Last updated on 2026-10-29 01:57:54 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 42.90 | 242.49 | 285.39 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.08 | 204.75 | 243.83 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 154.08 | 184.08 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 165.37 | 207.37 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.18 | 226.93 | 278.11 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 51.51 | 238.45 | 289.96 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 77.00 | 303.00 | 380.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 76.00 | 92.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 89.00 | 386.00 | 475.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/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$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
Traceback:
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1: 1) } else {eval(c.expr, envir = args, enclos = envir) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 2: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, eval(c.expr, envir = args, enclos = envir) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 3: 1)doTryCatch(return(expr), name, parentenv, handler) }
} 4: else {tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, 5: obj$Sigma_iX, obj$cov, infile, paste0(outfile, tryCatchList(expr, classes, parentenv, handlers) "_tmp.", i), center2, MAF.range[1], MAF.range[2],
miss.cutoff, miss.method, nperbatch, select, 6: threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) bgenInfo$N, bgenInfo$CompressionFlag, 1)
} 7: else {FUN(X[[i]], ...) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 8: "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, lapply(X = S, FUN = FUN, ...) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],
bgenInfo$N, bgenInfo$CompressionFlag, 1) 9: }doTryCatch(return(expr), name, parentenv, handler) }}
10: 20: tryCatchOne(expr, names, parentenv, handlers[[1L]])glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)11:
tryCatchList(expr, classes, parentenv, handlers)21:
eval(code, test_env)12:
tryCatch(expr, error = function(e) {22: call <- conditionCall(e)eval(code, test_env) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 23: call <- sys.call(-4L)withCallingHandlers({ dcall <- deparse(call, nlines = 1L) eval(code, test_env) prefix <- paste("Error in", dcall, ": ") new_expectations <- the$test_expectations > starting_expectations LONG <- 75L if (snapshot_skipped) { sm <- strsplit(conditionMessage(e), "\n")[[1L]] skip("On CRAN") } w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") else if (!new_expectations && skip_on_empty) { if (is.na(w)) skip_empty() } w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], }, expectation = handle_expectation, packageNotFoundError = function(e) { type = "b") if (on_cran()) { if (w > LONG) skip(paste0("{", e$package, "} is not installed.")) prefix <- paste0(prefix, "\n ") } }}, snapshot_on_cran = function(cnd) { else prefix <- "Error : " snapshot_skipped <<- TRUE msg <- paste0(prefix, conditionMessage(e), "\n") invokeRestart("muffle_cran_snapshot") .Internal(seterrmessage(msg[1L]))}, skip = handle_skip, warning = handle_warning, message = handle_message, if (!silent && isTRUE(getOption("show.error.messages"))) { error = handle_error, interrupt = handle_interrupt) cat(msg, file = outFile)
.Internal(printDeferredWarnings())24: }doTryCatch(return(expr), name, parentenv, handler) invisible(structure(msg, class = "try-error", condition = e))
})25:
tryCatchOne(expr, names, parentenv, handlers[[1L]])
13: 26: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)tryCatchList(expr, classes, parentenv, handlers)
14: 27: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))tryCatch(withCallingHandlers({
eval(code, test_env)15: new_expectations <- the$test_expectations > starting_expectationsFUN(X[[i]], ...) if (snapshot_skipped) {
skip("On CRAN")16: lapply(seq_len(cores), inner.do) }
else if (!new_expectations && skip_on_empty) {17: skip_empty() }mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, }, expectation = handle_expectation, packageNotFoundError = function(e) { mc.silent = silent, mc.cores = cores) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))18: }e$fun(obj, substitute(ex), parent.frame(), e$data)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE19: invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, foreach(i = 1:ncores) %dopar% { error = handle_error, interrupt = handle_interrupt), error = handle_fatal) if (!is.null(obj$P)) {
if (bgenInfo$LayoutFlag == 2) {28: .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, doWithOneRestart(return(expr), restart) infile, paste0(outfile, "_tmp.", i), center2,
MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 29: nperbatch, select, threadInfo$begin[i], threadInfo$end[i], withOneRestart(expr, restarts[[1L]]) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) }
else {30: withRestarts(tryCatch(withCallingHandlers({ .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, eval(code, test_env) infile, paste0(outfile, "_tmp.", i), center2, new_expectations <- the$test_expectations > starting_expectations MAF.range[1], MAF.range[2], miss.cutoff, miss.method, if (snapshot_skipped) { skip("On CRAN") nperbatch, select, threadInfo$begin[i], threadInfo$end[i], } threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, else if (!new_expectations && skip_on_empty) { 1) skip_empty() } } }}, expectation = handle_expectation, packageNotFoundError = function(e) { else { if (on_cran()) { if (bgenInfo$LayoutFlag == 2) { skip(paste0("{", e$package, "} is not installed.")) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, } obj$Sigma_iX, obj$cov, infile, paste0(outfile, }, snapshot_on_cran = function(cnd) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], snapshot_skipped <<- TRUE miss.cutoff, miss.method, nperbatch, select, invokeRestart("muffle_cran_snapshot") threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], }, skip = handle_skip, warning = handle_warning, message = handle_message, bgenInfo$N, bgenInfo$CompressionFlag, 1) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), } end_test = function() { else { }) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 31: "_tmp.", i), center2, MAF.range[1], MAF.range[2], test_code(code, parent.frame()) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 32: bgenInfo$N, bgenInfo$CompressionFlag, 1)test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", }} package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")20: gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")
txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")21: data(example)eval(code, test_env) suppressWarnings(RNGversion("3.5.0"))
set.seed(123)22: pheno <- rbind(example$pheno, example$pheno[1:100, ])eval(code, test_env) pheno$id <- 1:500
pheno$disease[sample(1:500, 20)] <- NA23: pheno$age[sample(1:500, 20)] <- NAwithCallingHandlers({ pheno$sex[sample(1:500, 20)] <- NA eval(code, test_env) pheno <- pheno[sample(1:500, 450), ] new_expectations <- the$test_expectations > starting_expectations pheno <- pheno[pheno$id <= 400, ] if (snapshot_skipped) { kins <- example$GRM skip("On CRAN") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, } else if (!new_expectations && skip_on_empty) { id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") skip_empty() select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.bed.noselect.1 <- tempfile() if (on_cran()) { glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) skip(paste0("{", e$package, "} is not installed.")) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, } header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile()}, snapshot_on_cran = function(cnd) { expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, snapshot_skipped <<- TRUE ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") invokeRestart("muffle_cran_snapshot") unlink(obj1.outfile.bed.noselect.1.tmp)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt) obj1.outfile.bed.select.1 <- tempfile()
glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)24: obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile()25: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, tryCatchOne(expr, names, parentenv, handlers[[1L]]) outfile = obj1.outfile.bgen.noselect.1)
obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, 26: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.bgen.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 27: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)tryCatch(withCallingHandlers({ obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) if (snapshot_skipped) { unlink(obj1.outfile.bgen.noselect.1.tmp) skip("On CRAN") obj1.outfile.bgen.select.1 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, else if (!new_expectations && skip_on_empty) { select = select, outfile = obj1.outfile.bgen.select.1) skip_empty() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) if (on_cran()) { expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", skip(paste0("{", e$package, "} is not installed.")) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, } c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", }, snapshot_on_cran = function(cnd) { "VAR", "PVAL")]) snapshot_skipped <<- TRUE if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", invokeRestart("muffle_cran_snapshot") quietly = TRUE)) {}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.gds.noselect.1 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)
obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, 28: header = TRUE, as.is = TRUE)doWithOneRestart(return(expr), restart) obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp,
ncores = 2)29: obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, withOneRestart(expr, restarts[[1L]]) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)
unlink(obj1.outfile.gds.noselect.1.tmp)30: obj1.outfile.gds.select.1 <- tempfile()withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) eval(code, test_env) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) skip("On CRAN") expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) } expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, else if (!new_expectations && skip_on_empty) { 0.986534857))) skip_empty() unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } }}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.txt.select.1 <- tempfile() if (on_cran()) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, skip(paste0("{", e$package, "} is not installed.")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, snapshot_on_cran = function(cnd) { "Allele2")) snapshot_skipped <<- TRUE obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.outfile.txt.select.1.tmp <- tempfile() end_test = function() { expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, }) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 31: "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") unlink(obj1.outfile.txt.select.1.tmp)test_code(code, parent.frame()) obj1.outfile.txt1.select.1 <- tempfile()
glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, 32: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", test_that("cross-sectional id le 400 binomial", { "Allele2")) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.txt.select.1, obj1.txt1.select.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj1.outfile.txt2.select.1 <- tempfile() txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") select = select, infile.header.print = c("SNP", "Allele1", data(example) "Allele2")) suppressWarnings(RNGversion("3.5.0")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) pheno$id <- 1:500 unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, pheno$disease[sample(1:500, 20)] <- NA obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, pheno$age[sample(1:500, 20)] <- NA obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] obj1.outfile.txt2.select.1)) skip_on_cran() pheno <- pheno[pheno$id <= 400, ] obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, kins <- example$GRM id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.bed.noselect.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) method.optim = "AI") obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 obj2.outfile.bed.select.1 <- tempfile() obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.noselect.1.tmp <- tempfile() outfile = obj2.outfile.bgen.noselect.1) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.bed.noselect.1.tmp) obj2.outfile.bgen.select.1 <- tempfile() obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) select = select, outfile = obj2.outfile.bgen.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", outfile = obj1.outfile.bgen.noselect.1) "VAR", "PVAL")]) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) unlink(obj1.outfile.bgen.noselect.1.tmp) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) } expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj2.outfile.txt.select.1 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", quietly = TRUE)) { "Allele2")) obj1.outfile.gds.noselect.1 <- tempfile() obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.1 <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, ncores = 2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) unlink(obj1.outfile.gds.noselect.1.tmp) obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, idx <- sample(nrow(pheno)) 0.986534857))) pheno <- pheno[idx, ] unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.noselect.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.bed.select.2 <- tempfile() obj1.outfile.txt.select.1.tmp <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1, obj1.bed.select.2) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.bgen.noselect.2 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt1.select.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) "Allele2")) obj1.outfile.bgen.select.2 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) select = select, outfile = obj1.outfile.bgen.select.2) obj1.outfile.txt2.select.1 <- tempfile() obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.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.select.1, obj1.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.gds.select.2 <- tempfile() obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, } obj1.outfile.txt.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.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.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) "Allele2")) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.2) obj2.bed.select.1 <- read.table(obj2.outfile.bed.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(obj2.bed.noselect.1, obj2.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj2.outfile.bgen.noselect.1) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) "Allele2")) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, "VAR", "PVAL")]) id = "id", family = binomial(link = "logit"), method = "REML", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", method.optim = "AI") quietly = TRUE)) { select <- match(1:400, unique(obj2$id_include)) obj2.outfile.gds.noselect.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, expect_equal(obj2.bed.select.1, obj2.bed.select.2) 0.996996766))) obj2.outfile.bgen.noselect.2 <- tempfile() } obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, outfile = obj2.outfile.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.2) obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt1.select.1) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj2.gds.select.1, obj2.gds.select.2) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) } idx <- sample(nrow(pheno)) obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.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") select <- match(1:400, unique(obj1$id_include)) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj2.outfile.txt1.select.2 <- tempfile() obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt2.select.2 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) "Allele2")) obj1.outfile.bgen.select.2 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) kins <- kins[idx, idx] if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, quietly = TRUE)) { id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) method.optim = "AI") obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.bed.noselect.3 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt.select.1, obj1.txt.select.2) outfile = obj1.outfile.bgen.noselect.3) obj1.outfile.txt1.select.2 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3) "Allele2")) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt2.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.outfile.gds.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.gds.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) method.optim = "AI") obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.bed.noselect.2 <- tempfile() } glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.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.bed.select.1, obj2.bed.select.2) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, outfile = obj2.outfile.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) "Allele2")) obj2.outfile.bgen.select.2 <- tempfile() obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.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.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) "Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.gds.select.1, obj2.gds.select.2) select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.3 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) "Allele2")) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.3) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, outfile = obj2.outfile.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj2.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) quietly = TRUE)) { obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) idx <- sample(nrow(kins)) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) kins <- kins[idx, idx] expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.gds.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) method.optim = "AI") obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj1.outfile.bed.noselect.3 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.txt.select.3 <- tempfile() obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.txt1.select.3 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj1.outfile.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, outfile = obj1.outfile.bgen.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) quietly = TRUE)) { unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj1.outfile.gds.noselect.3 <- tempfile() obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.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.gds.noselect.1, obj1.gds.noselect.3) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.txt2.select.2)) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, } obj2.outfile.txt2.select.2)) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.3)) "Allele2")) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.outfile.txt2.select.3)) obj1.outfile.txt1.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, quietly = TRUE)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, "Allele2")) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)}) obj1.outfile.txt2.select.3 <- tempfile()
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, 33: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1",
34: "Allele2"))eval(code, test_env) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3,
header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)35: obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, withCallingHandlers({ id = "id", family = binomial(link = "logit"), method = "REML", eval(code, test_env) method.optim = "AI") new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 skip("On CRAN") } else if (!new_expectations && skip_on_empty) { obj2.outfile.bed.noselect.3 <- tempfile() skip_empty() } glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, if (on_cran()) { header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.bed.select.3 <- tempfile() } glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3)}, snapshot_on_cran = function(cnd) { obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj2.bed.select.1, obj2.bed.select.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.bgen.noselect.3 <- tempfile() error = handle_error, interrupt = handle_interrupt)
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 36: outfile = obj2.outfile.bgen.noselect.3)doTryCatch(return(expr), name, parentenv, handler) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3,
header = TRUE, as.is = TRUE)37: expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.outfile.bgen.select.3 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 38: select = select, outfile = obj2.outfile.bgen.select.3)tryCatchList(expr, classes, parentenv, handlers) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3,
header = TRUE, as.is = TRUE)39: expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)tryCatch(withCallingHandlers({ if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", eval(code, test_env) quietly = TRUE)) { new_expectations <- the$test_expectations > starting_expectations obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) if (snapshot_skipped) { obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) else if (!new_expectations && skip_on_empty) { obj2.outfile.gds.select.3 <- tempfile() skip_empty() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) } obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.gds.select.1, obj2.gds.select.3) skip(paste0("{", e$package, "} is not installed.")) } } obj2.outfile.txt.select.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, snapshot_skipped <<- TRUE infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, select = select, infile.header.print = c("SNP", "Allele1", error = handle_error, interrupt = handle_interrupt), error = handle_fatal) "Allele2"))
obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, 40: header = TRUE, as.is = TRUE)doWithOneRestart(return(expr), restart) expect_equal(obj2.txt.select.1, obj2.txt.select.3)
obj2.outfile.txt1.select.3 <- tempfile()41: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, withOneRestart(expr, restarts[[1L]]) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 42: "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) if (snapshot_skipped) { obj2.outfile.txt2.select.3 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", skip_empty() "Allele2")) } obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) skip(paste0("{", e$package, "} is not installed.")) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, } obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, }, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, invokeRestart("muffle_cran_snapshot") obj2.outfile.txt2.select.1))}, skip = handle_skip, warning = handle_warning, message = handle_message, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, end_test = function() { obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, }) obj1.outfile.txt2.select.2))
43: unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, test_code(code = exprs, env = env, reporter = get_reporter() %||% obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, StopReporter$new()) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2,
obj2.outfile.txt2.select.2))44: unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, error_call = error_call) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3,
obj1.outfile.txt2.select.3))45: unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, FUN(X[[i]], ...) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3,
obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, 46: obj2.outfile.txt2.select.3))lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, error_call = error_call)
obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, 47: obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, doTryCatch(return(expr), name, parentenv, handler) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3,
obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))48: })tryCatchOne(expr, names, parentenv, handlers[[1L]])
33: 49: eval(code, test_env)
tryCatchList(expr, classes, parentenv, handlers)34:
eval(code, test_env)50:
tryCatch(code, testthat_abort_reporter = function(cnd) {35: cat(conditionMessage(cnd), "\n")withCallingHandlers({ NULL eval(code, test_env)}) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {51: skip("On CRAN")with_reporter(reporters$multi, lapply(test_paths, test_one_file, } env = env, desc = desc, shuffle = shuffle, error_call = error_call)) else if (!new_expectations && skip_on_empty) {
skip_empty()52: }test_files_serial(test_dir = test_dir, test_package = test_package, }, expectation = handle_expectation, packageNotFoundError = function(e) { test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, if (on_cran()) { env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, skip(paste0("{", e$package, "} is not installed.")) } desc = desc, load_package = load_package, shuffle = shuffle, }, snapshot_on_cran = function(cnd) { error_call = error_call) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")53: test_files(test_dir = path, test_paths = test_paths, test_package = package, }, skip = handle_skip, warning = handle_warning, message = handle_message, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, error = handle_error, interrupt = handle_interrupt) load_package = load_package, parallel = parallel, shuffle = shuffle)
36: 54: doTryCatch(return(expr), name, parentenv, handler)test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
37: 55: tryCatchOne(expr, names, parentenv, handlers[[1L]])test_check("GMMAT")
38: tryCatchList(expr, classes, parentenv, handlers)An irrecoverable exception occurred. R is aborting now ...
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