Last updated on 2026-11-01 10:54:32 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.44 | 234.31 | 279.75 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.34 | 215.07 | 253.41 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 158.82 | 187.82 | 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 | 52.77 | 231.81 | 284.58 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 54.79 | 229.43 | 284.22 | 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 | 18.00 | 78.00 | 96.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 89.00 | 382.00 | 471.00 | 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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
27: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")
Traceback:
}, skip = handle_skip, warning = handle_warning, message = handle_message, 1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
3: 28: doTryCatch(return(expr), name, parentenv, handler)doWithOneRestart(return(expr), restart)
4: 29: tryCatchOne(expr, names, parentenv, handlers[[1L]])withOneRestart(expr, restarts[[1L]])
30: 5: tryCatchList(expr, classes, parentenv, handlers)withRestarts(tryCatch(withCallingHandlers({
6: eval(code, test_env)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) { 7: skip("On CRAN")FUN(X[[i]], ...)
} 8: else if (!new_expectations && skip_on_empty) {lapply(X = S, FUN = FUN, ...) skip_empty()
9: }doTryCatch(return(expr), name, parentenv, handler)
}, expectation = handle_expectation, packageNotFoundError = function(e) {10: if (on_cran()) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) skip(paste0("{", e$package, "} is not installed."))
}11: }, snapshot_on_cran = function(cnd) {tryCatchList(expr, classes, parentenv, handlers) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, 12: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), tryCatch(expr, error = function(e) { end_test = function() { call <- conditionCall(e) }) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 31: call <- sys.call(-4L)test_code(code, parent.frame()) dcall <- deparse(call, nlines = 1L)
prefix <- paste("Error in", dcall, ": ")32: LONG <- 75Ltest_that("cross-sectional id le 400 binomial", { sm <- strsplit(conditionMessage(e), "\n")[[1L]] plinkfiles <- strsplit(system.file("extdata", "geno.bed", w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") package = "GMMAT"), ".bed", fixed = TRUE)[[1]] if (is.na(w)) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") type = "b") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") if (w > LONG) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") prefix <- paste0(prefix, "\n ") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") } txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") else prefix <- "Error : " data(example) msg <- paste0(prefix, conditionMessage(e), "\n") suppressWarnings(RNGversion("3.5.0")) .Internal(seterrmessage(msg[1L])) set.seed(123) if (!silent && isTRUE(getOption("show.error.messages"))) { pheno <- rbind(example$pheno, example$pheno[1:100, ]) cat(msg, file = outFile) pheno$id <- 1:500 .Internal(printDeferredWarnings()) pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA } pheno$sex[sample(1:500, 20)] <- NA invisible(structure(msg, class = "try-error", condition = e)) pheno <- pheno[sample(1:500, 450), ]}) pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM
13: obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, try(lapply(X = S, FUN = FUN, ...), silent = TRUE) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI")14: select <- match(1:400, unique(obj1$id_include))sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) select[is.na(select)] <- 0
obj1.outfile.bed.noselect.1 <- tempfile()15: glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)FUN(X[[i]], ...) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1,
header = TRUE, as.is = TRUE)16: lapply(seq_len(cores), inner.do) obj1.outfile.bed.noselect.1.tmp <- tempfile()
expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, 17: ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, unlink(obj1.outfile.bed.noselect.1.tmp) mc.silent = silent, mc.cores = cores) obj1.outfile.bed.select.1 <- tempfile()
glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)18: obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, e$fun(obj, substitute(ex), parent.frame(), e$data) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 19: outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, foreach(i = 1:ncores) %dopar% { header = TRUE, as.is = TRUE) if (!is.null(obj$P)) { obj1.outfile.bgen.noselect.1.tmp <- tempfile() if (bgenInfo$LayoutFlag == 2) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) infile, paste0(outfile, "_tmp.", i), center2, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, header = TRUE, as.is = TRUE) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, unlink(obj1.outfile.bgen.noselect.1.tmp) 1) } obj1.outfile.bgen.select.1 <- tempfile() else { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, infile, paste0(outfile, "_tmp.", i), center2, header = TRUE, as.is = TRUE) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", nperbatch, select, threadInfo$begin[i], threadInfo$end[i], "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 1) "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } } else { quietly = TRUE)) { if (bgenInfo$LayoutFlag == 2) { obj1.outfile.gds.noselect.1 <- tempfile() .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, "_tmp.", i), center2, MAF.range[1], MAF.range[2], header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() miss.cutoff, miss.method, nperbatch, select, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, bgenInfo$N, bgenInfo$CompressionFlag, 1) header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) unlink(obj1.outfile.gds.noselect.1.tmp) else { obj1.outfile.gds.select.1 <- tempfile() .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) "_tmp.", i), center2, MAF.range[1], MAF.range[2], expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) miss.cutoff, miss.method, nperbatch, select, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, bgenInfo$N, bgenInfo$CompressionFlag, 1) 0.986534857))) } unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } } obj1.outfile.txt.select.1 <- tempfile()} glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1,
20: 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.1.tmp, ncores = 2) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))21: obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)22: obj1.outfile.txt.select.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, eval(code, test_env) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 23: "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")withCallingHandlers({ eval(code, test_env) unlink(obj1.outfile.txt.select.1.tmp) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, if (snapshot_skipped) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip("On CRAN") select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) else if (!new_expectations && skip_on_empty) { obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (on_cran()) { select = select, infile.header.print = c("SNP", "Allele1", skip(paste0("{", e$package, "} is not installed.")) } "Allele2"))}, snapshot_on_cran = function(cnd) { obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.txt.select.1, obj1.txt2.select.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, error = handle_error, interrupt = handle_interrupt) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1,
24: obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, doTryCatch(return(expr), name, parentenv, handler) obj1.outfile.txt2.select.1))
skip_on_cran()25: obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, tryCatchOne(expr, names, parentenv, handlers[[1L]]) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI")26: tryCatchList(expr, classes, parentenv, handlers) select <- match(1:400, unique(obj2$id_include))
select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile()27: glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1)tryCatch(withCallingHandlers({ obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, eval(code, test_env) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) if (snapshot_skipped) { obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) else if (!new_expectations && skip_on_empty) { obj2.outfile.bgen.noselect.1 <- tempfile() skip_empty() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, }}, expectation = handle_expectation, packageNotFoundError = function(e) { outfile = obj2.outfile.bgen.noselect.1) if (on_cran()) { obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, skip(paste0("{", e$package, "} is not installed.")) } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { obj2.outfile.bgen.select.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, invokeRestart("muffle_cran_snapshot") select = select, outfile = obj2.outfile.bgen.select.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)
expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", 28: "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, doWithOneRestart(return(expr), restart) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE",
"VAR", "PVAL")])29: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", withOneRestart(expr, restarts[[1L]]) quietly = TRUE)) {
30: obj2.outfile.gds.noselect.1 <- tempfile()withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) eval(code, test_env) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) skip("On CRAN") obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) skip_empty() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) } expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, }, expectation = handle_expectation, packageNotFoundError = function(e) { 0.996996766))) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt.select.1 <- tempfile() } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, }, snapshot_on_cran = function(cnd) { 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")) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, end_test = function() { header = TRUE, as.is = TRUE) }) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL)
obj2.outfile.txt1.select.1 <- tempfile()31: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, test_code(code, parent.frame()) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 32: "Allele2"))test_that("cross-sectional id le 400 binomial", { obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, plinkfiles <- strsplit(system.file("extdata", "geno.bed", header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] obj2.outfile.txt2.select.1 <- tempfile() bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") select = select, infile.header.print = c("SNP", "Allele1", txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") "Allele2")) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") header = TRUE, as.is = TRUE) data(example) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) suppressWarnings(RNGversion("3.5.0")) idx <- sample(nrow(pheno)) set.seed(123) pheno <- pheno[idx, ] pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, pheno$id <- 1:500 id = "id", family = binomial(link = "logit"), method = "REML", pheno$disease[sample(1:500, 20)] <- NA method.optim = "AI") pheno$age[sample(1:500, 20)] <- NA select <- match(1:400, unique(obj1$id_include)) pheno$sex[sample(1:500, 20)] <- NA select[is.na(select)] <- 0 pheno <- pheno[sample(1:500, 450), ] obj1.outfile.bed.noselect.2 <- tempfile() pheno <- pheno[pheno$id <= 400, ] glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) kins <- example$GRM obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) method.optim = "AI") obj1.outfile.bed.select.2 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) select[is.na(select)] <- 0 obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.outfile.bed.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj1.outfile.bgen.select.2 <- tempfile() ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(obj1.outfile.bed.noselect.1.tmp) select = select, outfile = obj1.outfile.bgen.select.2) obj1.outfile.bed.select.1 <- tempfile() obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) header = TRUE, as.is = TRUE) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) quietly = TRUE)) { obj1.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) outfile = obj1.outfile.bgen.noselect.1) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.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.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.select.2 <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) } unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.txt.select.2 <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj1.outfile.bgen.select.1) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.select.1) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, select = select, infile.header.print = c("SNP", "Allele1", c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "Allele2")) "VAR", "PVAL")]) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.outfile.gds.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.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.txt2.select.1, obj1.txt2.select.2) ncores = 2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) method.optim = "AI") unlink(obj1.outfile.gds.noselect.1.tmp) select <- match(1:400, unique(obj2$id_include)) obj1.outfile.gds.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj2.outfile.bed.noselect.2 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj2.outfile.bed.select.2 <- tempfile() 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) 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.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, 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(obj2.bed.select.1, obj2.bed.select.2) "Allele2")) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.outfile.txt.select.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select = select, outfile = obj2.outfile.bgen.select.2) unlink(obj1.outfile.txt.select.1.tmp) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj1.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", quietly = TRUE)) { "Allele2")) obj2.outfile.gds.noselect.2 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 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) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt2.select.1) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, "Allele2")) obj1.outfile.txt2.select.1)) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, skip_on_cran() header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.txt.select.1, obj2.txt.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt1.select.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, select <- match(1:400, unique(obj2$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.outfile.bed.noselect.1 <- tempfile() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, idx <- sample(nrow(kins)) outfile = obj2.outfile.bgen.noselect.1) kins <- kins[idx, idx] obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bgen.select.1 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select <- match(1:400, unique(obj1$id_include)) select = select, outfile = obj2.outfile.bgen.select.1) select[is.na(select)] <- 0 obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.outfile.bed.select.3 <- tempfile() "VAR", "PVAL")]) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.gds.noselect.1 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 0.996996766))) select = select, outfile = obj1.outfile.bgen.select.3) } obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj2.outfile.txt.select.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.1 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.gds.select.3 <- tempfile() 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.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, expect_equal(obj1.gds.select.1, obj1.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.outfile.txt.select.3 <- tempfile() obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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.3 <- read.table(obj1.outfile.txt.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj1.txt.select.1, obj1.txt.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, idx <- sample(nrow(pheno)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- pheno[idx, ] 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") obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) "Allele2")) obj1.outfile.bed.select.2 <- tempfile() obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bed.select.1, obj1.bed.select.2) method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) obj1.outfile.bgen.noselect.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bed.noselect.3 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.outfile.bgen.select.2 <- tempfile() obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) select = select, outfile = obj1.outfile.bgen.select.2) obj2.bed.select.3 <- read.table(obj2.outfile.bed.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(obj2.bed.select.1, obj2.bed.select.3) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.outfile.bgen.noselect.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { outfile = obj2.outfile.bgen.noselect.3) obj1.outfile.gds.noselect.2 <- tempfile() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.3 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.select.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) expect_equal(obj1.gds.select.1, obj1.gds.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } obj1.outfile.txt.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj2.outfile.gds.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, 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.gds.select.1, obj2.gds.select.3) } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.3) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj2.outfile.txt1.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.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.txt1.select.1, obj2.txt1.select.3) obj2.outfile.txt2.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.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.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.bed.select.2 <- tempfile() obj2.outfile.txt2.select.1)) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.bed.select.2 <- read.table(obj2.outfile.bed.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.bed.select.1, obj2.bed.select.2) obj1.outfile.txt2.select.2)) obj2.outfile.bgen.noselect.2 <- tempfile() unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.txt2.select.2)) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2.outfile.bgen.select.2 <- tempfile() obj1.outfile.txt2.select.3)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, select = select, outfile = obj2.outfile.bgen.select.2) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) quietly = TRUE)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, quietly = TRUE)) { obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.outfile.gds.noselect.2 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))}) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2,
header = TRUE, as.is = TRUE)33: expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2)eval(code, test_env) obj2.outfile.gds.select.2 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2)34: obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, eval(code, test_env)
header = TRUE, as.is = TRUE)35: expect_equal(obj2.gds.select.1, obj2.gds.select.2)withCallingHandlers({ } obj2.outfile.txt.select.2 <- tempfile() eval(code, test_env) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, new_expectations <- the$test_expectations > starting_expectations infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", if (snapshot_skipped) { "Allele2")) skip("On CRAN") obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj2.txt.select.1, obj2.txt.select.2) skip_empty() obj2.outfile.txt1.select.2 <- tempfile() } glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (on_cran()) { select = select, infile.header.print = c("SNP", "Allele1", skip(paste0("{", e$package, "} is not installed.")) "Allele2")) } obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) invokeRestart("muffle_cran_snapshot") obj2.outfile.txt2.select.2 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 36: select = select, infile.header.print = c("SNP", "Allele1", doTryCatch(return(expr), name, parentenv, handler) "Allele2"))
obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, 37: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]])
expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)38: idx <- sample(nrow(kins)) kins <- kins[idx, idx]tryCatchList(expr, classes, parentenv, handlers) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 39: method.optim = "AI")tryCatch(withCallingHandlers({ select <- match(1:400, unique(obj1$id_include)) eval(code, test_env) select[is.na(select)] <- 0 new_expectations <- the$test_expectations > starting_expectations obj1.outfile.bed.noselect.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) skip("On CRAN") obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) skip_empty() obj1.outfile.bed.select.3 <- tempfile() } glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.bed.select.1, obj1.bed.select.3) } obj1.outfile.bgen.noselect.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE outfile = obj1.outfile.bgen.noselect.3) invokeRestart("muffle_cran_snapshot") obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)
obj1.outfile.bgen.select.3 <- tempfile()40: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, doWithOneRestart(return(expr), restart) select = select, outfile = obj1.outfile.bgen.select.3)
obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, 41: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 42: quietly = TRUE)) {withRestarts(tryCatch(withCallingHandlers({ obj1.outfile.gds.noselect.3 <- tempfile() eval(code, test_env) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) new_expectations <- the$test_expectations > starting_expectations obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, if (snapshot_skipped) { skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) else if (!new_expectations && skip_on_empty) { obj1.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) skip_empty() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.gds.select.1, obj1.gds.select.3) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, }, snapshot_on_cran = function(cnd) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, snapshot_skipped <<- TRUE select = select, infile.header.print = c("SNP", "Allele1", invokeRestart("muffle_cran_snapshot") "Allele2"))}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), header = TRUE, as.is = TRUE) end_test = function() { }) expect_equal(obj1.txt.select.1, obj1.txt.select.3)
obj1.outfile.txt1.select.3 <- tempfile()43: glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, test_code(code = exprs, env = env, reporter = get_reporter() %||% infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, StopReporter$new()) select = select, infile.header.print = c("SNP", "Allele1", "Allele2"))
obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, 44: header = TRUE, as.is = TRUE)source_file(path, env = env(env), desc = desc, shuffle = shuffle, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt2.select.3 <- tempfile() error_call = error_call) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 45: select = select, infile.header.print = c("SNP", "Allele1", FUN(X[[i]], ...) "Allele2")) 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)46: obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, id = "id", family = binomial(link = "logit"), method = "REML", error_call = error_call) method.optim = "AI")
select <- match(1:400, unique(obj2$id_include))47: select[is.na(select)] <- 0doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.bed.noselect.3 <- tempfile()
glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)48: obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)49: obj2.outfile.bed.select.3 <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3)
obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, 50: header = TRUE, as.is = TRUE)tryCatch(code, testthat_abort_reporter = function(cnd) { expect_equal(obj2.bed.select.1, obj2.bed.select.3) cat(conditionMessage(cnd), "\n") obj2.outfile.bgen.noselect.3 <- tempfile() NULL glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, }) outfile = obj2.outfile.bgen.noselect.3)
obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 51: header = TRUE, as.is = TRUE)with_reporter(reporters$multi, lapply(test_paths, test_one_file, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj2.outfile.bgen.select.3 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 52: select = select, outfile = obj2.outfile.bgen.select.3)test_files_serial(test_dir = test_dir, test_package = test_package, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, header = TRUE, as.is = TRUE) env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) error_call = error_call) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {53: obj2.outfile.gds.noselect.3 <- tempfile()test_files(test_dir = path, test_paths = test_paths, test_package = package, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) reporter = reporter, load_helpers = load_helpers, env = env, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, header = TRUE, as.is = TRUE) load_package = load_package, parallel = parallel, shuffle = shuffle) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)
obj2.outfile.gds.select.3 <- tempfile()54: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)test_dir("testthat", package = package, reporter = reporter, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, ..., load_package = "installed") header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.select.1, obj2.gds.select.3)55: }test_check("GMMAT") obj2.outfile.txt.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, An irrecoverable exception occurred. R is aborting now ...
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, 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