Last updated on 2026-11-14 12:50:25 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.58 | 240.25 | 283.83 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.92 | 215.31 | 254.23 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 158.84 | 188.84 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 72.00 | 391.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 53.75 | 246.68 | 300.43 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 68.00 | 314.00 | 382.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | ERROR | ||||
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 391.00 | 477.00 | 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:
Traceback:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) 1: eval(c.expr, envir = args, enclos = envir)
15:
2: FUN(X[[i]], ...)eval(c.expr, envir = args, enclos = envir)
16:
3: lapply(seq_len(cores), inner.do)
doTryCatch(return(expr), name, parentenv, handler)
4: 17: tryCatchOne(expr, names, parentenv, handlers[[1L]])mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed,
5: mc.silent = silent, mc.cores = cores)
tryCatchList(expr, classes, parentenv, handlers)18:
e$fun(obj, substitute(ex), parent.frame(), e$data) 6:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)19:
foreach(i = 1:ncores) %dopar% { 7: if (!is.null(obj$P)) {FUN(X[[i]], ...) if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13, as.numeric(res), obj$P, 8: infile, paste0(outfile, "_tmp.", i), center2, lapply(X = S, FUN = FUN, ...) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
9: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, doTryCatch(return(expr), name, parentenv, handler) 1)
}10: else {tryCatchOne(expr, names, parentenv, handlers[[1L]]) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 11: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, tryCatchList(expr, classes, parentenv, handlers) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1)
}12: }tryCatch(expr, error = function(e) { else { call <- conditionCall(e) if (bgenInfo$LayoutFlag == 2) { if (!is.null(call)) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, if (identical(call[[1L]], quote(doTryCatch))) obj$Sigma_iX, obj$cov, infile, paste0(outfile, call <- sys.call(-4L) "_tmp.", i), center2, MAF.range[1], MAF.range[2], dcall <- deparse(call, nlines = 1L) miss.cutoff, miss.method, nperbatch, select, prefix <- paste("Error in", dcall, ": ") threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] bgenInfo$N, bgenInfo$CompressionFlag, 1) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") } if (is.na(w)) else { w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, type = "b") obj$Sigma_iX, obj$cov, infile, paste0(outfile, if (w > LONG) "_tmp.", i), center2, MAF.range[1], MAF.range[2], prefix <- paste0(prefix, "\n ") miss.cutoff, miss.method, nperbatch, select, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], else prefix <- "Error : " bgenInfo$N, bgenInfo$CompressionFlag, 1) msg <- paste0(prefix, conditionMessage(e), "\n") } .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) {} cat(msg, file = outFile)
.Internal(printDeferredWarnings())20: }glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, invisible(structure(msg, class = "try-error", condition = e)) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)})
21: 13: eval(code, test_env)try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
22: 14: eval(code, test_env)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
23: 15: withCallingHandlers({FUN(X[[i]], ...) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations16: if (snapshot_skipped) {lapply(seq_len(cores), inner.do) skip("On CRAN")
}17: else if (!new_expectations && skip_on_empty) {mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, skip_empty() mc.silent = silent, mc.cores = cores) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {18: if (on_cran()) {e$fun(obj, substitute(ex), parent.frame(), e$data) skip(paste0("{", e$package, "} is not installed."))
}19: }, snapshot_on_cran = function(cnd) {foreach(i = 1:ncores) %dopar% { snapshot_skipped <<- TRUE if (!is.null(obj$P)) { invokeRestart("muffle_cran_snapshot") if (bgenInfo$LayoutFlag == 2) {}, skip = handle_skip, warning = handle_warning, message = handle_message, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, error = handle_error, interrupt = handle_interrupt) infile, paste0(outfile, "_tmp.", i), center2,
MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 24: nperbatch, select, threadInfo$begin[i], threadInfo$end[i], doTryCatch(return(expr), name, parentenv, handler) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,
1)25: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) else {
.Call(C_glmm_score_bgen11, as.numeric(res), obj$P, 26: infile, paste0(outfile, "_tmp.", i), center2, tryCatchList(expr, classes, parentenv, handlers) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 27: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, tryCatch(withCallingHandlers({ 1) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) { } skip("On CRAN") else { } if (bgenInfo$LayoutFlag == 2) { else if (!new_expectations && skip_on_empty) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, skip_empty() obj$Sigma_iX, obj$cov, infile, paste0(outfile, } "_tmp.", i), center2, MAF.range[1], MAF.range[2], }, expectation = handle_expectation, packageNotFoundError = function(e) { miss.cutoff, miss.method, nperbatch, select, if (on_cran()) { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], skip(paste0("{", e$package, "} is not installed.")) bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}, snapshot_on_cran = function(cnd) { else { snapshot_skipped <<- TRUE .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, invokeRestart("muffle_cran_snapshot") obj$Sigma_iX, obj$cov, infile, paste0(outfile, }, skip = handle_skip, warning = handle_warning, message = handle_message, "_tmp.", i), center2, MAF.range[1], MAF.range[2], error = handle_error, interrupt = handle_interrupt), error = handle_fatal) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 28: bgenInfo$N, bgenInfo$CompressionFlag, 1)doWithOneRestart(return(expr), restart) }
}29: }withOneRestart(expr, restarts[[1L]])
20: 30: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, withRestarts(tryCatch(withCallingHandlers({ outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations21: if (snapshot_skipped) {eval(code, test_env) skip("On CRAN")
}22: else if (!new_expectations && skip_on_empty) {eval(code, test_env) skip_empty()
}23: }, expectation = handle_expectation, packageNotFoundError = function(e) {withCallingHandlers({ if (on_cran()) { eval(code, test_env) skip(paste0("{", e$package, "} is not installed.")) new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) {}, snapshot_on_cran = function(cnd) { skip("On CRAN") snapshot_skipped <<- TRUE } invokeRestart("muffle_cran_snapshot") else if (!new_expectations && skip_on_empty) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip_empty() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), } end_test = function() {}, expectation = handle_expectation, packageNotFoundError = function(e) { }) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))31: }test_code(code, parent.frame())}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE32: invokeRestart("muffle_cran_snapshot")test_that("cross-sectional id le 400 binomial", {}, skip = handle_skip, warning = handle_warning, message = handle_message, plinkfiles <- strsplit(system.file("extdata", "geno.bed", error = handle_error, interrupt = handle_interrupt) package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")24: samplefile <- system.file("extdata", "geno.sample", package = "GMMAT")doTryCatch(return(expr), name, parentenv, handler) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")
txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")25: txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")tryCatchOne(expr, names, parentenv, handlers[[1L]]) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")
data(example)26: suppressWarnings(RNGversion("3.5.0"))tryCatchList(expr, classes, parentenv, handlers) set.seed(123)
pheno <- rbind(example$pheno, example$pheno[1:100, ])27: pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NAtryCatch(withCallingHandlers({ pheno$age[sample(1:500, 20)] <- NA eval(code, test_env) pheno$sex[sample(1:500, 20)] <- NA new_expectations <- the$test_expectations > starting_expectations pheno <- pheno[sample(1:500, 450), ] if (snapshot_skipped) { pheno <- pheno[pheno$id <= 400, ] skip("On CRAN") kins <- example$GRM } else if (!new_expectations && skip_on_empty) { obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, skip_empty() id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI")}, expectation = handle_expectation, packageNotFoundError = function(e) { select <- match(1:400, unique(obj1$id_include)) if (on_cran()) { select[is.na(select)] <- 0 skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.bed.noselect.1 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)}, snapshot_on_cran = function(cnd) { obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") obj1.outfile.bed.noselect.1.tmp <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")
unlink(obj1.outfile.bed.noselect.1.tmp)28: obj1.outfile.bed.select.1 <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)
obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, 29: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)
obj1.outfile.bgen.noselect.1 <- tempfile()30: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, withRestarts(tryCatch(withCallingHandlers({ outfile = obj1.outfile.bgen.noselect.1) eval(code, test_env) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { obj1.outfile.bgen.noselect.1.tmp <- tempfile() skip("On CRAN") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) else if (!new_expectations && skip_on_empty) { obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)}, expectation = handle_expectation, packageNotFoundError = function(e) { unlink(obj1.outfile.bgen.noselect.1.tmp) if (on_cran()) { obj1.outfile.bgen.select.1 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj1.outfile.bgen.select.1)}, snapshot_on_cran = function(cnd) { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", error = handle_error, interrupt = handle_interrupt), error = handle_fatal), "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, end_test = function() { c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", }) "VAR", "PVAL")])
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) {31: obj1.outfile.gds.noselect.1 <- tempfile()test_code(code, parent.frame()) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)
obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, 32: header = TRUE, as.is = TRUE)test_that("cross-sectional id le 400 binomial", { obj1.outfile.gds.noselect.1.tmp <- tempfile() plinkfiles <- strsplit(system.file("extdata", "geno.bed", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] ncores = 2) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") header = TRUE, as.is = TRUE) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") unlink(obj1.outfile.gds.noselect.1.tmp) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj1.outfile.gds.select.1 <- tempfile() txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) suppressWarnings(RNGversion("3.5.0")) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, set.seed(123) header = TRUE, as.is = TRUE) pheno <- rbind(example$pheno, example$pheno[1:100, ]) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) pheno$id <- 1:500 expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) pheno$disease[sample(1:500, 20)] <- NA expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, pheno$age[sample(1:500, 20)] <- NA 0.986534857))) pheno$sex[sample(1:500, 20)] <- NA unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) pheno <- pheno[sample(1:500, 450), ] } pheno <- pheno[pheno$id <= 400, ] obj1.outfile.txt.select.1 <- tempfile() kins <- example$GRM glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") "Allele2")) select <- match(1:400, unique(obj1$id_include)) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1 <- tempfile() expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.outfile.txt.select.1.tmp <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.noselect.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.txt.select.1.tmp) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) outfile = obj1.outfile.bgen.noselect.1) obj1.outfile.txt2.select.1 <- tempfile() obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, 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.txt.select.1, obj1.txt2.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.bgen.select.1 <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt2.select.1)) select = select, outfile = obj1.outfile.bgen.select.1) skip_on_cran() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, 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.bgen.noselect.1, obj1.bgen.select.1) method.optim = "AI") expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", select <- match(1:400, unique(obj2$id_include)) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, select[is.na(select)] <- 0 c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bed.noselect.1 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { obj2.outfile.bed.select.1 <- tempfile() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, ncores = 2) outfile = obj2.outfile.bgen.noselect.1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.gds.noselect.1.tmp) obj2.outfile.bgen.select.1 <- tempfile() obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) select = select, outfile = obj2.outfile.bgen.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, 0.986534857))) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj2.outfile.gds.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, unlink(obj1.outfile.txt.select.1.tmp) 0.996996766))) obj1.outfile.txt1.select.1 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj2.outfile.txt.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.1)) select = select, infile.header.print = c("SNP", "Allele1", skip_on_cran() "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.txt.select.1, obj2.txt2.select.1) select <- match(1:400, unique(obj2$id_include)) idx <- sample(nrow(pheno)) select[is.na(select)] <- 0 pheno <- pheno[idx, ] obj2.outfile.bed.noselect.1 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.bed.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj1.outfile.bed.noselect.2 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", select = select, outfile = obj1.outfile.bgen.select.2) "VAR", "PVAL")]) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.outfile.gds.noselect.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) quietly = TRUE)) { obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj1.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.outfile.gds.select.1 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.2 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } } obj2.outfile.txt.select.1 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.outfile.txt1.select.1 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, idx <- sample(nrow(pheno)) id = "id", family = binomial(link = "logit"), method = "REML", pheno <- pheno[idx, ] method.optim = "AI") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select <- match(1:400, unique(obj2$id_include)) id = "id", family = binomial(link = "logit"), method = "REML", select[is.na(select)] <- 0 method.optim = "AI") obj2.outfile.bed.noselect.2 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select[is.na(select)] <- 0 obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, 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(obj2.bed.select.1, obj2.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) outfile = obj1.outfile.bgen.noselect.2) obj2.outfile.bgen.select.2 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select = select, outfile = obj1.outfile.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj2.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, expect_equal(obj1.txt.select.1, obj1.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, idx <- sample(nrow(kins)) header = TRUE, as.is = TRUE) kins <- kins[idx, idx] expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.outfile.bed.select.3 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, expect_equal(obj2.bed.select.1, obj2.bed.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.3 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.outfile.gds.noselect.3 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) } glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) "Allele2")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, idx <- sample(nrow(kins)) kins <- kins[idx, idx] id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select <- match(1:400, unique(obj2$id_include)) id = "id", family = binomial(link = "logit"), method = "REML", select[is.na(select)] <- 0 method.optim = "AI") obj2.outfile.bed.noselect.3 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) select[is.na(select)] <- 0 obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, 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.bed.noselect.1, obj2.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj1.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.bgen.noselect.3 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) quietly = TRUE)) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.gds.noselect.3 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj1.outfile.gds.noselect.3 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.3 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj1.outfile.gds.select.3 <- tempfile() obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, expect_equal(obj1.txt.select.1, obj1.txt.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt1.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) "Allele2")) obj2.outfile.txt2.select.3 <- tempfile() obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.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", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, method.optim = "AI") obj2.outfile.txt2.select.1)) select <- match(1:400, unique(obj2$id_include)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, select[is.na(select)] <- 0 obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj2.outfile.bed.noselect.3 <- tempfile() obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.outfile.txt2.select.2)) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.bed.select.3 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj1.outfile.txt2.select.3)) obj2.outfile.bgen.noselect.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, outfile = obj2.outfile.bgen.noselect.3) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj2.outfile.txt2.select.3)) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) quietly = TRUE)) obj2.outfile.bgen.select.3 <- tempfile() unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)}) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {33: obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)eval(code, test_env)
obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, 34: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)
obj2.outfile.gds.select.3 <- tempfile()35: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)withCallingHandlers({ obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj2.gds.select.1, obj2.gds.select.3) new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) { obj2.outfile.txt.select.3 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", skip_empty() "Allele2")) } obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.txt.select.1, obj2.txt.select.3) skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt1.select.3 <- tempfile() } glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.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, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, error = handle_error, interrupt = handle_interrupt) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)36: obj2.outfile.txt2.select.3 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) 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"))37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers)
expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)39: tryCatch(withCallingHandlers({ unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, if (snapshot_skipped) { obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, skip("On CRAN") obj2.outfile.txt2.select.1)) } unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, else if (!new_expectations && skip_on_empty) { obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, skip_empty() obj1.outfile.txt2.select.2)) } unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, if (on_cran()) { obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt2.select.2)) } unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, }, snapshot_on_cran = function(cnd) { obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, snapshot_skipped <<- TRUE obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, invokeRestart("muffle_cran_snapshot") obj1.outfile.txt2.select.3))}, skip = handle_skip, warning = handle_warning, message = handle_message, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3,
obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, 40: obj2.outfile.txt2.select.3))doWithOneRestart(return(expr), restart) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) 41: unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, withOneRestart(expr, restarts[[1L]])
obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, 42: obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, withRestarts(tryCatch(withCallingHandlers({ obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, eval(code, test_env) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) new_expectations <- the$test_expectations > starting_expectations}) if (snapshot_skipped) {
skip("On CRAN")33: }eval(code, test_env) else if (!new_expectations && skip_on_empty) {
skip_empty()34: }eval(code, test_env)}, expectation = handle_expectation, packageNotFoundError = function(e) {
35: if (on_cran()) {withCallingHandlers({ skip(paste0("{", e$package, "} is not installed.")) } eval(code, test_env)}, snapshot_on_cran = function(cnd) { new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { snapshot_skipped <<- TRUE skip("On CRAN") invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, else if (!new_expectations && skip_on_empty) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), skip_empty() end_test = function() { } })}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {43: skip(paste0("{", e$package, "} is not installed."))test_code(code = exprs, env = env, reporter = get_reporter() %||% } StopReporter$new())}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE44: invokeRestart("muffle_cran_snapshot")source_file(path, env = env(env), desc = desc, shuffle = shuffle, }, skip = handle_skip, warning = handle_warning, message = handle_message, error_call = error_call) error = handle_error, interrupt = handle_interrupt)
45: FUN(X[[i]], ...)36:
doTryCatch(return(expr), name, parentenv, handler)46:
lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, 37: error_call = error_call)tryCatchOne(expr, names, parentenv, handlers[[1L]])
47: 38: doTryCatch(return(expr), name, parentenv, handler)tryCatchList(expr, classes, parentenv, handlers)
48: 39: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatch(withCallingHandlers({
eval(code, test_env)49: new_expectations <- the$test_expectations > starting_expectationstryCatchList(expr, classes, parentenv, handlers) if (snapshot_skipped) {
skip("On CRAN")50: }tryCatch(code, testthat_abort_reporter = function(cnd) { else if (!new_expectations && skip_on_empty) { cat(conditionMessage(cnd), "\n") skip_empty() NULL }})}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {51: skip(paste0("{", e$package, "} is not installed."))with_reporter(reporters$multi, lapply(test_paths, test_one_file, } env = env, desc = desc, shuffle = shuffle, error_call = error_call))}, 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)52:
test_files_serial(test_dir = test_dir, test_package = test_package, 40: test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, doWithOneRestart(return(expr), restart)
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 41: desc = desc, load_package = load_package, shuffle = shuffle, withOneRestart(expr, restarts[[1L]]) error_call = error_call)
53: 42: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, withRestarts(tryCatch(withCallingHandlers({ stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {54: skip("On CRAN")test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed") } else if (!new_expectations && skip_on_empty) {
55: skip_empty() }test_check("GMMAT")}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))An irrecoverable exception occurred. R is aborting now ...
}}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), 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