Last updated on 2026-11-13 12:51:11 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.11 | 246.51 | 291.62 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 37.72 | 233.26 | 270.98 | 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 | 17.00 | 77.00 | 94.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 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:
Traceback:
FUN(X[[i]], ...) 1:
eval(c.expr, envir = args, enclos = envir) 8:
lapply(X = S, FUN = FUN, ...) 2:
eval(c.expr, envir = args, enclos = envir) 9:
doTryCatch(return(expr), name, parentenv, handler) 3:
doTryCatch(return(expr), name, parentenv, handler)10:
tryCatchOne(expr, names, parentenv, handlers[[1L]]) 4:
tryCatchOne(expr, names, parentenv, handlers[[1L]])11:
tryCatchList(expr, classes, parentenv, handlers) 5:
tryCatchList(expr, classes, parentenv, handlers)12:
tryCatch(expr, error = function(e) { 6: call <- conditionCall(e)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 7: call <- sys.call(-4L)FUN(X[[i]], ...) dcall <- deparse(call, nlines = 1L)
prefix <- paste("Error in", dcall, ": ") 8: LONG <- 75Llapply(X = S, FUN = FUN, ...) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") 9: if (is.na(w)) doTryCatch(return(expr), name, parentenv, handler) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b") if (w > LONG) 10: prefix <- paste0(prefix, "\n ")tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
else prefix <- "Error : "11: msg <- paste0(prefix, conditionMessage(e), "\n")tryCatchList(expr, classes, parentenv, handlers) .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {12: cat(msg, file = outFile)tryCatch(expr, error = function(e) { .Internal(printDeferredWarnings()) call <- conditionCall(e) } if (!is.null(call)) { invisible(structure(msg, class = "try-error", condition = e)) if (identical(call[[1L]], quote(doTryCatch))) }) call <- sys.call(-4L)
dcall <- deparse(call, nlines = 1L)13: prefix <- paste("Error in", dcall, ": ")try(lapply(X = S, FUN = FUN, ...), silent = TRUE) LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]14: w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], 15: FUN(X[[i]], ...) type = "b")
if (w > LONG) 16: prefix <- paste0(prefix, "\n ")lapply(seq_len(cores), inner.do) }
else prefix <- "Error : "17: msg <- paste0(prefix, conditionMessage(e), "\n")mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, .Internal(seterrmessage(msg[1L])) mc.silent = silent, mc.cores = cores) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))18: e$fun(obj, substitute(ex), parent.frame(), e$data)})
19: 13: foreach(i = 1:ncores) %dopar% {try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) {14: .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) infile, paste0(outfile, "_tmp.", i), center2,
MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 15: FUN(X[[i]], ...) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 16: 1)lapply(seq_len(cores), inner.do) }
else {17: .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, mc.silent = silent, mc.cores = cores) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 18: 1)e$fun(obj, substitute(ex), parent.frame(), e$data) }
} else {19: foreach(i = 1:ncores) %dopar% { if (bgenInfo$LayoutFlag == 2) { if (!is.null(obj$P)) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, if (bgenInfo$LayoutFlag == 2) { obj$Sigma_iX, obj$cov, infile, paste0(outfile, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, "_tmp.", i), center2, MAF.range[1], MAF.range[2], infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, bgenInfo$N, bgenInfo$CompressionFlag, 1) } 1) else { } .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, else { obj$Sigma_iX, obj$cov, infile, paste0(outfile, .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, "_tmp.", i), center2, MAF.range[1], MAF.range[2], infile, paste0(outfile, "_tmp.", i), center2, miss.cutoff, miss.method, nperbatch, select, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, bgenInfo$N, bgenInfo$CompressionFlag, 1) 1) } } }} }
else {20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 21: "_tmp.", i), center2, MAF.range[1], MAF.range[2], eval(code, test_env)
miss.cutoff, miss.method, nperbatch, select, 22: eval(code, test_env) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],
bgenInfo$N, bgenInfo$CompressionFlag, 1)23: }withCallingHandlers({ else { eval(code, test_env) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, new_expectations <- the$test_expectations > starting_expectations obj$Sigma_iX, obj$cov, infile, paste0(outfile, if (snapshot_skipped) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], skip("On CRAN") } miss.cutoff, miss.method, nperbatch, select, else if (!new_expectations && skip_on_empty) { threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], skip_empty() bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) {} skip(paste0("{", e$package, "} is not installed."))
}20: }, snapshot_on_cran = function(cnd) {glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt)21:
eval(code, test_env)24:
doTryCatch(return(expr), name, parentenv, handler)22:
eval(code, test_env)25:
tryCatchOne(expr, names, parentenv, handlers[[1L]])23:
withCallingHandlers({26: eval(code, test_env)tryCatchList(expr, classes, parentenv, handlers) new_expectations <- the$test_expectations > starting_expectations
27: if (snapshot_skipped) {tryCatch(withCallingHandlers({ skip("On CRAN") eval(code, test_env) } else if (!new_expectations && skip_on_empty) { new_expectations <- the$test_expectations > starting_expectations skip_empty() if (snapshot_skipped) { } skip("On CRAN")}, expectation = handle_expectation, packageNotFoundError = function(e) { } else if (!new_expectations && skip_on_empty) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) skip_empty() } }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { error = handle_error, interrupt = handle_interrupt)
snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")24: doTryCatch(return(expr), name, parentenv, handler)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
25:
28: tryCatchOne(expr, names, parentenv, handlers[[1L]])
doWithOneRestart(return(expr), restart)
26: 29: tryCatchList(expr, classes, parentenv, handlers)withOneRestart(expr, restarts[[1L]])
27: 30: tryCatch(withCallingHandlers({withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { if (snapshot_skipped) { skip("On CRAN") skip("On CRAN") } } else if (!new_expectations && skip_on_empty) { else if (!new_expectations && skip_on_empty) { skip_empty() skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) skip(paste0("{", e$package, "} is not installed.")) } }}, snapshot_on_cran = function(cnd) {}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
end_test = function() {28: })doWithOneRestart(return(expr), restart)
29: 31: withOneRestart(expr, restarts[[1L]])test_code(code, parent.frame())
32: test_that("cross-sectional id le 400 binomial", {30: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) plinkfiles <- strsplit(system.file("extdata", "geno.bed", new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") skip("On CRAN") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") } gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") else if (!new_expectations && skip_on_empty) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") skip_empty() txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") } txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")}, expectation = handle_expectation, packageNotFoundError = function(e) { data(example) suppressWarnings(RNGversion("3.5.0")) set.seed(123) if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) pheno <- rbind(example$pheno, example$pheno[1:100, ]) } pheno$id <- 1:500}, snapshot_on_cran = function(cnd) { pheno$disease[sample(1:500, 20)] <- NA snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") pheno$age[sample(1:500, 20)] <- NA}, skip = handle_skip, warning = handle_warning, message = handle_message, pheno$sex[sample(1:500, 20)] <- NA error = handle_error, interrupt = handle_interrupt), error = handle_fatal), pheno <- pheno[sample(1:500, 450), ] end_test = function() { }) pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM
31: obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, test_code(code, parent.frame()) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI")32: select <- match(1:400, unique(obj1$id_include))test_that("cross-sectional id le 400 binomial", { select[is.na(select)] <- 0 plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.outfile.bed.noselect.1 <- tempfile() package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj1.outfile.bed.noselect.1.tmp <- tempfile() data(example) suppressWarnings(RNGversion("3.5.0")) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) pheno$id <- 1:500 obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, pheno$disease[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$age[sample(1:500, 20)] <- NA expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) pheno$sex[sample(1:500, 20)] <- NA obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] outfile = obj1.outfile.bgen.noselect.1) kins <- example$GRM obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, select = select, outfile = obj1.outfile.bgen.select.1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, unlink(obj1.outfile.bed.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.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, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.outfile.bgen.noselect.1 <- tempfile() obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.select.1 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.1) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) "VAR", "PVAL")]) } obj1.outfile.txt.select.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.gds.noselect.1.tmp <- tempfile() obj1.outfile.txt.select.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, ncores = 2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) "Allele2")) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, "Allele2")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, 0.986534857))) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.1 <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) skip_on_cran() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) "Allele2")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj1.outfile.txt.select.1.tmp <- tempfile() header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) unlink(obj1.outfile.txt.select.1.tmp) obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, outfile = obj2.outfile.bgen.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) 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) obj2.outfile.bgen.select.1 <- tempfile() obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, select = select, outfile = obj2.outfile.bgen.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, 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.txt.select.1, obj1.txt2.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, "VAR", "PVAL")]) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt2.select.1)) skip_on_cran() quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, 0.996996766))) } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj2.outfile.bgen.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, select = select, outfile = obj2.outfile.bgen.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, header = TRUE, as.is = TRUE) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.noselect.1 <- tempfile() "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, 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(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, 0.996996766))) } header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt1.select.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0 obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.bed.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.bgen.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", outfile = obj1.outfile.bgen.noselect.2) "Allele2")) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.outfile.bgen.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj1.outfile.bgen.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) method.optim = "AI") obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2.outfile.bed.noselect.2 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.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, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) } obj2.outfile.bed.select.2 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) 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.bgen.noselect.1, obj2.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { obj2.outfile.gds.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.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.txt2.select.1, obj1.txt2.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.gds.select.1, obj2.gds.select.2) select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.2 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) "Allele2")) obj2.outfile.bed.select.2 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.2) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.2 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj2.outfile.bgen.noselect.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj2.outfile.bgen.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj2.outfile.bgen.select.2) "Allele2")) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", idx <- sample(nrow(kins)) quietly = TRUE)) { kins <- kins[idx, idx] obj2.outfile.gds.noselect.2 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) select[is.na(select)] <- 0 obj2.outfile.gds.select.2 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.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.gds.select.1, obj2.gds.select.2) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) } obj1.outfile.bed.select.3 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1, obj1.bed.select.3) "Allele2")) obj1.outfile.bgen.noselect.3 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.3) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, 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.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj1.outfile.bgen.select.3) "Allele2")) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) quietly = TRUE)) { obj2.outfile.txt2.select.2 <- tempfile() obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.outfile.gds.select.3 <- tempfile() 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.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj1.gds.select.1, obj1.gds.select.3) kins <- kins[idx, idx] } obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, select <- match(1:400, unique(obj1$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.txt1.select.3 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) outfile = obj1.outfile.bgen.noselect.3) obj1.outfile.txt2.select.3 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, 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(obj1.bgen.noselect.1, obj1.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, select = select, outfile = obj1.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) id = "id", family = binomial(link = "logit"), method = "REML", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", method.optim = "AI") quietly = TRUE)) { select <- match(1:400, unique(obj2$id_include)) obj1.outfile.gds.noselect.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.bed.noselect.3 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) header = TRUE, as.is = TRUE) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) expect_equal(obj2.bed.select.1, obj2.bed.select.3) } obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.3 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj2.outfile.bgen.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj2.outfile.bgen.select.3) "Allele2")) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) expect_equal(obj1.txt.select.1, obj1.txt.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt1.select.3 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.outfile.gds.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.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.txt1.select.1, obj1.txt1.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj1.outfile.txt2.select.3 <- tempfile() obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, 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.gds.select.1, obj2.gds.select.3) } select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.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.txt2.select.1, obj1.txt2.select.3) "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.txt.select.1, obj2.txt.select.3) select <- match(1:400, unique(obj2$id_include)) obj2.outfile.txt1.select.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj2.outfile.bed.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.txt2.select.3 <- tempfile() obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.select.1, obj2.bed.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, outfile = obj2.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.bgen.select.3 <- tempfile() obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt2.select.1)) select = select, outfile = obj2.outfile.bgen.select.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj1.outfile.txt2.select.2)) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, quietly = TRUE)) { obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.gds.noselect.3 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.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.gds.noselect.1, obj2.gds.noselect.3) obj1.outfile.txt2.select.3)) obj2.outfile.gds.select.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3)) expect_equal(obj2.gds.select.1, obj2.gds.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) obj2.outfile.txt.select.3 <- tempfile() unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, "Allele2")) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, }) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt.select.1, obj2.txt.select.3)33: obj2.outfile.txt1.select.3 <- tempfile()eval(code, test_env) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 34: select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2"))
obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, 35: header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) eval(code, test_env) obj2.outfile.txt2.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, if (snapshot_skipped) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip("On CRAN") select = select, infile.header.print = c("SNP", "Allele1", } else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) if (on_cran()) { unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, } obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, }, snapshot_on_cran = function(cnd) { obj2.outfile.txt2.select.1)) snapshot_skipped <<- TRUE unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, invokeRestart("muffle_cran_snapshot") obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, error = handle_error, interrupt = handle_interrupt) obj1.outfile.txt2.select.2))
unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, 36: obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2,
obj2.outfile.txt2.select.2))37: unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3,
obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, 38: obj1.outfile.txt2.select.3))tryCatchList(expr, classes, parentenv, handlers) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3,
obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, 39: obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, tryCatch(withCallingHandlers({ obj2.outfile.txt2.select.3)) eval(code, test_env) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", new_expectations <- the$test_expectations > starting_expectations quietly = TRUE)) if (snapshot_skipped) { unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, skip("On CRAN") obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, } obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, else if (!new_expectations && skip_on_empty) { obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, skip_empty() obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) }})}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {33: skip(paste0("{", e$package, "} is not installed."))eval(code, test_env) }
}, snapshot_on_cran = function(cnd) {34: snapshot_skipped <<- TRUEeval(code, test_env) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 35: error = handle_error, interrupt = handle_interrupt), error = handle_fatal)withCallingHandlers({
eval(code, test_env)40: new_expectations <- the$test_expectations > starting_expectationsdoWithOneRestart(return(expr), restart) if (snapshot_skipped) {
skip("On CRAN")41: }withOneRestart(expr, restarts[[1L]]) else if (!new_expectations && skip_on_empty) {
skip_empty()42: }withRestarts(tryCatch(withCallingHandlers({}, expectation = handle_expectation, packageNotFoundError = function(e) { eval(code, test_env) if (on_cran()) { new_expectations <- the$test_expectations > starting_expectations skip(paste0("{", e$package, "} is not installed.")) if (snapshot_skipped) { } skip("On CRAN")}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE else if (!new_expectations && skip_on_empty) { invokeRestart("muffle_cran_snapshot") skip_empty()}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt)
}, expectation = handle_expectation, packageNotFoundError = function(e) {36: if (on_cran()) {doTryCatch(return(expr), name, parentenv, handler) skip(paste0("{", e$package, "} is not installed."))
}37: }, snapshot_on_cran = function(cnd) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")38: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatchList(expr, classes, parentenv, handlers) error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
end_test = function() {39: })tryCatch(withCallingHandlers({
eval(code, test_env)43: new_expectations <- the$test_expectations > starting_expectationstest_code(code = exprs, env = env, reporter = get_reporter() %||% if (snapshot_skipped) { StopReporter$new()) skip("On CRAN")
}44: else if (!new_expectations && skip_on_empty) {source_file(path, env = env(env), desc = desc, shuffle = shuffle, skip_empty() error_call = error_call) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {45: if (on_cran()) {FUN(X[[i]], ...) skip(paste0("{", e$package, "} is not installed."))
}46: }, snapshot_on_cran = function(cnd) {lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, snapshot_skipped <<- TRUE error_call = error_call) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 47: error = handle_error, interrupt = handle_interrupt), error = handle_fatal)doTryCatch(return(expr), name, parentenv, handler)
40: 48: doWithOneRestart(return(expr), restart)tryCatchOne(expr, names, parentenv, handlers[[1L]])
41: 49: tryCatchList(expr, classes, parentenv, handlers)withOneRestart(expr, restarts[[1L]])
50: 42: tryCatch(code, testthat_abort_reporter = function(cnd) {withRestarts(tryCatch(withCallingHandlers({ cat(conditionMessage(cnd), "\n") eval(code, test_env) NULL new_expectations <- the$test_expectations > starting_expectations}) if (snapshot_skipped) {
skip("On CRAN")51: }with_reporter(reporters$multi, lapply(test_paths, test_one_file, else if (!new_expectations && skip_on_empty) { env = env, desc = desc, shuffle = shuffle, error_call = error_call)) skip_empty()
}52: }, expectation = handle_expectation, packageNotFoundError = function(e) {test_files_serial(test_dir = test_dir, test_package = test_package, if (on_cran()) { test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, skip(paste0("{", e$package, "} is not installed.")) env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, } desc = desc, load_package = load_package, shuffle = shuffle, }, snapshot_on_cran = function(cnd) { error_call = error_call) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")53: }, skip = handle_skip, warning = handle_warning, message = handle_message, test_files(test_dir = path, test_paths = test_paths, test_package = package, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), reporter = reporter, load_helpers = load_helpers, env = env, end_test = function() { stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, }) load_package = load_package, parallel = parallel, shuffle = shuffle)
43: 54: test_code(code = exprs, env = env, reporter = get_reporter() %||% test_dir("testthat", package = package, reporter = reporter, StopReporter$new()) ..., load_package = "installed")
44: 55: source_file(path, env = env(env), desc = desc, shuffle = shuffle, test_check("GMMAT") error_call = error_call)
An irrecoverable exception occurred. R is aborting now ...
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$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