Last updated on 2026-10-12 07:52:16 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 44.57 | 239.79 | 284.36 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 40.47 | 210.64 | 251.11 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 156.48 | 186.48 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 41.00 | 168.67 | 209.67 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.86 | 231.73 | 283.59 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 50.71 | 234.78 | 285.49 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 70.00 | 313.00 | 383.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 80.00 | 96.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 402.00 | 488.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16:
Traceback:
1: lapply(seq_len(cores), inner.do)eval(c.expr, envir = args, enclos = envir)
17: 2: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, eval(c.expr, envir = args, enclos = envir) mc.silent = silent, mc.cores = cores)
3: 18: doTryCatch(return(expr), name, parentenv, handler)e$fun(obj, substitute(ex), parent.frame(), e$data)
4: 19: foreach(i = 1:ncores) %dopar% {tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (!is.null(obj$P)) {
if (bgenInfo$LayoutFlag == 2) { 5: .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, tryCatchList(expr, classes, parentenv, handlers) infile, paste0(outfile, "_tmp.", i), center2,
MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 6: nperbatch, select, threadInfo$begin[i], threadInfo$end[i], tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,
1) 7: }FUN(X[[i]], ...) else {
.Call(C_glmm_score_bgen11, as.numeric(res), obj$P, 8: infile, paste0(outfile, "_tmp.", i), center2, lapply(X = S, FUN = FUN, ...) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 9: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, doTryCatch(return(expr), name, parentenv, handler) 1)
}10: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) else {
if (bgenInfo$LayoutFlag == 2) {11: .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, tryCatchList(expr, classes, parentenv, handlers) obj$Sigma_iX, obj$cov, infile, paste0(outfile,
"_tmp.", i), center2, MAF.range[1], MAF.range[2], 12: miss.cutoff, miss.method, nperbatch, select, tryCatch(expr, error = function(e) { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], call <- conditionCall(e) bgenInfo$N, bgenInfo$CompressionFlag, 1) if (!is.null(call)) { } if (identical(call[[1L]], quote(doTryCatch))) else { call <- sys.call(-4L) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, dcall <- deparse(call, nlines = 1L) obj$Sigma_iX, obj$cov, infile, paste0(outfile, prefix <- paste("Error in", dcall, ": ") "_tmp.", i), center2, MAF.range[1], MAF.range[2], LONG <- 75L miss.cutoff, miss.method, nperbatch, select, sm <- strsplit(conditionMessage(e), "\n")[[1L]] threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") bgenInfo$N, bgenInfo$CompressionFlag, 1) if (is.na(w)) } w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b")} if (w > LONG)
prefix <- paste0(prefix, "\n ")20: }glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, else prefix <- "Error : " outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))21: if (!silent && isTRUE(getOption("show.error.messages"))) {eval(code, test_env) cat(msg, file = outFile)
.Internal(printDeferredWarnings())22: }eval(code, test_env) invisible(structure(msg, class = "try-error", condition = e))
})23: withCallingHandlers({
eval(code, test_env)13: new_expectations <- the$test_expectations > starting_expectationstry(lapply(X = S, FUN = FUN, ...), silent = TRUE) if (snapshot_skipped) {
skip("On CRAN")14: }sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) else if (!new_expectations && skip_on_empty) {
15: skip_empty()FUN(X[[i]], ...) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {16: if (on_cran()) {lapply(seq_len(cores), inner.do) skip(paste0("{", e$package, "} is not installed."))
}17: }, snapshot_on_cran = function(cnd) {mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, snapshot_skipped <<- TRUE mc.silent = silent, mc.cores = cores) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 18: error = handle_error, interrupt = handle_interrupt)e$fun(obj, substitute(ex), parent.frame(), e$data)
24:
doTryCatch(return(expr), name, parentenv, handler)19:
foreach(i = 1:ncores) %dopar% {25: if (!is.null(obj$P)) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13, as.numeric(res), obj$P, 26: infile, paste0(outfile, "_tmp.", i), center2, tryCatchList(expr, classes, parentenv, handlers) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 27: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, tryCatch(withCallingHandlers({ 1) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations else { if (snapshot_skipped) { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, skip("On CRAN") infile, paste0(outfile, "_tmp.", i), center2, } MAF.range[1], MAF.range[2], miss.cutoff, miss.method, else if (!new_expectations && skip_on_empty) { nperbatch, select, threadInfo$begin[i], threadInfo$end[i], skip_empty() threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, } 1)}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) else { } if (bgenInfo$LayoutFlag == 2) {}, snapshot_on_cran = function(cnd) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, snapshot_skipped <<- TRUE obj$Sigma_iX, obj$cov, infile, paste0(outfile, invokeRestart("muffle_cran_snapshot") "_tmp.", i), center2, MAF.range[1], MAF.range[2], }, skip = handle_skip, warning = handle_warning, message = handle_message, miss.cutoff, miss.method, nperbatch, select, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],
bgenInfo$N, bgenInfo$CompressionFlag, 1)28: }doWithOneRestart(return(expr), restart) else {
.Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, 29: withOneRestart(expr, restarts[[1L]]) obj$Sigma_iX, obj$cov, infile, paste0(outfile,
"_tmp.", i), center2, MAF.range[1], MAF.range[2], 30: miss.cutoff, miss.method, nperbatch, select, withRestarts(tryCatch(withCallingHandlers({ threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], eval(code, test_env) bgenInfo$N, bgenInfo$CompressionFlag, 1) new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) { } skip("On CRAN")} }
else if (!new_expectations && skip_on_empty) {20: skip_empty()glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {21: skip(paste0("{", e$package, "} is not installed."))eval(code, test_env) }
}, snapshot_on_cran = function(cnd) {22: snapshot_skipped <<- TRUEeval(code, test_env) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 23: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), withCallingHandlers({ end_test = function() { eval(code, test_env) }) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {31: skip("On CRAN")test_code(code, parent.frame()) }
else if (!new_expectations && skip_on_empty) {32: skip_empty()test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", }, expectation = handle_expectation, packageNotFoundError = function(e) { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] if (on_cran()) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") skip(paste0("{", e$package, "} is not installed.")) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") } gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")}, snapshot_on_cran = function(cnd) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") snapshot_skipped <<- TRUE txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") invokeRestart("muffle_cran_snapshot") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")}, skip = handle_skip, warning = handle_warning, message = handle_message, data(example) error = handle_error, interrupt = handle_interrupt) suppressWarnings(RNGversion("3.5.0"))
set.seed(123)24: pheno <- rbind(example$pheno, example$pheno[1:100, ])doTryCatch(return(expr), name, parentenv, handler) pheno$id <- 1:500
pheno$disease[sample(1:500, 20)] <- NA25: pheno$age[sample(1:500, 20)] <- NAtryCatchOne(expr, names, parentenv, handlers[[1L]]) pheno$sex[sample(1:500, 20)] <- NA
pheno <- pheno[sample(1:500, 450), ]26: pheno <- pheno[pheno$id <= 400, ]tryCatchList(expr, classes, parentenv, handlers) kins <- example$GRM
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 27: id = "id", family = binomial(link = "logit"), method = "REML", tryCatch(withCallingHandlers({ method.optim = "AI") eval(code, test_env) select <- match(1:400, unique(obj1$id_include)) new_expectations <- the$test_expectations > starting_expectations select[is.na(select)] <- 0 if (snapshot_skipped) { obj1.outfile.bed.noselect.1 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) } obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() obj1.outfile.bed.noselect.1.tmp <- tempfile() } expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, }, expectation = handle_expectation, packageNotFoundError = function(e) { ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") if (on_cran()) { unlink(obj1.outfile.bed.noselect.1.tmp) skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) } obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) invokeRestart("muffle_cran_snapshot") obj1.outfile.bgen.noselect.1 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) outfile = obj1.outfile.bgen.noselect.1)
obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, 28: header = TRUE, as.is = TRUE)doWithOneRestart(return(expr), restart) obj1.outfile.bgen.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 29: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)withOneRestart(expr, restarts[[1L]]) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp,
header = TRUE, as.is = TRUE)30: expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)withRestarts(tryCatch(withCallingHandlers({ unlink(obj1.outfile.bgen.noselect.1.tmp) eval(code, test_env) obj1.outfile.bgen.select.1 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (snapshot_skipped) { select = select, outfile = obj1.outfile.bgen.select.1) skip("On CRAN") obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) skip_empty() expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", } "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, }, expectation = handle_expectation, packageNotFoundError = function(e) { c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", if (on_cran()) { "VAR", "PVAL")]) skip(paste0("{", e$package, "} is not installed.")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) {}, snapshot_on_cran = function(cnd) { obj1.outfile.gds.noselect.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) invokeRestart("muffle_cran_snapshot") obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.outfile.gds.noselect.1.tmp <- tempfile() end_test = function() { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, }) ncores = 2)
obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, 31: header = TRUE, as.is = TRUE)test_code(code, parent.frame()) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)
unlink(obj1.outfile.gds.noselect.1.tmp)32: obj1.outfile.gds.select.1 <- tempfile()test_that("cross-sectional id le 400 binomial", { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") 0.986534857))) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") } data(example) obj1.outfile.txt.select.1 <- tempfile() suppressWarnings(RNGversion("3.5.0")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, set.seed(123) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- rbind(example$pheno, example$pheno[1:100, ]) select = select, infile.header.print = c("SNP", "Allele1", pheno$id <- 1:500 "Allele2")) pheno$disease[sample(1:500, 20)] <- NA obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, pheno$age[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$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() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj1.outfile.txt2.select.1 <- tempfile() unlink(obj1.outfile.bed.noselect.1.tmp) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj1.outfile.bed.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt2.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, outfile = obj1.outfile.bgen.noselect.1) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1)) obj1.outfile.bgen.noselect.1.tmp <- tempfile() skip_on_cran() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) select[is.na(select)] <- 0 unlink(obj1.outfile.bgen.noselect.1.tmp) obj2.outfile.bed.noselect.1 <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, select = select, outfile = obj1.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj2.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bgen.noselect.1 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj2.outfile.bgen.noselect.1) quietly = TRUE)) { obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj2.outfile.bgen.select.1 <- tempfile() obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", unlink(obj1.outfile.gds.noselect.1.tmp) "VAR", "PVAL")]) obj1.outfile.gds.select.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) quietly = TRUE)) { obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, 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.select.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj2.outfile.gds.select.1 <- tempfile() 0.986534857))) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, select = select, infile.header.print = c("SNP", "Allele1", 0.996996766))) "Allele2")) } obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj2.outfile.txt.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) unlink(obj1.outfile.txt.select.1.tmp) obj2.outfile.txt1.select.1 <- tempfile() obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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")) "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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")) "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) idx <- sample(nrow(pheno)) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, pheno <- pheno[idx, ] obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt2.select.1)) method.optim = "AI") skip_on_cran() select <- match(1:400, unique(obj1$id_include)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bed.noselect.2 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) select <- match(1:400, unique(obj2$id_include)) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj1.outfile.bed.select.2 <- tempfile() obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) outfile = obj1.outfile.bgen.noselect.2) obj2.outfile.bgen.noselect.1 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.1 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select = select, outfile = obj2.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) quietly = TRUE)) { expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.outfile.gds.noselect.2 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, "VAR", "PVAL")]) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) quietly = TRUE)) { obj1.outfile.gds.select.2 <- tempfile() obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj2.outfile.gds.select.1 <- tempfile() } glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1.outfile.txt.select.2 <- tempfile() obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) "Allele2")) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, 0.996996766))) header = TRUE, as.is = TRUE) } expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.outfile.txt.select.1 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.outfile.txt1.select.1 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.outfile.txt2.select.1 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, id = "id", family = binomial(link = "logit"), method = "REML", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, method.optim = "AI") select = select, infile.header.print = c("SNP", "Allele1", select <- match(1:400, unique(obj2$id_include)) "Allele2")) select[is.na(select)] <- 0 obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj2.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.bed.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) method.optim = "AI") obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.outfile.bed.noselect.2 <- tempfile() obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, 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.bed.noselect.1, obj1.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.2 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj2.outfile.bgen.select.2 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) outfile = obj1.outfile.bgen.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.2) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj2.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.outfile.gds.noselect.2 <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.outfile.txt.select.2 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj2.outfile.txt1.select.2 <- tempfile() } glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj1.outfile.txt.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj1.outfile.txt1.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) kins <- kins[idx, idx] expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt2.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, 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.3 <- tempfile() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) method.optim = "AI") obj1.outfile.bed.select.3 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) select[is.na(select)] <- 0 obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.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.select.1, obj1.bed.select.3) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) outfile = obj1.outfile.bgen.noselect.3) obj2.outfile.bed.select.2 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.bgen.select.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) select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.outfile.gds.noselect.3 <- tempfile() obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.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.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) } obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj1.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) "Allele2")) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj2.outfile.txt1.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt2.select.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, select <- match(1:400, unique(obj2$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.noselect.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) idx <- sample(nrow(kins)) obj2.outfile.bed.select.3 <- tempfile() kins <- kins[idx, idx] glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bed.select.1, obj2.bed.select.3) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.bgen.noselect.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.3) obj1.outfile.bed.noselect.3 <- tempfile() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.3 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.3 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bed.select.1, obj1.bed.select.3) quietly = TRUE)) { obj1.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) outfile = obj1.outfile.bgen.noselect.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.outfile.gds.select.3 <- tempfile() obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, select = select, outfile = obj1.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, expect_equal(obj2.gds.select.1, obj2.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.outfile.txt.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) "Allele2")) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj1.outfile.gds.select.3 <- tempfile() obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj1.gds.select.3 <- read.table(obj1.outfile.gds.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(obj1.gds.select.1, obj1.gds.select.3) "Allele2")) } obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj1.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.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.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, "Allele2")) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt2.select.3 <- tempfile() obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj1.outfile.txt2.select.2)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, "Allele2")) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt2.select.3)) id = "id", family = binomial(link = "logit"), method = "REML", unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, method.optim = "AI") obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, select <- match(1:400, unique(obj2$id_include)) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, select[is.na(select)] <- 0 obj2.outfile.txt2.select.3)) obj2.outfile.bed.noselect.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) quietly = TRUE)) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, }) header = TRUE, as.is = TRUE)
expect_equal(obj2.bed.select.1, obj2.bed.select.3)33: obj2.outfile.bgen.noselect.3 <- tempfile()eval(code, test_env) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj2.outfile.bgen.noselect.3)34: obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)35: obj2.outfile.bgen.select.3 <- tempfile()withCallingHandlers({ glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, eval(code, test_env) select = select, outfile = obj2.outfile.bgen.select.3) new_expectations <- the$test_expectations > starting_expectations obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", else if (!new_expectations && skip_on_empty) { quietly = TRUE)) { skip_empty() obj2.outfile.gds.noselect.3 <- tempfile() } glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) } obj2.outfile.gds.select.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) snapshot_skipped <<- TRUE obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj2.gds.select.1, obj2.gds.select.3) error = handle_error, interrupt = handle_interrupt) }
obj2.outfile.txt.select.3 <- tempfile()36: glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, doTryCatch(return(expr), name, parentenv, handler) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 37: "Allele2"))tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3,
header = TRUE, as.is = TRUE)38: expect_equal(obj2.txt.select.1, obj2.txt.select.3)tryCatchList(expr, classes, parentenv, handlers) obj2.outfile.txt1.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, 39: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, tryCatch(withCallingHandlers({ select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) else if (!new_expectations && skip_on_empty) { obj2.outfile.txt2.select.3 <- tempfile() skip_empty() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, infile.header.print = c("SNP", "Allele1", if (on_cran()) { "Allele2")) skip(paste0("{", e$package, "} is not installed.")) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) snapshot_skipped <<- TRUE unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, invokeRestart("muffle_cran_snapshot") obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.txt2.select.1)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2,
obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, 40: doWithOneRestart(return(expr), restart) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2,
obj1.outfile.txt2.select.2))41: withOneRestart(expr, restarts[[1L]]) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,
obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, 42: obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, withRestarts(tryCatch(withCallingHandlers({ obj2.outfile.txt2.select.2)) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, eval(code, test_env) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.3)) if (snapshot_skipped) { unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, skip("On CRAN") obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, } obj2.outfile.txt2.select.3)) else if (!new_expectations && skip_on_empty) { skip_empty() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }}, expectation = handle_expectation, packageNotFoundError = function(e) { quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, if (on_cran()) { obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, } obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, }, snapshot_on_cran = function(cnd) { obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) 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), 33: eval(code, test_env) end_test = function() {
})34: eval(code, test_env)
43:
test_code(code = exprs, env = env, reporter = get_reporter() %||% 35: StopReporter$new())
44: withCallingHandlers({ eval(code, test_env)source_file(path, env = env(env), desc = desc, shuffle = shuffle, new_expectations <- the$test_expectations > starting_expectations error_call = error_call)
45: if (snapshot_skipped) {FUN(X[[i]], ...)
skip("On CRAN") }46: else if (!new_expectations && skip_on_empty) {lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, skip_empty() error_call = error_call)
47: }doTryCatch(return(expr), name, parentenv, handler)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {48: skip(paste0("{", e$package, "} is not installed."))tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
49: }, snapshot_on_cran = function(cnd) {tryCatchList(expr, classes, parentenv, handlers) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")50: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatch(code, testthat_abort_reporter = function(cnd) { error = handle_error, interrupt = handle_interrupt) cat(conditionMessage(cnd), "\n")
NULL36: })
51: doTryCatch(return(expr), name, parentenv, handler)with_reporter(reporters$multi, lapply(test_paths, test_one_file,
env = env, desc = desc, shuffle = shuffle, error_call = error_call))37:
tryCatchOne(expr, names, parentenv, handlers[[1L]])
52: 38: test_files_serial(test_dir = test_dir, test_package = test_package, tryCatchList(expr, classes, parentenv, handlers) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 39: desc = desc, load_package = load_package, shuffle = shuffle, tryCatch(withCallingHandlers({ eval(code, test_env) error_call = error_call) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {53: skip("On CRAN")test_files(test_dir = path, test_paths = test_paths, test_package = package, } reporter = reporter, load_helpers = load_helpers, env = env, else if (!new_expectations && skip_on_empty) { stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, skip_empty() load_package = load_package, parallel = parallel, shuffle = shuffle) }
54: }, expectation = handle_expectation, packageNotFoundError = function(e) {test_dir("testthat", package = package, reporter = reporter, if (on_cran()) { ..., load_package = "installed") skip(paste0("{", e$package, "} is not installed."))
}55: }, snapshot_on_cran = function(cnd) {test_check("GMMAT") snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")An irrecoverable exception occurred. R is aborting now ...
}, 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