Last updated on 2026-10-13 17:52:27 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.06 | 240.59 | 283.65 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.29 | 207.61 | 246.90 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 156.48 | 186.48 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 168.79 | 210.79 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.86 | 231.73 | 283.59 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 50.71 | 234.78 | 285.49 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 70.00 | 313.00 | 383.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 78.00 | 95.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 402.00 | 488.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
Traceback:
1: eval(c.expr, envir = args, enclos = envir)22:
eval(code, test_env)
2: 23: eval(c.expr, envir = args, enclos = envir)withCallingHandlers({
eval(code, test_env) 3: new_expectations <- the$test_expectations > starting_expectationsdoTryCatch(return(expr), name, parentenv, handler) if (snapshot_skipped) {
skip("On CRAN") 4: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) else if (!new_expectations && skip_on_empty) {
skip_empty() 5: tryCatchList(expr, classes, parentenv, handlers) }
}, expectation = handle_expectation, packageNotFoundError = function(e) { 6: if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) }
}, snapshot_on_cran = function(cnd) { 7: snapshot_skipped <<- TRUEFUN(X[[i]], ...) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 8: error = handle_error, interrupt = handle_interrupt)lapply(X = S, FUN = FUN, ...)
24: 9: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
25: 10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
tryCatchOne(expr, names, parentenv, handlers[[1L]])26:
tryCatchList(expr, classes, parentenv, handlers)11:
tryCatchList(expr, classes, parentenv, handlers)27:
tryCatch(withCallingHandlers({12: eval(code, test_env)tryCatch(expr, error = function(e) { new_expectations <- the$test_expectations > starting_expectations call <- conditionCall(e) if (snapshot_skipped) { if (!is.null(call)) { skip("On CRAN") if (identical(call[[1L]], quote(doTryCatch))) } call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") else if (!new_expectations && skip_on_empty) { LONG <- 75L skip_empty() sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") } if (is.na(w)) }, expectation = handle_expectation, packageNotFoundError = function(e) { w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], if (on_cran()) { type = "b") skip(paste0("{", e$package, "} is not installed.")) if (w > LONG) } prefix <- paste0(prefix, "\n ")}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") else prefix <- "Error : "}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))28: doWithOneRestart(return(expr), restart) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile) .Internal(printDeferredWarnings())29: }withOneRestart(expr, restarts[[1L]]) invisible(structure(msg, class = "try-error", condition = e))
})
30: 13: withRestarts(tryCatch(withCallingHandlers({try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
eval(code, test_env)14: new_expectations <- the$test_expectations > starting_expectationssendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) if (snapshot_skipped) {
skip("On CRAN")15: }FUN(X[[i]], ...) else if (!new_expectations && skip_on_empty) {
skip_empty()16: }lapply(seq_len(cores), inner.do)}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))17: }mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE18: invokeRestart("muffle_cran_snapshot")e$fun(obj, substitute(ex), parent.frame(), e$data)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 19: end_test = function() {foreach(i = 1:ncores) %dopar% { }) if (!is.null(obj$P)) {
if (bgenInfo$LayoutFlag == 2) {31: .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, test_code(code, parent.frame()) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 32: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, test_that("cross-sectional id le 400 binomial", { 1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", } package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") else { samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") infile, paste0(outfile, "_tmp.", i), center2, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") MAF.range[1], MAF.range[2], miss.cutoff, miss.method, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") nperbatch, select, threadInfo$begin[i], threadInfo$end[i], data(example) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, suppressWarnings(RNGversion("3.5.0")) 1) set.seed(123) } pheno <- rbind(example$pheno, example$pheno[1:100, ]) } pheno$id <- 1:500 else { pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA if (bgenInfo$LayoutFlag == 2) { pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, pheno <- pheno[pheno$id <= 400, ] obj$Sigma_iX, obj$cov, infile, paste0(outfile, kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, "_tmp.", i), center2, MAF.range[1], MAF.range[2], id = "id", family = binomial(link = "logit"), method = "REML", miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], method.optim = "AI") bgenInfo$N, bgenInfo$CompressionFlag, 1) select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0 else { obj1.outfile.bed.noselect.1 <- tempfile() .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, "_tmp.", i), center2, MAF.range[1], MAF.range[2], header = TRUE, as.is = TRUE) miss.cutoff, miss.method, nperbatch, select, obj1.outfile.bed.noselect.1.tmp <- tempfile() threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, bgenInfo$N, bgenInfo$CompressionFlag, 1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") } unlink(obj1.outfile.bed.noselect.1.tmp) } obj1.outfile.bed.select.1 <- tempfile()} glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)
obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, 20: header = TRUE, as.is = TRUE)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.bgen.noselect.1 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 21: eval(code, test_env) outfile = obj1.outfile.bgen.noselect.1)
obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, 22: header = TRUE, as.is = TRUE)eval(code, test_env) obj1.outfile.bgen.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 23: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)withCallingHandlers({ obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) 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[, skip(paste0("{", e$package, "} is not installed.")) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", } "VAR", "PVAL")])}, snapshot_on_cran = function(cnd) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", snapshot_skipped <<- TRUE quietly = TRUE)) { invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.noselect.1 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) error = handle_error, interrupt = handle_interrupt) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1,
header = TRUE, as.is = TRUE)24: obj1.outfile.gds.noselect.1.tmp <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp,
ncores = 2)25: obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)26: unlink(obj1.outfile.gds.noselect.1.tmp)tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.gds.select.1 <- tempfile()
glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)27: obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) if (snapshot_skipped) { expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, skip("On CRAN") 0.986534857))) } unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) else if (!new_expectations && skip_on_empty) { } skip_empty() obj1.outfile.txt.select.1 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, }, expectation = handle_expectation, packageNotFoundError = function(e) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (on_cran()) { select = select, infile.header.print = c("SNP", "Allele1", skip(paste0("{", e$package, "} is not installed.")) "Allele2")) } obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) invokeRestart("muffle_cran_snapshot") obj1.outfile.txt.select.1.tmp <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 28: "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")doWithOneRestart(return(expr), restart) unlink(obj1.outfile.txt.select.1.tmp)
obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, 29: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, withOneRestart(expr, restarts[[1L]]) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))30: obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, withRestarts(tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt2.select.1 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.txt.select.1, obj1.txt2.select.1) if (on_cran()) { unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, } obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, }, snapshot_on_cran = function(cnd) { obj1.outfile.txt2.select.1)) snapshot_skipped <<- TRUE skip_on_cran() invokeRestart("muffle_cran_snapshot") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, }, skip = handle_skip, warning = handle_warning, message = handle_message, id = "id", family = binomial(link = "logit"), method = "REML", error = handle_error, interrupt = handle_interrupt), error = handle_fatal), method.optim = "AI") end_test = function() { select <- match(1:400, unique(obj2$id_include)) }) select[is.na(select)] <- 0
obj2.outfile.bed.noselect.1 <- tempfile()31: glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1)test_code(code, parent.frame()) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1,
header = TRUE, as.is = TRUE)32: obj2.outfile.bed.select.1 <- tempfile()test_that("cross-sectional id le 400 binomial", { glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj2.outfile.bgen.noselect.1 <- tempfile() gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") outfile = obj2.outfile.bgen.noselect.1) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, data(example) header = TRUE, as.is = TRUE) suppressWarnings(RNGversion("3.5.0")) obj2.outfile.bgen.select.1 <- tempfile() set.seed(123) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, pheno <- rbind(example$pheno, example$pheno[1:100, ]) select = select, outfile = obj2.outfile.bgen.select.1) pheno$id <- 1:500 obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, pheno$disease[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$age[sample(1:500, 20)] <- NA expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) pheno$sex[sample(1:500, 20)] <- NA expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", pheno <- pheno[sample(1:500, 450), ] "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, pheno <- pheno[pheno$id <= 400, ] c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", kins <- example$GRM "VAR", "PVAL")]) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", id = "id", family = binomial(link = "logit"), method = "REML", quietly = TRUE)) { method.optim = "AI") obj2.outfile.gds.noselect.1 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) select[is.na(select)] <- 0 obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj2.outfile.gds.select.1 <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.outfile.bed.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) unlink(obj1.outfile.bed.noselect.1.tmp) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) 0.996996766))) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1.outfile.bgen.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", outfile = obj1.outfile.bgen.noselect.1) "Allele2")) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt1.select.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 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.bgen.noselect.1, obj1.bgen.noselect.1.tmp) "Allele2")) unlink(obj1.outfile.bgen.noselect.1.tmp) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) select = select, outfile = obj1.outfile.bgen.select.1) obj2.outfile.txt2.select.1 <- tempfile() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "Allele2")) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", idx <- sample(nrow(pheno)) quietly = TRUE)) { pheno <- pheno[idx, ] obj1.outfile.gds.noselect.1 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) id = "id", family = binomial(link = "logit"), method = "REML", obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) obj1.outfile.gds.noselect.1.tmp <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj1.outfile.bed.noselect.2 <- tempfile() ncores = 2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.bed.select.2 <- tempfile() obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, outfile = obj1.outfile.bgen.noselect.2) 0.986534857))) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.outfile.bgen.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj1.outfile.bgen.select.2) "Allele2")) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt.select.1.tmp <- tempfile() quietly = TRUE)) { expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.outfile.gds.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.txt.select.1.tmp) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.txt1.select.1 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt1.select.1) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.txt2.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, "Allele2")) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, 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(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.outfile.bed.noselect.1 <- tempfile() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.outfile.bed.select.1 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) select[is.na(select)] <- 0 obj2.outfile.bgen.noselect.1 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) select = select, outfile = obj2.outfile.bgen.select.1) obj2.outfile.bed.select.2 <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) 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(obj2.bed.select.1, obj2.bed.select.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bgen.noselect.2 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj2.outfile.bgen.noselect.2) quietly = TRUE)) { obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.select.1 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) quietly = TRUE)) { expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) 0.996996766))) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) } obj2.outfile.txt.select.2 <- tempfile() obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.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.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.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.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj2.outfile.txt2.select.2 <- tempfile() idx <- sample(nrow(pheno)) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, pheno <- pheno[idx, ] 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")) method.optim = "AI") obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1.outfile.bed.noselect.2 <- tempfile() idx <- sample(nrow(kins)) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) kins <- kins[idx, idx] obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) method.optim = "AI") obj1.outfile.bed.select.2 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) select[is.na(select)] <- 0 obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, expect_equal(obj1.bed.select.1, obj1.bed.select.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.3 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2) outfile = obj1.outfile.bgen.noselect.3) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bgen.select.3 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.2 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.gds.noselect.3 <- tempfile() obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, expect_equal(obj1.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) } obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, 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")) } obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.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(obj1.txt.select.1, obj1.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt.select.1, obj1.txt.select.3) "Allele2")) obj1.outfile.txt1.select.3 <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) "Allele2")) obj1.outfile.txt2.select.3 <- tempfile() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, "Allele2")) id = "id", family = binomial(link = "logit"), method = "REML", obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) select[is.na(select)] <- 0 obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.bed.noselect.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) method.optim = "AI") obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.outfile.bed.select.3 <- tempfile() obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, outfile = obj2.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, expect_equal(obj2.bed.select.1, obj2.bed.select.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.outfile.bgen.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.gds.noselect.2 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) quietly = TRUE)) { obj2.outfile.gds.select.2 <- tempfile() obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.txt.select.2 <- tempfile() obj2.outfile.gds.select.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.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", obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.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.3 <- read.table(obj2.outfile.txt1.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, idx <- sample(nrow(kins)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, kins <- kins[idx, idx] select = select, infile.header.print = c("SNP", "Allele1", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, "Allele2")) id = "id", family = binomial(link = "logit"), method = "REML", obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) select[is.na(select)] <- 0 unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj1.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.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.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.txt2.select.2)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.txt2.select.2)) obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, outfile = obj1.outfile.bgen.noselect.3) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3)) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.txt2.select.3)) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, quietly = TRUE)) { obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.gds.noselect.3 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, }) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)33: obj1.outfile.gds.select.3 <- tempfile()eval(code, test_env) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)
obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, 34: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj1.gds.select.1, obj1.gds.select.3)
}35: obj1.outfile.txt.select.3 <- tempfile()withCallingHandlers({ glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, eval(code, test_env) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", new_expectations <- the$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.txt.select.1, obj1.txt.select.3) else if (!new_expectations && skip_on_empty) { obj1.outfile.txt1.select.3 <- tempfile() skip_empty() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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$package, "} is not installed.")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) snapshot_skipped <<- TRUE obj1.outfile.txt2.select.3 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, error = handle_error, interrupt = handle_interrupt) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))36: obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)37: obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, tryCatchOne(expr, names, parentenv, handlers[[1L]]) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI")38: select <- match(1:400, unique(obj2$id_include))tryCatchList(expr, classes, parentenv, handlers) select[is.na(select)] <- 0
obj2.outfile.bed.noselect.3 <- tempfile()39: glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)tryCatch(withCallingHandlers({ obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) skip("On CRAN") obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj2.bed.select.1, obj2.bed.select.3) skip_empty() obj2.outfile.bgen.noselect.3 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, }, expectation = handle_expectation, packageNotFoundError = function(e) { outfile = obj2.outfile.bgen.noselect.3) if (on_cran()) { obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) } obj2.outfile.bgen.select.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE select = select, outfile = obj2.outfile.bgen.select.3) invokeRestart("muffle_cran_snapshot") obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.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.bgen.select.1, obj2.bgen.select.3)
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 40: quietly = TRUE)) {doWithOneRestart(return(expr), restart) obj2.outfile.gds.noselect.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)41: obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, withOneRestart(expr, restarts[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)42: obj2.outfile.gds.select.3 <- tempfile()withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) eval(code, test_env) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj2.gds.select.1, obj2.gds.select.3) skip("On CRAN") } } obj2.outfile.txt.select.3 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, skip_empty() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { "Allele2")) skip(paste0("{", e$package, "} is not installed.")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj2.txt.select.1, obj2.txt.select.3) snapshot_skipped <<- TRUE obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, invokeRestart("muffle_cran_snapshot") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), select = select, infile.header.print = c("SNP", "Allele1", end_test = function() { "Allele2")) }) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3,
header = TRUE, as.is = TRUE)43: expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)test_code(code = exprs, env = env, reporter = get_reporter() %||% obj2.outfile.txt2.select.3 <- tempfile() StopReporter$new()) 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", 44: "Allele2"))source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, error_call = error_call) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, 45: obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, FUN(X[[i]], ...) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1,
obj2.outfile.txt2.select.1))46: unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, error_call = error_call) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2,
obj1.outfile.txt2.select.2))47: unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2,
obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, 48: obj2.outfile.txt2.select.2))tryCatchOne(expr, names, parentenv, handlers[[1L]]) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3,
obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, 49: obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3,
obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, 50: obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, tryCatch(code, testthat_abort_reporter = function(cnd) { obj2.outfile.txt2.select.3)) cat(conditionMessage(cnd), "\n") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", NULL quietly = TRUE)) }) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1,
obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, 51: obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, with_reporter(reporters$multi, lapply(test_paths, test_one_file, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))
})52:
test_files_serial(test_dir = test_dir, test_package = test_package, 33: test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, eval(code, test_env) env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,
desc = desc, load_package = load_package, shuffle = shuffle, 34: error_call = error_call)eval(code, test_env)
53: 35: test_files(test_dir = path, test_paths = test_paths, test_package = package, withCallingHandlers({ reporter = reporter, load_helpers = load_helpers, env = env, eval(code, test_env) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, new_expectations <- the$test_expectations > starting_expectations load_package = load_package, parallel = parallel, shuffle = shuffle) if (snapshot_skipped) {
skip("On CRAN")54: }test_dir("testthat", package = package, reporter = reporter, else if (!new_expectations && skip_on_empty) { ..., load_package = "installed") skip_empty()
}55: }, expectation = handle_expectation, packageNotFoundError = function(e) {test_check("GMMAT") if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))An irrecoverable exception occurred. R is aborting now ...
}}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64