Last updated on 2026-10-23 00:56:16 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.86 | 234.23 | 278.09 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.74 | 224.56 | 264.30 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 154.08 | 184.08 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 163.82 | 205.82 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 392.00 | 462.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.18 | 226.93 | 278.11 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 52.75 | 232.73 | 285.48 | 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 | 66.00 | 309.00 | 375.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 73.00 | 89.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 91.00 | 410.00 | 501.00 | 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$package, "} is not installed.")) }
Traceback:
}, snapshot_on_cran = function(cnd) { 1: snapshot_skipped <<- TRUEeval(c.expr, envir = args, enclos = envir) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 2: error = handle_error, interrupt = handle_interrupt)eval(c.expr, envir = args, enclos = envir)
3: 24: doTryCatch(return(expr), name, parentenv, handler)
doTryCatch(return(expr), name, parentenv, handler) 4:
tryCatchOne(expr, names, parentenv, handlers[[1L]])25:
tryCatchOne(expr, names, parentenv, handlers[[1L]]) 5:
tryCatchList(expr, classes, parentenv, handlers)26:
tryCatchList(expr, classes, parentenv, handlers) 6:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)27:
tryCatch(withCallingHandlers({ 7: FUN(X[[i]], ...) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations 8: if (snapshot_skipped) {lapply(X = S, FUN = FUN, ...) skip("On CRAN")
} 9: else if (!new_expectations && skip_on_empty) {doTryCatch(return(expr), name, parentenv, handler) skip_empty()
}10: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))11: }tryCatchList(expr, classes, parentenv, handlers)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE12: invokeRestart("muffle_cran_snapshot")tryCatch(expr, error = function(e) {}, skip = handle_skip, warning = handle_warning, message = handle_message, call <- conditionCall(e) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) if (!is.null(call)) {
28: if (identical(call[[1L]], quote(doTryCatch))) doWithOneRestart(return(expr), restart) call <- sys.call(-4L)
dcall <- deparse(call, nlines = 1L)29: prefix <- paste("Error in", dcall, ": ")withOneRestart(expr, restarts[[1L]]) LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]30: w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")withRestarts(tryCatch(withCallingHandlers({ if (is.na(w)) eval(code, test_env) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], new_expectations <- the$test_expectations > starting_expectations type = "b") if (snapshot_skipped) { if (w > LONG) skip("On CRAN") prefix <- paste0(prefix, "\n ") } } else if (!new_expectations && skip_on_empty) { else prefix <- "Error : " skip_empty() } msg <- paste0(prefix, conditionMessage(e), "\n")}, expectation = handle_expectation, packageNotFoundError = function(e) { .Internal(seterrmessage(msg[1L])) if (on_cran()) { if (!silent && isTRUE(getOption("show.error.messages"))) { skip(paste0("{", e$package, "} is not installed.")) cat(msg, file = outFile) } .Internal(printDeferredWarnings())}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE invisible(structure(msg, class = "try-error", condition = e))}) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 13: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), try(lapply(X = S, FUN = FUN, ...), silent = TRUE) end_test = function() {
})14:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))31:
test_code(code, parent.frame())15:
FUN(X[[i]], ...)32:
test_that("cross-sectional id le 400 binomial", {16: plinkfiles <- strsplit(system.file("extdata", "geno.bed", lapply(seq_len(cores), inner.do) package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
17: bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") mc.silent = silent, mc.cores = cores)
gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")18: e$fun(obj, substitute(ex), parent.frame(), e$data) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")
data(example)19: suppressWarnings(RNGversion("3.5.0"))foreach(i = 1:ncores) %dopar% { set.seed(123) if (!is.null(obj$P)) { pheno <- rbind(example$pheno, example$pheno[1:100, ]) if (bgenInfo$LayoutFlag == 2) { pheno$id <- 1:500 .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, pheno$disease[sample(1:500, 20)] <- NA infile, paste0(outfile, "_tmp.", i), center2, pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, kins <- example$GRM 1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, } id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") else { select <- match(1:400, unique(obj1$id_include)) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, select[is.na(select)] <- 0 infile, paste0(outfile, "_tmp.", i), center2, obj1.outfile.bed.noselect.1 <- tempfile() MAF.range[1], MAF.range[2], miss.cutoff, miss.method, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() } 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.") else { unlink(obj1.outfile.bed.noselect.1.tmp) if (bgenInfo$LayoutFlag == 2) { obj1.outfile.bed.select.1 <- tempfile() .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, "_tmp.", i), center2, MAF.range[1], MAF.range[2], header = TRUE, as.is = TRUE) miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() bgenInfo$N, bgenInfo$CompressionFlag, 1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) } obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, else { header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj$Sigma_iX, obj$cov, infile, paste0(outfile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, miss.cutoff, miss.method, nperbatch, select, header = TRUE, as.is = TRUE) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) bgenInfo$N, bgenInfo$CompressionFlag, 1) unlink(obj1.outfile.bgen.noselect.1.tmp) } obj1.outfile.bgen.select.1 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj1.outfile.bgen.select.1)
obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, 20: header = TRUE, as.is = TRUE)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1",
"A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, 21: c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", eval(code, test_env) "VAR", "PVAL")])
22: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", eval(code, test_env) quietly = TRUE)) {
obj1.outfile.gds.noselect.1 <- tempfile()23: glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)withCallingHandlers({ obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.gds.noselect.1.tmp <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, skip("On CRAN") ncores = 2) } obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) } unlink(obj1.outfile.gds.noselect.1.tmp)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.gds.select.1 <- tempfile() if (on_cran()) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) skip(paste0("{", e$package, "} is not installed.")) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, }}, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, error = handle_error, interrupt = handle_interrupt)
0.986534857)))24: unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))doTryCatch(return(expr), name, parentenv, handler) }
obj1.outfile.txt.select.1 <- tempfile()25: glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, tryCatchOne(expr, names, parentenv, handlers[[1L]]) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 26: "Allele2"))tryCatchList(expr, classes, parentenv, handlers) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1,
header = TRUE, as.is = TRUE)27: expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)tryCatch(withCallingHandlers({ obj1.outfile.txt.select.1.tmp <- tempfile() eval(code, test_env) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, new_expectations <- the$test_expectations > starting_expectations select = select, infile.header.print = c("SNP", "Allele1", if (snapshot_skipped) { "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") skip("On CRAN") unlink(obj1.outfile.txt.select.1.tmp) } obj1.outfile.txt1.select.1 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, skip_empty() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, expectation = handle_expectation, packageNotFoundError = function(e) { "Allele2")) if (on_cran()) { obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj1.txt.select.1, obj1.txt1.select.1)}, snapshot_on_cran = function(cnd) { obj1.outfile.txt2.select.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, invokeRestart("muffle_cran_snapshot") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, skip = handle_skip, warning = handle_warning, message = handle_message, select = select, infile.header.print = c("SNP", "Allele1", error = handle_error, interrupt = handle_interrupt), error = handle_fatal) "Allele2"))
obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, 28: header = TRUE, as.is = TRUE)doWithOneRestart(return(expr), restart) expect_equal(obj1.txt.select.1, obj1.txt2.select.1)
unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, 29: obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, withOneRestart(expr, restarts[[1L]]) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1,
obj1.outfile.txt2.select.1))30: skip_on_cran()withRestarts(tryCatch(withCallingHandlers({ obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, eval(code, test_env) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") new_expectations <- the$test_expectations > starting_expectations select <- match(1:400, unique(obj2$id_include)) if (snapshot_skipped) { select[is.na(select)] <- 0 skip("On CRAN") obj2.outfile.bed.noselect.1 <- tempfile() } glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() obj2.outfile.bed.select.1 <- tempfile() } glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) } obj2.outfile.bgen.noselect.1 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE outfile = obj2.outfile.bgen.noselect.1) invokeRestart("muffle_cran_snapshot") obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, end_test = function() { select = select, outfile = obj2.outfile.bgen.select.1) })
obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE)31: expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)test_code(code, parent.frame()) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1",
"A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, 32: c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", test_that("cross-sectional id le 400 binomial", { "VAR", "PVAL")]) plinkfiles <- strsplit(system.file("extdata", "geno.bed", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] quietly = TRUE)) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj2.outfile.gds.noselect.1 <- tempfile() samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj2.outfile.gds.select.1 <- tempfile() txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, data(example) header = TRUE, as.is = TRUE) suppressWarnings(RNGversion("3.5.0")) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) set.seed(123) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) pheno <- rbind(example$pheno, example$pheno[1:100, ]) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA 0.996996766))) pheno$age[sample(1:500, 20)] <- NA } pheno$sex[sample(1:500, 20)] <- NA obj2.outfile.txt.select.1 <- tempfile() pheno <- pheno[sample(1:500, 450), ] glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- pheno[pheno$id <= 400, ] select = select, infile.header.print = c("SNP", "Allele1", kins <- example$GRM "Allele2")) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) "Allele2")) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt1.select.1) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj2.outfile.txt2.select.1 <- tempfile() ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, unlink(obj1.outfile.bed.noselect.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) "Allele2")) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() idx <- sample(nrow(pheno)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, pheno <- pheno[idx, ] outfile = obj1.outfile.bgen.noselect.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) obj1.outfile.bgen.noselect.1.tmp <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.noselect.2 <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.outfile.bed.select.2 <- tempfile() unlink(obj1.outfile.bgen.noselect.1.tmp) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.outfile.bgen.select.1 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) outfile = obj1.outfile.bgen.noselect.2) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, header = TRUE, as.is = TRUE) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) "VAR", "PVAL")]) obj1.outfile.bgen.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { select = select, outfile = obj1.outfile.bgen.select.2) obj1.outfile.gds.noselect.1 <- tempfile() obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.1.tmp <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj1.outfile.gds.noselect.2 <- tempfile() ncores = 2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.gds.select.2 <- tempfile() obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.gds.select.2 <- read.table(obj1.outfile.gds.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.gds.select.1, obj1.gds.select.2) 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(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.outfile.txt.select.2 <- tempfile() 0.986534857))) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt.select.1, obj1.txt.select.2) "Allele2")) obj1.outfile.txt1.select.2 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.1.tmp <- tempfile() "Allele2")) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, unlink(obj1.outfile.txt.select.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.outfile.bed.noselect.2 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.bgen.noselect.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, method.optim = "AI") outfile = obj2.outfile.bgen.noselect.2) select <- match(1:400, unique(obj2$id_include)) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.1 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.outfile.bgen.select.2 <- tempfile() obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) obj2.outfile.bed.select.1 <- tempfile() obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.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.bgen.select.1, obj2.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) quietly = TRUE)) { obj2.outfile.bgen.noselect.1 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) outfile = obj2.outfile.bgen.noselect.1) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.bgen.select.1 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) select = select, outfile = obj2.outfile.bgen.select.1) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) } expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.txt1.select.2 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) "Allele2")) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, 0.996996766))) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) "Allele2")) idx <- sample(nrow(kins)) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, kins <- kins[idx, idx] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) method.optim = "AI") obj2.outfile.txt1.select.1 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) "Allele2")) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1, obj1.bed.select.3) "Allele2")) obj1.outfile.bgen.noselect.3 <- tempfile() obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.3) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, id = "id", family = binomial(link = "logit"), method = "REML", select = select, outfile = obj1.outfile.bgen.select.3) method.optim = "AI") obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj1.outfile.bed.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) quietly = TRUE)) { obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj1.outfile.gds.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.outfile.gds.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.select.1, obj1.gds.select.3) } outfile = obj1.outfile.bgen.noselect.2) obj1.outfile.txt.select.3 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj1.bgen.select.1, obj1.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")) obj1.outfile.gds.noselect.2 <- tempfile() obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.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(obj1.txt1.select.1, obj1.txt1.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj1.outfile.gds.select.2 <- 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.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, expect_equal(obj1.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.outfile.txt.select.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, id = "id", family = binomial(link = "logit"), method = "REML", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, method.optim = "AI") select = select, infile.header.print = c("SNP", "Allele1", select <- match(1:400, unique(obj2$id_include)) "Allele2")) select[is.na(select)] <- 0 obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj2.outfile.bed.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt2.select.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.3 <- tempfile() "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.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(obj2.bgen.select.1, obj2.bgen.select.3) method.optim = "AI") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { select <- match(1:400, unique(obj2$id_include)) obj2.outfile.gds.noselect.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.outfile.bed.noselect.2 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.outfile.bed.select.2 <- tempfile() obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.gds.select.1, obj2.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.outfile.txt.select.3 <- tempfile() obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 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.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt1.select.3 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, 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.txt1.select.3 <- read.table(obj2.outfile.txt1.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.txt1.select.1, obj2.txt1.select.3) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, "Allele2")) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, "Allele2")) obj2.outfile.txt2.select.1)) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj2.outfile.txt1.select.2 <- tempfile() obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj1.outfile.txt2.select.2)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, "Allele2")) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj2.outfile.txt2.select.2 <- tempfile() obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj1.outfile.txt2.select.3)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, "Allele2")) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.outfile.txt2.select.3)) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) quietly = TRUE)) idx <- sample(nrow(kins)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, kins <- kins[idx, idx] obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) method.optim = "AI")}) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 033: obj1.outfile.bed.noselect.3 <- tempfile()eval(code, test_env) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3)
obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, 34: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3)
35: obj1.outfile.bed.select.3 <- tempfile()withCallingHandlers({ glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) eval(code, test_env) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj1.bed.select.1, obj1.bed.select.3) skip("On CRAN") } obj1.outfile.bgen.noselect.3 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip_empty() outfile = obj1.outfile.bgen.noselect.3) } obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.bgen.select.3 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3)}, snapshot_on_cran = function(cnd) { obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) error = handle_error, interrupt = handle_interrupt) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {36: obj1.outfile.gds.noselect.3 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)
obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 37: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)
obj1.outfile.gds.select.3 <- tempfile()38: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)tryCatchList(expr, classes, parentenv, handlers) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3,
header = TRUE, as.is = TRUE)39: expect_equal(obj1.gds.select.1, obj1.gds.select.3)tryCatch(withCallingHandlers({ } eval(code, test_env) obj1.outfile.txt.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, 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.txt.select.3 <- read.table(obj1.outfile.txt.select.3, skip_empty() header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) } obj1.outfile.txt1.select.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, if (on_cran()) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip(paste0("{", e$package, "} is not installed.")) select = select, infile.header.print = c("SNP", "Allele1", }}, snapshot_on_cran = function(cnd) { "Allele2")) snapshot_skipped <<- TRUE obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.outfile.txt2.select.3 <- tempfile()
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, 40: doWithOneRestart(return(expr), restart) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 41: "Allele2"))withOneRestart(expr, restarts[[1L]])
obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, 42: header = TRUE, as.is = TRUE)withRestarts(tryCatch(withCallingHandlers({ expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) eval(code, test_env) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, new_expectations <- the$test_expectations > starting_expectations id = "id", family = binomial(link = "logit"), method = "REML", if (snapshot_skipped) { method.optim = "AI") skip("On CRAN") select <- match(1:400, unique(obj2$id_include)) } select[is.na(select)] <- 0 else if (!new_expectations && skip_on_empty) { obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) skip_empty() obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) if (on_cran()) { obj2.outfile.bed.select.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) } obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj2.bed.select.1, obj2.bed.select.3) invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.bgen.noselect.3 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, end_test = function() { outfile = obj2.outfile.bgen.noselect.3) }) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3,
header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)43: obj2.outfile.bgen.select.3 <- tempfile()test_code(code = exprs, env = env, reporter = get_reporter() %||% glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, StopReporter$new()) select = select, outfile = obj2.outfile.bgen.select.3)
obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, 44: header = TRUE, as.is = TRUE)source_file(path, env = env(env), desc = desc, shuffle = shuffle, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) error_call = error_call) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {45: obj2.outfile.gds.noselect.3 <- tempfile()FUN(X[[i]], ...) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)
obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, 46: header = TRUE, as.is = TRUE)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) error_call = error_call) obj2.outfile.gds.select.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)47: obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.select.1, obj2.gds.select.3)48: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.outfile.txt.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 49: tryCatchList(expr, classes, parentenv, handlers) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 50: "Allele2"))tryCatch(code, testthat_abort_reporter = function(cnd) { obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, cat(conditionMessage(cnd), "\n") header = TRUE, as.is = TRUE) NULL expect_equal(obj2.txt.select.1, obj2.txt.select.3)}) obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 51: select = select, infile.header.print = c("SNP", "Allele1", with_reporter(reporters$multi, lapply(test_paths, test_one_file, "Allele2")) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3,
header = TRUE, as.is = TRUE)52: expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, obj2.outfile.txt2.select.3 <- tempfile() env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, desc = desc, load_package = load_package, shuffle = shuffle, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, error_call = error_call) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))53: obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, test_files(test_dir = path, test_paths = test_paths, test_package = package, header = TRUE, as.is = TRUE) reporter = reporter, load_helpers = load_helpers, env = env, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, load_package = load_package, parallel = parallel, shuffle = shuffle) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1,
obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, 54: obj2.outfile.txt2.select.1))test_dir("testthat", package = package, reporter = reporter, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, ..., load_package = "installed") obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2,
obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, 55: obj1.outfile.txt2.select.2))test_check("GMMAT") unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,
obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, An irrecoverable exception occurred. R is aborting now ...
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