Last updated on 2026-10-10 12:49:31 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 44.57 | 239.79 | 284.36 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 37.43 | 215.43 | 252.86 | 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 | 16.00 | 79.00 | 95.00 | 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 | 384.00 | 470.00 | 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)
Traceback:
1: 14: eval(c.expr, envir = args, enclos = envir)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
2: eval(c.expr, envir = args, enclos = envir)15: FUN(X[[i]], ...)
3: doTryCatch(return(expr), name, parentenv, handler)
16:
4: lapply(seq_len(cores), inner.do)tryCatchOne(expr, names, parentenv, handlers[[1L]])
17: 5: tryCatchList(expr, classes, parentenv, handlers)mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed,
mc.silent = silent, mc.cores = cores) 6:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)18:
e$fun(obj, substitute(ex), parent.frame(), e$data) 7:
19: FUN(X[[i]], ...)foreach(i = 1:ncores) %dopar% {
if (!is.null(obj$P)) { 8: if (bgenInfo$LayoutFlag == 2) {lapply(X = S, FUN = FUN, ...) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 9: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, doTryCatch(return(expr), name, parentenv, handler) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 10: 1)tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
else {11: .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, tryCatchList(expr, classes, parentenv, handlers) infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method,
nperbatch, select, threadInfo$begin[i], threadInfo$end[i], 12: threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, tryCatch(expr, error = function(e) { 1) call <- conditionCall(e) } if (!is.null(call)) { } if (identical(call[[1L]], quote(doTryCatch))) else { call <- sys.call(-4L) if (bgenInfo$LayoutFlag == 2) { dcall <- deparse(call, nlines = 1L) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, prefix <- paste("Error in", dcall, ": ") obj$Sigma_iX, obj$cov, infile, paste0(outfile, LONG <- 75L "_tmp.", i), center2, MAF.range[1], MAF.range[2], sm <- strsplit(conditionMessage(e), "\n")[[1L]] miss.cutoff, miss.method, nperbatch, select, w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], if (is.na(w)) bgenInfo$N, bgenInfo$CompressionFlag, 1) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b") if (w > LONG) else { prefix <- paste0(prefix, "\n ") .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, } obj$Sigma_iX, obj$cov, infile, paste0(outfile, else prefix <- "Error : " "_tmp.", i), center2, MAF.range[1], MAF.range[2], msg <- paste0(prefix, conditionMessage(e), "\n") miss.cutoff, miss.method, nperbatch, select, .Internal(seterrmessage(msg[1L])) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], if (!silent && isTRUE(getOption("show.error.messages"))) { bgenInfo$N, bgenInfo$CompressionFlag, 1) cat(msg, file = outFile) } .Internal(printDeferredWarnings()) } } invisible(structure(msg, class = "try-error", condition = e))}})
20: 13: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, try(lapply(X = S, FUN = FUN, ...), silent = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
14: 21: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))eval(code, test_env)
15: 22: FUN(X[[i]], ...)eval(code, test_env)
16: 23: lapply(seq_len(cores), inner.do)withCallingHandlers({
eval(code, test_env)17: new_expectations <- the$test_expectations > starting_expectationsmclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, if (snapshot_skipped) { mc.silent = silent, mc.cores = cores) skip("On CRAN")
}18: else if (!new_expectations && skip_on_empty) {e$fun(obj, substitute(ex), parent.frame(), e$data) skip_empty()
}19: }, expectation = handle_expectation, packageNotFoundError = function(e) {foreach(i = 1:ncores) %dopar% { if (on_cran()) { if (!is.null(obj$P)) { skip(paste0("{", e$package, "} is not installed.")) if (bgenInfo$LayoutFlag == 2) { } .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, }, snapshot_on_cran = function(cnd) { MAF.range[1], MAF.range[2], miss.cutoff, miss.method, snapshot_skipped <<- TRUE nperbatch, select, threadInfo$begin[i], threadInfo$end[i], invokeRestart("muffle_cran_snapshot") threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1)}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt) else {
.Call(C_glmm_score_bgen11, as.numeric(res), obj$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$CompressionFlag, tryCatchOne(expr, names, parentenv, handlers[[1L]]) 1)
26: }tryCatchList(expr, classes, parentenv, handlers) }
else {27: if (bgenInfo$LayoutFlag == 2) {tryCatch(withCallingHandlers({ eval(code, test_env) .Call(C_glmm_score_bgen13_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, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], else if (!new_expectations && skip_on_empty) { bgenInfo$N, bgenInfo$CompressionFlag, 1) } skip_empty() else { } .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj$Sigma_iX, obj$cov, infile, paste0(outfile, if (on_cran()) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], skip(paste0("{", e$package, "} is not installed.")) miss.cutoff, miss.method, nperbatch, select, } threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], }, snapshot_on_cran = function(cnd) { bgenInfo$N, bgenInfo$CompressionFlag, 1) snapshot_skipped <<- TRUE } invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
20: 28: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, doWithOneRestart(return(expr), restart) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
29: 21: withOneRestart(expr, restarts[[1L]])eval(code, test_env)
30: 22: withRestarts(tryCatch(withCallingHandlers({eval(code, test_env) eval(code, test_env)
23: new_expectations <- the$test_expectations > starting_expectationswithCallingHandlers({ if (snapshot_skipped) { eval(code, test_env) skip("On CRAN") new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) { else if (!new_expectations && skip_on_empty) { skip("On CRAN") skip_empty() } } else if (!new_expectations && skip_on_empty) {}, expectation = handle_expectation, packageNotFoundError = function(e) { skip_empty() if (on_cran()) { } skip(paste0("{", e$package, "} is not installed."))}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE}, snapshot_on_cran = function(cnd) { 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_error, interrupt = handle_interrupt), error = handle_fatal),
end_test = function() {24: })doTryCatch(return(expr), name, parentenv, handler)
31: 25: test_code(code, parent.frame())tryCatchOne(expr, names, parentenv, handlers[[1L]])
32: 26: test_that("cross-sectional id le 400 binomial", {tryCatchList(expr, classes, parentenv, handlers) plinkfiles <- strsplit(system.file("extdata", "geno.bed",
package = "GMMAT"), ".bed", fixed = TRUE)[[1]]27: bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")tryCatch(withCallingHandlers({ samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") eval(code, test_env) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") new_expectations <- the$test_expectations > starting_expectations txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") if (snapshot_skipped) { txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") skip("On CRAN") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") } data(example) else if (!new_expectations && skip_on_empty) { suppressWarnings(RNGversion("3.5.0")) skip_empty() set.seed(123) } pheno <- rbind(example$pheno, example$pheno[1:100, ])}, expectation = handle_expectation, packageNotFoundError = function(e) { pheno$id <- 1:500 if (on_cran()) { pheno$disease[sample(1:500, 20)] <- NA skip(paste0("{", e$package, "} is not installed.")) pheno$age[sample(1:500, 20)] <- NA } pheno$sex[sample(1:500, 20)] <- NA}, snapshot_on_cran = function(cnd) { pheno <- pheno[sample(1:500, 450), ] snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") pheno <- pheno[pheno$id <= 400, ]}, skip = handle_skip, warning = handle_warning, message = handle_message, kins <- example$GRM error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 28: method.optim = "AI")doWithOneRestart(return(expr), restart) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 029: obj1.outfile.bed.noselect.1 <- tempfile()withOneRestart(expr, restarts[[1L]])
glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)30: obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, withRestarts(tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) obj1.outfile.bed.noselect.1.tmp <- tempfile() new_expectations <- the$test_expectations > starting_expectations expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, if (snapshot_skipped) { ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") skip("On CRAN") unlink(obj1.outfile.bed.noselect.1.tmp) } obj1.outfile.bed.select.1 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) skip_empty() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.bgen.noselect.1 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }, snapshot_on_cran = function(cnd) { outfile = obj1.outfile.bgen.noselect.1) snapshot_skipped <<- TRUE obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) end_test = function() { 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)31: unlink(obj1.outfile.bgen.noselect.1.tmp)test_code(code, parent.frame()) obj1.outfile.bgen.select.1 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 32: select = select, outfile = obj1.outfile.bgen.select.1)test_that("cross-sectional id le 400 binomial", { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, plinkfiles <- strsplit(system.file("extdata", "geno.bed", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") "VAR", "PVAL")]) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", data(example) quietly = TRUE)) { suppressWarnings(RNGversion("3.5.0")) obj1.outfile.gds.noselect.1 <- tempfile() set.seed(123) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, pheno$id <- 1:500 header = TRUE, as.is = TRUE) pheno$disease[sample(1:500, 20)] <- NA obj1.outfile.gds.noselect.1.tmp <- tempfile() pheno$age[sample(1:500, 20)] <- NA glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, pheno$sex[sample(1:500, 20)] <- NA ncores = 2) pheno <- pheno[sample(1:500, 450), ] obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) id = "id", family = binomial(link = "logit"), method = "REML", unlink(obj1.outfile.gds.noselect.1.tmp) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) obj1.outfile.gds.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.outfile.bed.noselect.1 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") 0.986534857))) unlink(obj1.outfile.bed.noselect.1.tmp) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.outfile.bed.select.1 <- tempfile() } glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.outfile.txt.select.1 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, outfile = obj1.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.txt.select.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, outfile = obj1.outfile.bgen.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, 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.txt.select.1, obj1.txt1.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.outfile.txt2.select.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, "VAR", "PVAL")]) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", quietly = TRUE)) { "Allele2")) obj1.outfile.gds.noselect.1 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, ncores = 2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) skip_on_cran() unlink(obj1.outfile.gds.noselect.1.tmp) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.gds.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) method.optim = "AI") obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, 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.select.1) obj2.outfile.bed.noselect.1 <- tempfile() expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, 0.986534857))) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj2.outfile.bed.select.1 <- tempfile() } glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj1.outfile.txt.select.1 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.txt.select.1.tmp <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", unlink(obj1.outfile.txt.select.1.tmp) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj1.outfile.txt1.select.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, "VAR", "PVAL")]) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj2.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) 0.996996766))) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, } obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.1)) select = select, infile.header.print = c("SNP", "Allele1", skip_on_cran() "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) select <- match(1:400, unique(obj2$id_include)) obj2.outfile.txt1.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj2.outfile.bed.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj2.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, outfile = obj2.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] obj2.outfile.bgen.select.1 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, method.optim = "AI") select = select, outfile = obj2.outfile.bgen.select.1) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.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[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1.outfile.bgen.select.2 <- tempfile() obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) 0.996996766))) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, expect_equal(obj1.gds.select.1, obj1.gds.select.2) } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) 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.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.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.txt1.select.1, obj1.txt1.select.2) idx <- sample(nrow(pheno)) obj1.outfile.txt2.select.2 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") "Allele2")) select <- match(1:400, unique(obj1$id_include)) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.2 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) select[is.na(select)] <- 0 obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.noselect.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1, obj2.bed.select.2) select = select, outfile = obj1.outfile.bgen.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.outfile.gds.noselect.2 <- tempfile() obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, select = select, outfile = obj2.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.2 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) } obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj1.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) "Allele2")) obj2.gds.select.2 <- read.table(obj2.outfile.gds.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.gds.select.1, obj2.gds.select.2) expect_equal(obj1.txt.select.1, obj1.txt.select.2) } obj1.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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")) header = TRUE, as.is = TRUE) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj2.outfile.txt1.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, "Allele2")) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) method.optim = "AI") obj2.outfile.txt2.select.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) "Allele2")) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.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.bed.noselect.1, obj2.bed.noselect.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2.outfile.bed.select.2 <- tempfile() idx <- sample(nrow(kins)) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) kins <- kins[idx, idx] obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bed.select.1, obj2.bed.select.2) method.optim = "AI") obj2.outfile.bgen.noselect.2 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.3 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj1.outfile.bgen.noselect.3) quietly = TRUE)) { obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj1.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.gds.select.2 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) expect_equal(obj2.gds.select.1, obj2.gds.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) { obj2.outfile.txt.select.2 <- tempfile() obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.gds.select.1, obj1.gds.select.3) "Allele2")) } obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj1.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.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.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) "Allele2")) idx <- sample(nrow(kins)) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, kins <- kins[idx, idx] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt2.select.3 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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.txt2.select.3 <- read.table(obj1.outfile.txt2.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.txt2.select.1, obj1.txt2.select.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.bed.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) method.optim = "AI") obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.bed.noselect.3 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) outfile = obj1.outfile.bgen.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bed.select.3 <- read.table(obj2.outfile.bed.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(obj2.bed.select.1, obj2.bed.select.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj2.outfile.bgen.noselect.3) quietly = TRUE)) { obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.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.gds.noselect.1, obj1.gds.noselect.3) obj1.outfile.gds.select.3 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.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.bgen.select.1, obj2.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) } obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj1.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj1.outfile.txt1.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.outfile.txt.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.3) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj2.outfile.txt1.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt2.select.3 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.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.select.1, obj2.bed.select.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.outfile.bgen.noselect.3 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, outfile = obj2.outfile.bgen.noselect.3) obj1.outfile.txt2.select.2)) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.bgen.select.3 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, select = select, outfile = obj2.outfile.bgen.select.3) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.3)) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, quietly = TRUE)) { obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.gds.noselect.3 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, quietly = TRUE)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.select.3 <- tempfile() obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.3)}) } obj2.outfile.txt.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 33: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))34: obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, eval(code, test_env) header = TRUE, as.is = TRUE)
35: expect_equal(obj2.txt.select.1, obj2.txt.select.3)withCallingHandlers({ obj2.outfile.txt1.select.3 <- tempfile() eval(code, test_env) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, new_expectations <- the$test_expectations > starting_expectations infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (snapshot_skipped) { select = select, infile.header.print = c("SNP", "Allele1", skip("On CRAN") "Allele2")) } obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) } obj2.outfile.txt2.select.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, if (on_cran()) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip(paste0("{", e$package, "} is not installed.")) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, snapshot_on_cran = function(cnd) { obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, error = handle_error, interrupt = handle_interrupt) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1,
36: obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2,
obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, 37: obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.outfile.txt2.select.2))
unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, 38: obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, tryCatchList(expr, classes, parentenv, handlers) obj2.outfile.txt2.select.2))
unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, 39: obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, tryCatch(withCallingHandlers({ obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, eval(code, test_env) obj1.outfile.txt2.select.3)) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, if (snapshot_skipped) { obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, skip("On CRAN") obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, } obj2.outfile.txt2.select.3)) else if (!new_expectations && skip_on_empty) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", skip_empty() quietly = TRUE)) } unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, if (on_cran()) { obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) }}, snapshot_on_cran = function(cnd) {}) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")33: }, skip = handle_skip, warning = handle_warning, message = handle_message, eval(code, test_env) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
34:
eval(code, test_env)40:
doWithOneRestart(return(expr), restart)35:
withCallingHandlers({41: eval(code, test_env)withOneRestart(expr, restarts[[1L]]) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {42: skip("On CRAN")withRestarts(tryCatch(withCallingHandlers({ } eval(code, test_env) else if (!new_expectations && skip_on_empty) { new_expectations <- the$test_expectations > starting_expectations skip_empty() if (snapshot_skipped) { } skip("On CRAN")}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { else if (!new_expectations && skip_on_empty) { skip(paste0("{", e$package, "} is not installed.")) skip_empty() } }}, snapshot_on_cran = function(cnd) {}, expectation = handle_expectation, packageNotFoundError = function(e) { snapshot_skipped <<- TRUE if (on_cran()) { invokeRestart("muffle_cran_snapshot") skip(paste0("{", e$package, "} is not installed."))}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt)}, snapshot_on_cran = function(cnd) {
36: snapshot_skipped <<- TRUEdoTryCatch(return(expr), name, parentenv, handler) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 37: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), tryCatchOne(expr, names, parentenv, handlers[[1L]]) end_test = function() {
})38:
tryCatchList(expr, classes, parentenv, handlers)43:
test_code(code = exprs, env = env, reporter = get_reporter() %||% 39: StopReporter$new())tryCatch(withCallingHandlers({
eval(code, test_env)44: new_expectations <- the$test_expectations > starting_expectationssource_file(path, env = env(env), desc = desc, shuffle = shuffle, if (snapshot_skipped) { error_call = error_call) skip("On CRAN")
}45: else if (!new_expectations && skip_on_empty) {FUN(X[[i]], ...) skip_empty()
}46: }, expectation = handle_expectation, packageNotFoundError = function(e) {lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, if (on_cran()) { error_call = error_call) skip(paste0("{", e$package, "} is not installed."))
}47: }, snapshot_on_cran = function(cnd) {doTryCatch(return(expr), name, parentenv, handler) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")48: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatchOne(expr, names, parentenv, handlers[[1L]]) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
49: 40: tryCatchList(expr, classes, parentenv, handlers)doWithOneRestart(return(expr), restart)
50: 41: tryCatch(code, testthat_abort_reporter = function(cnd) {withOneRestart(expr, restarts[[1L]]) cat(conditionMessage(cnd), "\n")
NULL42: })withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)51: new_expectations <- the$test_expectations > starting_expectationswith_reporter(reporters$multi, lapply(test_paths, test_one_file, if (snapshot_skipped) { env = env, desc = desc, shuffle = shuffle, error_call = error_call)) skip("On CRAN")
}52: else if (!new_expectations && skip_on_empty) {test_files_serial(test_dir = test_dir, test_package = test_package, skip_empty() } test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, }, expectation = handle_expectation, packageNotFoundError = function(e) { env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, if (on_cran()) { desc = desc, load_package = load_package, shuffle = shuffle, skip(paste0("{", e$package, "} is not installed.")) error_call = error_call) }
}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE53: invokeRestart("muffle_cran_snapshot")test_files(test_dir = path, test_paths = test_paths, test_package = package, }, skip = handle_skip, warning = handle_warning, message = handle_message, reporter = reporter, load_helpers = load_helpers, env = env, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, end_test = function() { load_package = load_package, parallel = parallel, shuffle = shuffle) })
54: 43: test_dir("testthat", package = package, reporter = reporter, test_code(code = exprs, env = env, reporter = get_reporter() %||% ..., load_package = "installed") StopReporter$new())
55: 44: test_check("GMMAT")source_file(path, env = env(env), desc = desc, shuffle = shuffle,
error_call = error_call)An irrecoverable exception occurred. R is aborting now ...
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$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