Last updated on 2026-11-10 17:52:41 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.11 | 246.51 | 291.62 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 40.16 | 205.56 | 245.72 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 26.00 | 141.72 | 167.72 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | OK | ||||
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 15.00 | 76.00 | 91.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 85.00 | 389.00 | 474.00 | 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$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
22: eval(code, test_env)
23: withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
27: tryCatch(withCallingHandlers({ eval(code, test_env)
Traceback:
1: new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) {eval(c.expr, envir = args, enclos = envir)
skip("On CRAN") 2: } else if (!new_expectations && skip_on_empty) {eval(c.expr, envir = args, enclos = envir) skip_empty()
3: }doTryCatch(return(expr), name, parentenv, handler)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) { 4: skip(paste0("{", e$package, "} is not installed."))tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
}, snapshot_on_cran = function(cnd) { 5: snapshot_skipped <<- TRUEtryCatchList(expr, classes, parentenv, handlers)
invokeRestart("muffle_cran_snapshot") 6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal) 7:
FUN(X[[i]], ...)28:
doWithOneRestart(return(expr), restart) 8:
lapply(X = S, FUN = FUN, ...)
29: 9: doTryCatch(return(expr), name, parentenv, handler)withOneRestart(expr, restarts[[1L]])
30: 10: withRestarts(tryCatch(withCallingHandlers({tryCatchOne(expr, names, parentenv, handlers[[1L]]) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN")11: }tryCatchList(expr, classes, parentenv, handlers)
else if (!new_expectations && skip_on_empty) {12: skip_empty()tryCatch(expr, error = function(e) { } call <- conditionCall(e)}, expectation = handle_expectation, packageNotFoundError = function(e) { if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) if (on_cran()) { call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) skip(paste0("{", e$package, "} is not installed.")) } prefix <- paste("Error in", dcall, ": ") LONG <- 75L}, snapshot_on_cran = function(cnd) { sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") snapshot_skipped <<- TRUE if (is.na(w)) invokeRestart("muffle_cran_snapshot") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], }, skip = handle_skip, warning = handle_warning, message = handle_message, type = "b") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), if (w > LONG) end_test = function() { prefix <- paste0(prefix, "\n ") }) }
else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n")31: .Internal(seterrmessage(msg[1L]))test_code(code, parent.frame()) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)32: .Internal(printDeferredWarnings())test_that("cross-sectional id le 400 binomial", { plinkfiles <- strsplit(system.file("extdata", "geno.bed", } invisible(structure(msg, class = "try-error", condition = e)) package = "GMMAT"), ".bed", fixed = TRUE)[[1]]}) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")
samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")13: gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")try(lapply(X = S, FUN = FUN, ...), silent = TRUE) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")
txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")14: txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) data(example)
suppressWarnings(RNGversion("3.5.0"))15: set.seed(123)FUN(X[[i]], ...) pheno <- rbind(example$pheno, example$pheno[1:100, ])
pheno$id <- 1:50016: pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NAlapply(seq_len(cores), inner.do) pheno$sex[sample(1:500, 20)] <- NA
pheno <- pheno[sample(1:500, 450), ]17: pheno <- pheno[pheno$id <= 400, ]mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, kins <- example$GRM mc.silent = silent, mc.cores = cores) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML",
18: method.optim = "AI")e$fun(obj, substitute(ex), parent.frame(), e$data) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 019: obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)foreach(i = 1:ncores) %dopar% { obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) if (!is.null(obj$P)) { obj1.outfile.bed.noselect.1.tmp <- tempfile() if (bgenInfo$LayoutFlag == 2) { expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") infile, paste0(outfile, "_tmp.", i), center2, unlink(obj1.outfile.bed.noselect.1.tmp) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, obj1.outfile.bed.select.1 <- tempfile() nperbatch, select, threadInfo$begin[i], threadInfo$end[i], glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, 1) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) else { obj1.outfile.bgen.noselect.1 <- tempfile() .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile, paste0(outfile, "_tmp.", i), center2, outfile = obj1.outfile.bgen.noselect.1) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], header = TRUE, as.is = TRUE) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, obj1.outfile.bgen.noselect.1.tmp <- tempfile() 1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) } obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, else { header = TRUE, as.is = TRUE) if (bgenInfo$LayoutFlag == 2) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, unlink(obj1.outfile.bgen.noselect.1.tmp) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.outfile.bgen.select.1 <- tempfile() "_tmp.", i), center2, MAF.range[1], MAF.range[2], glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, miss.cutoff, miss.method, nperbatch, select, select = select, outfile = obj1.outfile.bgen.select.1) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, bgenInfo$N, bgenInfo$CompressionFlag, 1) header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) else { expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj$Sigma_iX, obj$cov, infile, paste0(outfile, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "_tmp.", i), center2, MAF.range[1], MAF.range[2], "VAR", "PVAL")]) miss.cutoff, miss.method, nperbatch, select, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], quietly = TRUE)) { bgenInfo$N, bgenInfo$CompressionFlag, 1) obj1.outfile.gds.noselect.1 <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) } obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, } header = TRUE, as.is = TRUE)
obj1.outfile.gds.noselect.1.tmp <- tempfile()20: glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, ncores = 2) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp,
header = TRUE, as.is = TRUE)21: expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)eval(code, test_env) unlink(obj1.outfile.gds.noselect.1.tmp)
obj1.outfile.gds.select.1 <- tempfile()22: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)eval(code, test_env) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1,
header = TRUE, as.is = TRUE)23: expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))withCallingHandlers({ expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, eval(code, test_env) 0.986534857))) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) if (snapshot_skipped) { } skip("On CRAN") obj1.outfile.txt.select.1 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, else if (!new_expectations && skip_on_empty) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_empty() select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) } obj1.outfile.txt.select.1.tmp <- tempfile()}, snapshot_on_cran = function(cnd) { expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, snapshot_skipped <<- TRUE infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot") select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") error = handle_error, interrupt = handle_interrupt) unlink(obj1.outfile.txt.select.1.tmp)
obj1.outfile.txt1.select.1 <- tempfile()24: glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, doTryCatch(return(expr), name, parentenv, handler) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 25: "Allele2"))tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1,
header = TRUE, as.is = TRUE)26: expect_equal(obj1.txt.select.1, obj1.txt1.select.1)tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.txt2.select.1 <- tempfile()
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 27: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, tryCatch(withCallingHandlers({ select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2")) new_expectations <- the$test_expectations > starting_expectations obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj1.txt.select.1, obj1.txt2.select.1) } unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, else if (!new_expectations && skip_on_empty) { obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, skip_empty() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, } obj1.outfile.txt2.select.1))}, expectation = handle_expectation, packageNotFoundError = function(e) { skip_on_cran() if (on_cran()) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip(paste0("{", e$package, "} is not installed.")) id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI")}, snapshot_on_cran = function(cnd) { select <- match(1:400, unique(obj2$id_include)) snapshot_skipped <<- TRUE select[is.na(select)] <- 0 invokeRestart("muffle_cran_snapshot") obj2.outfile.bed.noselect.1 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1,
header = TRUE, as.is = TRUE)28: obj2.outfile.bed.select.1 <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)
obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, 29: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1)
obj2.outfile.bgen.noselect.1 <- tempfile()30: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, withRestarts(tryCatch(withCallingHandlers({ outfile = obj2.outfile.bgen.noselect.1) eval(code, test_env) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { obj2.outfile.bgen.select.1 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj2.outfile.bgen.select.1) else if (!new_expectations && skip_on_empty) { obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, skip_empty() header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) }}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", if (on_cran()) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, skip(paste0("{", e$package, "} is not installed.")) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", } "VAR", "PVAL")])}, snapshot_on_cran = function(cnd) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") quietly = TRUE)) {}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.gds.noselect.1 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) end_test = function() { obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, }) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1)31: obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, test_code(code, parent.frame()) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1)
32: expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL))test_that("cross-sectional id le 400 binomial", { expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, plinkfiles <- strsplit(system.file("extdata", "geno.bed", 0.996996766))) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] } bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj2.outfile.txt.select.1 <- tempfile() samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") select = select, infile.header.print = c("SNP", "Allele1", txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") "Allele2")) data(example) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, suppressWarnings(RNGversion("3.5.0")) header = TRUE, as.is = TRUE) set.seed(123) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj2.outfile.txt1.select.1 <- tempfile() pheno$id <- 1:500 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, pheno$disease[sample(1:500, 20)] <- NA infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno$age[sample(1:500, 20)] <- NA select = select, infile.header.print = c("SNP", "Allele1", pheno$sex[sample(1:500, 20)] <- NA "Allele2")) pheno <- pheno[sample(1:500, 450), ] obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) pheno <- pheno[pheno$id <= 400, ] expect_equal(obj2.txt.select.1, obj2.txt1.select.1) kins <- example$GRM obj2.outfile.txt2.select.1 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, id = "id", family = binomial(link = "logit"), method = "REML", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, method.optim = "AI") select = select, infile.header.print = c("SNP", "Allele1", select <- match(1:400, unique(obj1$id_include)) "Allele2")) select[is.na(select)] <- 0 obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bed.noselect.1.tmp <- tempfile() method.optim = "AI") expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") select <- match(1:400, unique(obj1$id_include)) unlink(obj1.outfile.bed.noselect.1.tmp) select[is.na(select)] <- 0 obj1.outfile.bed.select.1 <- tempfile() obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.select.1 <- read.table(obj1.outfile.bed.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.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) outfile = obj1.outfile.bgen.noselect.1) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) select = select, outfile = obj1.outfile.bgen.select.2) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, quietly = TRUE)) { c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.outfile.gds.noselect.2 <- tempfile() "VAR", "PVAL")]) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.outfile.gds.select.2 <- tempfile() obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.gds.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_equal(obj1.gds.select.1, obj1.gds.select.2) ncores = 2) } obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(obj1.outfile.gds.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.select.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.txt.select.2 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, select = select, infile.header.print = c("SNP", "Allele1", 0.986534857))) "Allele2")) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.outfile.txt2.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select[is.na(select)] <- 0 unlink(obj1.outfile.txt.select.1.tmp) obj2.outfile.bed.noselect.2 <- tempfile() obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, 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) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) "Allele2")) obj2.outfile.bed.select.2 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.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(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.outfile.bgen.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", outfile = obj2.outfile.bgen.noselect.2) "Allele2")) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj2.outfile.bgen.select.2 <- tempfile() unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, select = select, outfile = obj2.outfile.bgen.select.2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", id = "id", family = binomial(link = "logit"), method = "REML", quietly = TRUE)) { method.optim = "AI") obj2.outfile.gds.noselect.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) select[is.na(select)] <- 0 obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj2.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.outfile.bed.select.1 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) header = TRUE, as.is = TRUE) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, expect_equal(obj2.gds.select.1, obj2.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.txt.select.2 <- tempfile() obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) obj2.outfile.bgen.select.1 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj2.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", select = select, infile.header.print = c("SNP", "Allele1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, "Allele2")) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.gds.noselect.1 <- tempfile() obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.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", obj2.outfile.gds.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.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.gds.noselect.1, obj2.gds.select.1) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) idx <- sample(nrow(kins)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, kins <- kins[idx, idx] 0.996996766))) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, } id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt.select.1 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, select <- match(1:400, unique(obj1$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj2.outfile.txt1.select.1 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.outfile.bgen.select.3 <- 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.3) "Allele2")) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) 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.3 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) id = "id", family = binomial(link = "logit"), method = "REML", obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) select[is.na(select)] <- 0 obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, 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.select.1, obj1.gds.select.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) } obj1.outfile.bed.select.2 <- tempfile() obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, 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")) obj1.outfile.bgen.noselect.2 <- tempfile() obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj1.outfile.bgen.select.2) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj1.outfile.txt2.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.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.txt2.select.1, obj1.txt2.select.3) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.gds.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) method.optim = "AI") obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, 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.2) obj2.outfile.bed.noselect.3 <- tempfile() } glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.outfile.txt.select.2 <- tempfile() obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj1.outfile.txt1.select.2 <- tempfile() obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj2.outfile.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", quietly = TRUE)) { "Allele2")) obj2.outfile.gds.noselect.3 <- tempfile() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.gds.select.3 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) select <- match(1:400, unique(obj2$id_include)) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) } obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj2.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) "Allele2")) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.txt.select.3 <- read.table(obj2.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(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile1, outfile = obj2.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) "Allele2")) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) select = select, outfile = obj2.outfile.bgen.select.2) obj2.outfile.txt2.select.3 <- tempfile() obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.gds.select.2 <- tempfile() obj2.outfile.txt2.select.1)) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.outfile.txt2.select.2)) } unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.txt.select.2 <- tempfile() obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.2)) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, "Allele2")) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3)) expect_equal(obj2.txt.select.1, obj2.txt.select.2) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.3)) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.outfile.txt2.select.2 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))33: obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)34: idx <- sample(nrow(kins))eval(code, test_env) kins <- kins[idx, idx]
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 35: id = "id", family = binomial(link = "logit"), method = "REML", withCallingHandlers({ method.optim = "AI") eval(code, test_env) select <- match(1:400, unique(obj1$id_include)) new_expectations <- the$test_expectations > starting_expectations select[is.na(select)] <- 0 if (snapshot_skipped) { obj1.outfile.bed.noselect.3 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) } obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) } obj1.outfile.bed.select.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) if (on_cran()) { obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1, obj1.bed.select.3)}, snapshot_on_cran = function(cnd) { obj1.outfile.bgen.noselect.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, invokeRestart("muffle_cran_snapshot") outfile = obj1.outfile.bgen.noselect.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, error = handle_error, interrupt = handle_interrupt) header = TRUE, as.is = TRUE)
expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)36: obj1.outfile.bgen.select.3 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
select = select, outfile = obj1.outfile.bgen.select.3)37: obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)38: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", tryCatchList(expr, classes, parentenv, handlers) quietly = TRUE)) {
obj1.outfile.gds.noselect.3 <- tempfile()39: glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)tryCatch(withCallingHandlers({ obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) if (snapshot_skipped) { obj1.outfile.gds.select.3 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) } obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj1.gds.select.1, obj1.gds.select.3) } }}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.txt.select.3 <- tempfile() if (on_cran()) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, skip(paste0("{", e$package, "} is not installed.")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, snapshot_on_cran = function(cnd) { "Allele2")) snapshot_skipped <<- TRUE obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.txt.select.1, obj1.txt.select.3) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.outfile.txt1.select.3 <- tempfile()
glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, 40: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, doWithOneRestart(return(expr), restart) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))41: obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, withOneRestart(expr, restarts[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)42: obj1.outfile.txt2.select.3 <- tempfile()withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, eval(code, test_env) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, new_expectations <- the$test_expectations > starting_expectations select = select, infile.header.print = c("SNP", "Allele1", if (snapshot_skipped) { "Allele2")) skip("On CRAN") obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) skip_empty() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, } id = "id", family = binomial(link = "logit"), method = "REML", }, expectation = handle_expectation, packageNotFoundError = function(e) { method.optim = "AI") if (on_cran()) { select <- match(1:400, unique(obj2$id_include)) skip(paste0("{", e$package, "} is not installed.")) select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) snapshot_skipped <<- TRUE obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) end_test = function() { obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, }) header = TRUE, as.is = TRUE)
expect_equal(obj2.bed.select.1, obj2.bed.select.3)43: obj2.outfile.bgen.noselect.3 <- tempfile()test_code(code = exprs, env = env, reporter = get_reporter() %||% glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, StopReporter$new()) outfile = obj2.outfile.bgen.noselect.3)
obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 44: header = TRUE, as.is = TRUE)source_file(path, env = env(env), desc = desc, shuffle = shuffle, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) error_call = error_call) obj2.outfile.bgen.select.3 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 45: select = select, outfile = obj2.outfile.bgen.select.3)FUN(X[[i]], ...) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3,
header = TRUE, as.is = TRUE)46: expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", error_call = error_call) quietly = TRUE)) {
obj2.outfile.gds.noselect.3 <- tempfile()47: glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)doTryCatch(return(expr), name, parentenv, handler) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3,
header = TRUE, as.is = TRUE)48: expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.outfile.gds.select.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)49: obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, tryCatchList(expr, classes, parentenv, handlers) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.select.1, obj2.gds.select.3)50: }tryCatch(code, testthat_abort_reporter = function(cnd) { obj2.outfile.txt.select.3 <- tempfile() cat(conditionMessage(cnd), "\n") glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, NULL infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))51: obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, with_reporter(reporters$multi, lapply(test_paths, test_one_file, header = TRUE, as.is = TRUE) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) expect_equal(obj2.txt.select.1, obj2.txt.select.3)
obj2.outfile.txt1.select.3 <- tempfile()52: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, test_files_serial(test_dir = test_dir, test_package = test_package, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, select = select, infile.header.print = c("SNP", "Allele1", env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, "Allele2")) desc = desc, load_package = load_package, shuffle = shuffle, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, error_call = error_call) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)53: obj2.outfile.txt2.select.3 <- tempfile()test_files(test_dir = path, test_paths = test_paths, test_package = package, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, reporter = reporter, load_helpers = load_helpers, env = env, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, select = select, infile.header.print = c("SNP", "Allele1", load_package = load_package, parallel = parallel, shuffle = shuffle) "Allele2"))
obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, 54: header = TRUE, as.is = TRUE)test_dir("testthat", package = package, reporter = reporter, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) ..., load_package = "installed") unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,
obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, 55: obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, test_check("GMMAT") obj2.outfile.txt2.select.1))
unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, An irrecoverable exception occurred. R is aborting now ...
obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2)) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, 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.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, 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, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.3)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
33: eval(code, test_env)
34: eval(code, test_env)
35: 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)
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