fixed = TRUE)[[1]] glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") select = select, infile.header.print = c("SNP", "Allele1", gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") "Allele2")) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) suppressWarnings(RNGversion("3.5.0")) skip_on_cran() set.seed(123) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno$id <- 1:500 id = "id", family = binomial(link = "logit"), method = "REML", pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA method.optim = "AI") pheno$sex[sample(1:500, 20)] <- NA select <- match(1:400, unique(obj2$id_include)) pheno <- pheno[sample(1:500, 450), ] select[is.na(select)] <- 0 pheno <- pheno[pheno$id <= 400, ] obj2.outfile.bed.noselect.1 <- tempfile() kins <- example$GRM glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") obj2.outfile.bed.select.1 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) select[is.na(select)] <- 0 obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.noselect.1.tmp <- tempfile() outfile = obj2.outfile.bgen.noselect.1) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.bed.noselect.1.tmp) obj2.outfile.bgen.select.1 <- tempfile() obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) select = select, outfile = obj2.outfile.bgen.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, outfile = obj1.outfile.bgen.noselect.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) unlink(obj1.outfile.bgen.noselect.1.tmp) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) } expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj2.outfile.txt.select.1 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "VAR", "PVAL")]) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, ncores = 2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) unlink(obj1.outfile.gds.noselect.1.tmp) obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, idx <- sample(nrow(pheno)) 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) pheno <- pheno[idx, ] } obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj1.outfile.bed.noselect.2 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.bed.select.2 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.outfile.txt1.select.1 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.select.1, obj1.bed.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj1.outfile.bgen.select.2) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, quietly = TRUE)) { obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.gds.noselect.2 <- tempfile() obj1.outfile.txt2.select.1)) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) skip_on_cran() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) method.optim = "AI") obj1.outfile.gds.select.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt1.select.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj2.outfile.bgen.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj1.outfile.txt2.select.2 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.gds.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) method.optim = "AI") obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, 0.996996766))) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.txt.select.1 <- tempfile() obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.txt1.select.1 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.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(obj2.bgen.noselect.1, obj2.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, select = select, outfile = obj2.outfile.bgen.select.2) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) 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.2 <- tempfile() obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, 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(obj2.txt.select.1, obj2.txt2.select.1) header = TRUE, as.is = TRUE) idx <- sample(nrow(pheno)) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.gds.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, } header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj2.outfile.txt.select.2 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt.select.1, obj2.txt.select.2) outfile = obj1.outfile.bgen.noselect.2) obj2.outfile.txt1.select.2 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, 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.txt1.select.1, obj2.txt1.select.2) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) idx <- sample(nrow(kins)) obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) kins <- kins[idx, idx] obj1.gds.select.2 <- read.table(obj1.outfile.gds.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.gds.select.1, obj1.gds.select.2) method.optim = "AI") } select <- match(1:400, unique(obj1$id_include)) obj1.outfile.txt.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.3) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj1.outfile.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.bgen.select.3 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, select = select, outfile = obj1.outfile.bgen.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj1.outfile.gds.noselect.3 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) id = "id", family = binomial(link = "logit"), method = "REML", obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) select[is.na(select)] <- 0 obj1.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj2.outfile.bed.noselect.2 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, 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.gds.select.1, obj1.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt.select.1, obj1.txt.select.3) outfile = obj2.outfile.bgen.noselect.2) obj1.outfile.txt1.select.3 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.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(obj2.bgen.noselect.1, obj2.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.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) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj1.outfile.txt2.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, quietly = TRUE)) { obj2.outfile.gds.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.gds.select.1, obj2.gds.select.2) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) } select[is.na(select)] <- 0 obj2.outfile.txt.select.2 <- tempfile() obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.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.bed.noselect.1, obj2.bed.noselect.3) "Allele2")) obj2.outfile.bed.select.3 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, outfile = obj2.outfile.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.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.txt1.select.1, obj2.txt1.select.2) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj2.outfile.txt2.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) "Allele2")) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) idx <- sample(nrow(kins)) obj2.outfile.gds.select.3 <- tempfile() kins <- kins[idx, idx] glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.gds.select.1, obj2.gds.select.3) select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0 obj2.outfile.txt.select.3 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.outfile.txt1.select.3 <- tempfile() obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) "Allele2")) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, select = select, outfile = obj1.outfile.bgen.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj1.outfile.gds.noselect.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.txt2.select.2)) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) } obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj1.outfile.txt.select.3 <- tempfile() obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj2.outfile.txt2.select.2)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, "Allele2")) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.txt.select.3 <- read.table(obj1.outfile.txt.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(obj1.txt.select.1, obj1.txt.select.3) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj1.outfile.txt1.select.3 <- tempfile() obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.outfile.txt2.select.3)) 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")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.txt2.select.3 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))33: obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)34: obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, eval(code, test_env) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI") select <- match(1:400, unique(obj2$id_include))35: select[is.na(select)] <- 0withCallingHandlers({ eval(code, test_env) obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) new_expectations <- the$test_expectations > starting_expectations obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, if (snapshot_skipped) { header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) skip("On CRAN") obj2.outfile.bed.select.3 <- tempfile() } glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) else if (!new_expectations && skip_on_empty) { obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.select.1, obj2.bed.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.bgen.noselect.3 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, skip(paste0("{", e$package, "} is not installed.")) outfile = obj2.outfile.bgen.noselect.3) } obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) invokeRestart("muffle_cran_snapshot") obj2.outfile.bgen.select.3 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, error = handle_error, interrupt = handle_interrupt) select = select, outfile = obj2.outfile.bgen.select.3)
obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, 36: header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)doTryCatch(return(expr), name, parentenv, handler) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {37: obj2.outfile.gds.noselect.3 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)
obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, 38: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)
obj2.outfile.gds.select.3 <- tempfile()39: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj2.gds.select.1, obj2.gds.select.3) new_expectations <- the$test_expectations > starting_expectations } obj2.outfile.txt.select.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.txt.select.1, obj2.txt.select.3) if (on_cran()) { obj2.outfile.txt1.select.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, snapshot_on_cran = function(cnd) { select = select, infile.header.print = c("SNP", "Allele1", snapshot_skipped <<- TRUE "Allele2")) invokeRestart("muffle_cran_snapshot") obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)
obj2.outfile.txt2.select.3 <- tempfile()40: glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", doWithOneRestart(return(expr), restart) "Allele2"))
obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, 41: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]])
expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)42: unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, withRestarts(tryCatch(withCallingHandlers({ obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, eval(code, test_env) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { obj2.outfile.txt2.select.1)) skip("On CRAN") unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, } obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, else if (!new_expectations && skip_on_empty) { obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, skip_empty() obj1.outfile.txt2.select.2)) }}, expectation = handle_expectation, packageNotFoundError = function(e) { unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, if (on_cran()) { obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, } obj2.outfile.txt2.select.2))}, snapshot_on_cran = function(cnd) { unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, snapshot_skipped <<- TRUE obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, invokeRestart("muffle_cran_snapshot") obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.txt2.select.3)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, end_test = function() { obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, }) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3,
obj2.outfile.txt2.select.3))43: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new()) quietly = TRUE))
unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, 44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, error_call = error_call) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2,
obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, 45: obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))FUN(X[[i]], ...)
})46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,
error_call = error_call)33:
eval(code, test_env)47:
doTryCatch(return(expr), name, parentenv, handler)34:
eval(code, test_env)48:
tryCatchOne(expr, names, parentenv, handlers[[1L]])35: withCallingHandlers({
eval(code, test_env)49: new_expectations <- the$test_expectations > starting_expectationstryCatchList(expr, classes, parentenv, handlers) if (snapshot_skipped) {
skip("On CRAN")50: tryCatch(code, testthat_abort_reporter = function(cnd) { } cat(conditionMessage(cnd), "\n") else if (!new_expectations && skip_on_empty) { NULL skip_empty()}) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {51: if (on_cran()) {with_reporter(reporters$multi, lapply(test_paths, test_one_file, skip(paste0("{", e$package, "} is not installed.")) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) }
}, snapshot_on_cran = function(cnd) {52: snapshot_skipped <<- TRUEtest_files_serial(test_dir = test_dir, test_package = test_package, invokeRestart("muffle_cran_snapshot") test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, }, skip = handle_skip, warning = handle_warning, message = handle_message, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, error = handle_error, interrupt = handle_interrupt) desc = desc, load_package = load_package, shuffle = shuffle,
error_call = error_call)36:
doTryCatch(return(expr), name, parentenv, handler)53:
test_files(test_dir = path, test_paths = test_paths, test_package = package, 37: reporter = reporter, load_helpers = load_helpers, env = env, tryCatchOne(expr, names, parentenv, handlers[[1L]]) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,
load_package = load_package, parallel = parallel, shuffle = shuffle)
38: 54: tryCatchList(expr, classes, parentenv, handlers)test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
39: 55: tryCatch(withCallingHandlers({test_check("GMMAT") eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectationsAn irrecoverable exception occurred. R is aborting now ...
if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$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