Last updated on 2026-10-08 08:03:19 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 41.75 | 238.79 | 280.54 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.61 | 206.43 | 246.04 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 156.48 | 186.48 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 41.00 | 168.67 | 209.67 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.86 | 231.73 | 283.59 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 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 | 70.00 | 313.00 | 383.00 | 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 | 87.00 | 398.00 | 485.00 | 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))
Traceback:
1: 15: FUN(X[[i]], ...)eval(c.expr, envir = args, enclos = envir)
16: 2: lapply(seq_len(cores), inner.do)eval(c.expr, envir = args, enclos = envir)
17: 3: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, doTryCatch(return(expr), name, parentenv, handler) mc.silent = silent, mc.cores = cores)
4: 18: tryCatchOne(expr, names, parentenv, handlers[[1L]])e$fun(obj, substitute(ex), parent.frame(), e$data)
5: 19: tryCatchList(expr, classes, parentenv, handlers)foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) { 6: if (bgenInfo$LayoutFlag == 2) {tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 7: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, FUN(X[[i]], ...) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 8: 1)lapply(X = S, FUN = FUN, ...) }
else { 9: .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, doTryCatch(return(expr), name, parentenv, handler) infile, paste0(outfile, "_tmp.", i), center2,
MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 10: nperbatch, select, threadInfo$begin[i], threadInfo$end[i], tryCatchOne(expr, names, parentenv, handlers[[1L]]) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,
1)11: tryCatchList(expr, classes, parentenv, handlers) }
}12: else {tryCatch(expr, error = function(e) { if (bgenInfo$LayoutFlag == 2) { call <- conditionCall(e) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, if (!is.null(call)) { obj$Sigma_iX, obj$cov, infile, paste0(outfile, if (identical(call[[1L]], quote(doTryCatch))) "_tmp.", i), center2, MAF.range[1], MAF.range[2], call <- sys.call(-4L) miss.cutoff, miss.method, nperbatch, select, dcall <- deparse(call, nlines = 1L) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], prefix <- paste("Error in", dcall, ": ") bgenInfo$N, bgenInfo$CompressionFlag, 1) LONG <- 75L } sm <- strsplit(conditionMessage(e), "\n")[[1L]] else { w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, if (is.na(w)) obj$Sigma_iX, obj$cov, infile, paste0(outfile, w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], "_tmp.", i), center2, MAF.range[1], MAF.range[2], type = "b") miss.cutoff, miss.method, nperbatch, select, if (w > LONG) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], prefix <- paste0(prefix, "\n ") bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else prefix <- "Error : " } msg <- paste0(prefix, conditionMessage(e), "\n")} .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {20: cat(msg, file = outFile)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, .Internal(printDeferredWarnings()) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) }
invisible(structure(msg, class = "try-error", condition = e))21: })eval(code, test_env)
22: 13: eval(code, test_env)try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
23: 14: withCallingHandlers({sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations15: if (snapshot_skipped) {FUN(X[[i]], ...) skip("On CRAN")
}16: else if (!new_expectations && skip_on_empty) {lapply(seq_len(cores), inner.do) skip_empty()
}17: }, expectation = handle_expectation, packageNotFoundError = function(e) {mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, if (on_cran()) { mc.silent = silent, mc.cores = cores) skip(paste0("{", e$package, "} is not installed."))
}18: }, snapshot_on_cran = function(cnd) {e$fun(obj, substitute(ex), parent.frame(), e$data) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")19: }, skip = handle_skip, warning = handle_warning, message = handle_message, foreach(i = 1:ncores) %dopar% { error = handle_error, interrupt = handle_interrupt) if (!is.null(obj$P)) {
if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, 24: infile, paste0(outfile, "_tmp.", i), center2, doTryCatch(return(expr), name, parentenv, handler) MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 25: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, tryCatchOne(expr, names, parentenv, handlers[[1L]]) 1)
26: }tryCatchList(expr, classes, parentenv, handlers) else {
.Call(C_glmm_score_bgen11, as.numeric(res), obj$P, 27: infile, paste0(outfile, "_tmp.", i), center2, tryCatch(withCallingHandlers({ MAF.range[1], MAF.range[2], miss.cutoff, miss.method, eval(code, test_env) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], new_expectations <- the$test_expectations > starting_expectations threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, if (snapshot_skipped) { 1) skip("On CRAN") } } } else if (!new_expectations && skip_on_empty) { else { skip_empty() if (bgenInfo$LayoutFlag == 2) { }}, expectation = handle_expectation, packageNotFoundError = function(e) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, if (on_cran()) { obj$Sigma_iX, obj$cov, infile, paste0(outfile, skip(paste0("{", e$package, "} is not installed.")) "_tmp.", i), center2, MAF.range[1], MAF.range[2], } miss.cutoff, miss.method, nperbatch, select, }, snapshot_on_cran = function(cnd) { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], snapshot_skipped <<- TRUE bgenInfo$N, bgenInfo$CompressionFlag, 1) invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, else { error = handle_error, interrupt = handle_interrupt), error = handle_fatal) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 28: "_tmp.", i), center2, MAF.range[1], MAF.range[2], doWithOneRestart(return(expr), restart) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 29: bgenInfo$N, bgenInfo$CompressionFlag, 1)withOneRestart(expr, restarts[[1L]]) }
}30: }withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)20: new_expectations <- the$test_expectations > starting_expectationsglmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (snapshot_skipped) { outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) skip("On CRAN")
}21: else if (!new_expectations && skip_on_empty) {eval(code, test_env) skip_empty()
}22: }, expectation = handle_expectation, packageNotFoundError = function(e) {eval(code, test_env) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))23: }withCallingHandlers({}, snapshot_on_cran = function(cnd) { eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations snapshot_skipped <<- TRUE if (snapshot_skipped) { invokeRestart("muffle_cran_snapshot") skip("On CRAN")}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal), else if (!new_expectations && skip_on_empty) { end_test = function() { skip_empty() })
}31: }, expectation = handle_expectation, packageNotFoundError = function(e) {test_code(code, parent.frame()) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))32: }test_that("cross-sectional id le 400 binomial", {}, snapshot_on_cran = function(cnd) { plinkfiles <- strsplit(system.file("extdata", "geno.bed", snapshot_skipped <<- TRUE package = "GMMAT"), ".bed", fixed = TRUE)[[1]] invokeRestart("muffle_cran_snapshot") bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")}, skip = handle_skip, warning = handle_warning, message = handle_message, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") error = handle_error, interrupt = handle_interrupt) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")
txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")24: txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")doTryCatch(return(expr), name, parentenv, handler) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")
data(example)25: suppressWarnings(RNGversion("3.5.0"))tryCatchOne(expr, names, parentenv, handlers[[1L]]) set.seed(123)
pheno <- rbind(example$pheno, example$pheno[1:100, ])26: pheno$id <- 1:500tryCatchList(expr, classes, parentenv, handlers) pheno$disease[sample(1:500, 20)] <- NA
pheno$age[sample(1:500, 20)] <- NA27: pheno$sex[sample(1:500, 20)] <- NAtryCatch(withCallingHandlers({ eval(code, test_env) 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$GRM } obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, else if (!new_expectations && skip_on_empty) { id = "id", family = binomial(link = "logit"), method = "REML", skip_empty() method.optim = "AI") } select <- match(1:400, unique(obj1$id_include))}, expectation = handle_expectation, packageNotFoundError = function(e) { select[is.na(select)] <- 0 if (on_cran()) { obj1.outfile.bed.noselect.1 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) } obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE obj1.outfile.bed.noselect.1.tmp <- tempfile() invokeRestart("muffle_cran_snapshot") expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, }, skip = handle_skip, warning = handle_warning, message = handle_message, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") error = handle_error, interrupt = handle_interrupt), error = handle_fatal) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile()
glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)28: obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, doWithOneRestart(return(expr), restart) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile()29: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, withOneRestart(expr, restarts[[1L]]) outfile = obj1.outfile.bgen.noselect.1)
obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, 30: header = TRUE, as.is = TRUE)withRestarts(tryCatch(withCallingHandlers({ obj1.outfile.bgen.noselect.1.tmp <- tempfile() eval(code, test_env) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, new_expectations <- the$test_expectations > starting_expectations outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, if (snapshot_skipped) { skip("On CRAN") header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) } unlink(obj1.outfile.bgen.noselect.1.tmp) else if (!new_expectations && skip_on_empty) { obj1.outfile.bgen.select.1 <- tempfile() skip_empty() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj1.outfile.bgen.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) } expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", }, snapshot_on_cran = function(cnd) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, snapshot_skipped <<- TRUE c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) invokeRestart("muffle_cran_snapshot") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, skip = handle_skip, warning = handle_warning, message = handle_message, quietly = TRUE)) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { obj1.outfile.gds.noselect.1 <- tempfile() }) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)
obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, 31: header = TRUE, as.is = TRUE)test_code(code, parent.frame()) obj1.outfile.gds.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, 32: ncores = 2)test_that("cross-sectional id le 400 binomial", { obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, plinkfiles <- strsplit(system.file("extdata", "geno.bed", header = TRUE, as.is = TRUE) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") unlink(obj1.outfile.gds.noselect.1.tmp) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj1.outfile.gds.select.1 <- tempfile() gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) suppressWarnings(RNGversion("3.5.0")) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) set.seed(123) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, pheno <- rbind(example$pheno, example$pheno[1:100, ]) 0.986534857))) pheno$id <- 1:500 unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } pheno$disease[sample(1:500, 20)] <- NA obj1.outfile.txt.select.1 <- tempfile() pheno$age[sample(1:500, 20)] <- NA glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno$sex[sample(1:500, 20)] <- NA select = select, infile.header.print = c("SNP", "Allele1", pheno <- pheno[sample(1:500, 450), ] "Allele2")) pheno <- pheno[pheno$id <= 400, ] obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, kins <- example$GRM header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.bed.noselect.1 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.outfile.txt1.select.1 <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, 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", "Allele2")) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.bed.noselect.1.tmp) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.bed.select.1 <- tempfile() obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) "Allele2")) obj1.outfile.bgen.noselect.1 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.outfile.txt2.select.1)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip_on_cran() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) select <- match(1:400, unique(obj2$id_include)) unlink(obj1.outfile.bgen.noselect.1.tmp) select[is.na(select)] <- 0 obj1.outfile.bgen.select.1 <- tempfile() obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) select = select, outfile = obj1.outfile.bgen.select.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj2.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bgen.noselect.1 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj2.outfile.bgen.noselect.1) quietly = TRUE)) { obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, select = select, outfile = obj2.outfile.bgen.select.1) ncores = 2) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", unlink(obj1.outfile.gds.noselect.1.tmp) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj1.outfile.gds.select.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) "VAR", "PVAL")]) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj2.outfile.gds.select.1 <- tempfile() 0.986534857))) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 0.996996766))) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj2.outfile.txt.select.1 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) "Allele2")) obj1.outfile.txt.select.1.tmp <- tempfile() obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.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, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt1.select.1 <- tempfile() "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, 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, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) method.optim = "AI") skip_on_cran() select <- match(1:400, unique(obj1$id_include)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bed.noselect.2 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj1.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj2.outfile.bed.select.1 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.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, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) outfile = obj2.outfile.bgen.noselect.1) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.bgen.select.2 <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "VAR", "PVAL")]) quietly = TRUE)) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.outfile.gds.noselect.1 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj1.gds.select.1, obj1.gds.select.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) } expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj1.outfile.txt.select.2 <- tempfile() 0.996996766))) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) idx <- sample(nrow(pheno)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, pheno <- pheno[idx, ] id = "id", family = binomial(link = "logit"), method = "REML", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) method.optim = "AI") select[is.na(select)] <- 0 select <- match(1:400, unique(obj1$id_include)) obj2.outfile.bed.noselect.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.outfile.bed.noselect.2 <- tempfile() obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) 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) expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.2 <- 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.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.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.select.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) quietly = TRUE)) { obj2.outfile.gds.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj1.outfile.gds.noselect.2 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.outfile.gds.select.2 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } } obj2.outfile.txt.select.2 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, 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.2 <- read.table(obj2.outfile.txt.select.2, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, 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.2 <- read.table(obj2.outfile.txt1.select.2, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, 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")) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, 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.2) idx <- sample(nrow(kins)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, kins <- kins[idx, idx] id = "id", family = binomial(link = "logit"), method = "REML", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, method.optim = "AI") id = "id", family = binomial(link = "logit"), method = "REML", select <- match(1:400, unique(obj2$id_include)) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj2.outfile.bed.noselect.2 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) header = TRUE, as.is = TRUE) 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, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1, obj1.bed.select.3) outfile = obj2.outfile.bgen.noselect.2) obj1.outfile.bgen.noselect.3 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) select = select, outfile = obj2.outfile.bgen.select.2) obj1.outfile.bgen.select.3 <- tempfile() obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.3) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.outfile.gds.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) quietly = TRUE)) { obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.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(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj2.gds.select.2 <- read.table(obj2.outfile.gds.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(obj2.gds.select.1, obj2.gds.select.2) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() 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() 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.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt.select.2) "Allele2")) obj2.outfile.txt1.select.2 <- tempfile() obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt.select.1, obj1.txt.select.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.2 <- read.table(obj2.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(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj1.txt1.select.3 <- read.table(obj1.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(obj1.txt1.select.1, obj1.txt1.select.3) "Allele2")) obj1.outfile.txt2.select.3 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) idx <- sample(nrow(kins)) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) kins <- kins[idx, idx] obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) method.optim = "AI") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select <- match(1:400, unique(obj1$id_include)) id = "id", family = binomial(link = "logit"), method = "REML", select[is.na(select)] <- 0 method.optim = "AI") obj1.outfile.bed.noselect.3 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) select[is.na(select)] <- 0 obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, 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.bed.noselect.1, obj1.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.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) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.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) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1, obj2.bed.select.3) outfile = obj1.outfile.bgen.noselect.3) obj2.outfile.bgen.noselect.3 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.bgen.select.3 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.3) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj1.outfile.gds.noselect.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj1.outfile.gds.select.3 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.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.select.1, obj1.gds.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) } obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj1.outfile.txt.select.3 <- tempfile() 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(obj2.gds.select.1, obj2.gds.select.3) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj2.outfile.txt.select.3 <- tempfile() obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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.txt1.select.3 <- read.table(obj1.outfile.txt1.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.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() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select = select, infile.header.print = c("SNP", "Allele1", id = "id", family = binomial(link = "logit"), method = "REML", "Allele2")) method.optim = "AI") obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.outfile.bed.noselect.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1)) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj1.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt2.select.2)) outfile = obj2.outfile.bgen.noselect.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.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.bgen.noselect.1, obj2.bgen.noselect.3) obj1.outfile.txt2.select.3)) obj2.outfile.bgen.select.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, select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj2.outfile.txt2.select.3)) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) quietly = TRUE)) { unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj2.outfile.gds.noselect.3 <- tempfile() obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.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.gds.noselect.1, obj2.gds.noselect.3)}) obj2.outfile.gds.select.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)33: obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.select.1, obj2.gds.select.3)34: }eval(code, test_env) obj2.outfile.txt.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 35: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, withCallingHandlers({ select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2")) new_expectations <- the$test_expectations > starting_expectations obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj2.txt.select.1, obj2.txt.select.3) } obj2.outfile.txt1.select.3 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, skip_empty() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, expectation = handle_expectation, packageNotFoundError = function(e) { "Allele2")) if (on_cran()) { obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)}, snapshot_on_cran = function(cnd) { obj2.outfile.txt2.select.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 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) "Allele2")) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,
header = TRUE, as.is = TRUE)36: expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)doTryCatch(return(expr), name, parentenv, handler) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,
obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, 37: obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.1))tryCatchOne(expr, names, parentenv, handlers[[1L]]) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2,
obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, 38: obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.txt2.select.2))
unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, 39: obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, tryCatch(withCallingHandlers({ obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, eval(code, test_env) obj2.outfile.txt2.select.2)) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, if (snapshot_skipped) { obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, skip("On CRAN") obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, } obj1.outfile.txt2.select.3)) else if (!new_expectations && skip_on_empty) { unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, skip_empty() obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, } obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj2.outfile.txt2.select.3)) skip(paste0("{", e$package, "} is not installed.")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, }, snapshot_on_cran = function(cnd) { obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, snapshot_skipped <<- TRUE obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))}) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
33: 40: eval(code, test_env)doWithOneRestart(return(expr), restart)
34: eval(code, test_env)41:
withOneRestart(expr, restarts[[1L]])35:
withCallingHandlers({42: eval(code, test_env)withRestarts(tryCatch(withCallingHandlers({ new_expectations <- the$test_expectations > starting_expectations eval(code, test_env) if (snapshot_skipped) { new_expectations <- the$test_expectations > starting_expectations skip("On CRAN") if (snapshot_skipped) { } skip("On CRAN") else if (!new_expectations && skip_on_empty) { } skip_empty() else if (!new_expectations && skip_on_empty) { } skip_empty()}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) {}, expectation = handle_expectation, packageNotFoundError = function(e) { skip(paste0("{", e$package, "} is not installed.")) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed."))}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE}, snapshot_on_cran = function(cnd) { invokeRestart("muffle_cran_snapshot") snapshot_skipped <<- TRUE}, skip = handle_skip, warning = handle_warning, message = handle_message, invokeRestart("muffle_cran_snapshot") error = handle_error, interrupt = handle_interrupt)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 36: end_test = function() {doTryCatch(return(expr), name, parentenv, handler) })
37: 43: tryCatchOne(expr, names, parentenv, handlers[[1L]])test_code(code = exprs, env = env, reporter = get_reporter() %||%
StopReporter$new())38:
tryCatchList(expr, classes, parentenv, handlers)44:
source_file(path, env = env(env), desc = desc, shuffle = shuffle, 39: error_call = error_call)
tryCatch(withCallingHandlers({45: eval(code, test_env)FUN(X[[i]], ...) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {46: skip("On CRAN")lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, } error_call = error_call) else if (!new_expectations && skip_on_empty) {
skip_empty()47: }doTryCatch(return(expr), name, parentenv, handler)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {48: skip(paste0("{", e$package, "} is not installed."))tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
}, snapshot_on_cran = function(cnd) {49: snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")tryCatchList(expr, classes, parentenv, handlers)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal)50:
tryCatch(code, testthat_abort_reporter = function(cnd) {40: cat(conditionMessage(cnd), "\n")doWithOneRestart(return(expr), restart) NULL
})41:
withOneRestart(expr, restarts[[1L]])51:
with_reporter(reporters$multi, lapply(test_paths, test_one_file, 42: env = env, desc = desc, shuffle = shuffle, error_call = error_call))withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)52: new_expectations <- the$test_expectations > starting_expectationstest_files_serial(test_dir = test_dir, test_package = test_package, if (snapshot_skipped) { test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, skip("On CRAN") } env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, else if (!new_expectations && skip_on_empty) { desc = desc, load_package = load_package, shuffle = shuffle, skip_empty() error_call = error_call) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {53: if (on_cran()) {test_files(test_dir = path, test_paths = test_paths, test_package = package, skip(paste0("{", e$package, "} is not installed.")) reporter = reporter, load_helpers = load_helpers, env = env, } stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, }, snapshot_on_cran = function(cnd) { load_package = load_package, parallel = parallel, shuffle = shuffle) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")54: }, skip = handle_skip, warning = handle_warning, message = handle_message, test_dir("testthat", package = package, reporter = reporter, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), ..., load_package = "installed")
end_test = function() {55: })test_check("GMMAT")
43: An irrecoverable exception occurred. R is aborting now ...
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