Last updated on 2026-10-23 13:51:09 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.86 | 234.23 | 278.09 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.74 | 224.56 | 264.30 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 154.08 | 184.08 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 163.82 | 205.82 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 392.00 | 462.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.18 | 226.93 | 278.11 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 52.75 | 232.73 | 285.48 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 66.00 | 309.00 | 375.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 73.00 | 89.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 88.00 | 402.00 | 490.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile,
Traceback:
1: "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, eval(c.expr, envir = args, enclos = envir) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1)
} 2: else {eval(c.expr, envir = args, enclos = envir) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 3: "_tmp.", i), center2, MAF.range[1], MAF.range[2], doTryCatch(return(expr), name, parentenv, handler) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 4: bgenInfo$N, bgenInfo$CompressionFlag, 1)tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
} 5: }tryCatchList(expr, classes, parentenv, handlers)
20: 6: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
7: 21: FUN(X[[i]], ...)eval(code, test_env)
8: 22: lapply(X = S, FUN = FUN, ...)eval(code, test_env)
9: 23: doTryCatch(return(expr), name, parentenv, handler)withCallingHandlers({
eval(code, test_env)10: new_expectations <- the$test_expectations > starting_expectationstryCatchOne(expr, names, parentenv, handlers[[1L]]) if (snapshot_skipped) {
skip("On CRAN")11: }tryCatchList(expr, classes, parentenv, handlers) else if (!new_expectations && skip_on_empty) {
skip_empty()12: }tryCatch(expr, error = function(e) {}, expectation = handle_expectation, packageNotFoundError = function(e) { call <- conditionCall(e) if (on_cran()) { if (!is.null(call)) { skip(paste0("{", e$package, "} is not installed.")) if (identical(call[[1L]], quote(doTryCatch))) } call <- sys.call(-4L)}, snapshot_on_cran = function(cnd) { dcall <- deparse(call, nlines = 1L) snapshot_skipped <<- TRUE prefix <- paste("Error in", dcall, ": ") invokeRestart("muffle_cran_snapshot") LONG <- 75L}, skip = handle_skip, warning = handle_warning, message = handle_message, sm <- strsplit(conditionMessage(e), "\n")[[1L]] error = handle_error, interrupt = handle_interrupt) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 24: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], doTryCatch(return(expr), name, parentenv, handler) type = "b")
if (w > LONG) 25: prefix <- paste0(prefix, "\n ")tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
else prefix <- "Error : "26: msg <- paste0(prefix, conditionMessage(e), "\n")tryCatchList(expr, classes, parentenv, handlers) .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {27: cat(msg, file = outFile)tryCatch(withCallingHandlers({ .Internal(printDeferredWarnings()) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { invisible(structure(msg, class = "try-error", condition = e)) skip("On CRAN")}) }
else if (!new_expectations && skip_on_empty) {13: skip_empty()try(lapply(X = S, FUN = FUN, ...), silent = TRUE) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {14: if (on_cran()) {sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) skip(paste0("{", e$package, "} is not installed."))
}15: }, snapshot_on_cran = function(cnd) {FUN(X[[i]], ...) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")16: }, skip = handle_skip, warning = handle_warning, message = handle_message, lapply(seq_len(cores), inner.do) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, 28: mc.silent = silent, mc.cores = cores)doWithOneRestart(return(expr), restart)
18: 29: e$fun(obj, substitute(ex), parent.frame(), e$data)withOneRestart(expr, restarts[[1L]])
19: 30: foreach(i = 1:ncores) %dopar% {withRestarts(tryCatch(withCallingHandlers({ if (!is.null(obj$P)) { eval(code, test_env) if (bgenInfo$LayoutFlag == 2) { new_expectations <- the$test_expectations > starting_expectations .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, if (snapshot_skipped) { infile, paste0(outfile, "_tmp.", i), center2, skip("On CRAN") MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, else if (!new_expectations && skip_on_empty) { 1) skip_empty() } } else {}, expectation = handle_expectation, packageNotFoundError = function(e) { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, if (on_cran()) { infile, paste0(outfile, "_tmp.", i), center2, skip(paste0("{", e$package, "} is not installed.")) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], }, snapshot_on_cran = function(cnd) { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, snapshot_skipped <<- TRUE 1) invokeRestart("muffle_cran_snapshot") }}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt), error = handle_fatal), else { end_test = function() { if (bgenInfo$LayoutFlag == 2) { }) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 31: "_tmp.", i), center2, MAF.range[1], MAF.range[2], test_code(code, parent.frame()) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 32: bgenInfo$N, bgenInfo$CompressionFlag, 1)test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", else { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj$Sigma_iX, obj$cov, infile, paste0(outfile, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") "_tmp.", i), center2, MAF.range[1], MAF.range[2], gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") miss.cutoff, miss.method, nperbatch, select, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") bgenInfo$N, bgenInfo$CompressionFlag, 1) data(example) } suppressWarnings(RNGversion("3.5.0")) } set.seed(123)} pheno <- rbind(example$pheno, example$pheno[1:100, ])
pheno$id <- 1:50020: pheno$disease[sample(1:500, 20)] <- NAglmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, pheno$age[sample(1:500, 20)] <- NA outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) pheno$sex[sample(1:500, 20)] <- NA
pheno <- pheno[sample(1:500, 450), ]21: pheno <- pheno[pheno$id <= 400, ]eval(code, test_env) kins <- example$GRM
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 22: id = "id", family = binomial(link = "logit"), method = "REML", eval(code, test_env) method.optim = "AI")
select <- match(1:400, unique(obj1$id_include))23: select[is.na(select)] <- 0withCallingHandlers({ obj1.outfile.bed.noselect.1 <- tempfile() eval(code, test_env) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) new_expectations <- the$test_expectations > starting_expectations obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") obj1.outfile.bed.noselect.1.tmp <- tempfile() } expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, else if (!new_expectations && skip_on_empty) { ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") skip_empty() unlink(obj1.outfile.bed.noselect.1.tmp) } obj1.outfile.bed.select.1 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) if (on_cran()) { obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, skip(paste0("{", e$package, "} is not installed.")) } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) snapshot_skipped <<- TRUE obj1.outfile.bgen.noselect.1 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }, skip = handle_skip, warning = handle_warning, message = handle_message, outfile = obj1.outfile.bgen.noselect.1) error = handle_error, interrupt = handle_interrupt) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1,
header = TRUE, as.is = TRUE)24: obj1.outfile.bgen.noselect.1.tmp <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)25: obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)26: unlink(obj1.outfile.bgen.noselect.1.tmp)tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.bgen.select.1 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 27: select = select, outfile = obj1.outfile.bgen.select.1)tryCatch(withCallingHandlers({ obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) skip("On CRAN") expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", } "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, else if (!new_expectations && skip_on_empty) { c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", skip_empty() "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, expectation = handle_expectation, packageNotFoundError = function(e) { quietly = TRUE)) { if (on_cran()) { obj1.outfile.gds.noselect.1 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { obj1.outfile.gds.noselect.1.tmp <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, invokeRestart("muffle_cran_snapshot") ncores = 2)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) header = TRUE, as.is = TRUE)
28: expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)doWithOneRestart(return(expr), restart) unlink(obj1.outfile.gds.noselect.1.tmp)
obj1.outfile.gds.select.1 <- tempfile()29: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)withOneRestart(expr, restarts[[1L]]) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1,
header = TRUE, as.is = TRUE)30: expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)withRestarts(tryCatch(withCallingHandlers({ expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) eval(code, test_env) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, new_expectations <- the$test_expectations > starting_expectations 0.986534857))) if (snapshot_skipped) { unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) skip("On CRAN") } } obj1.outfile.txt.select.1 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, skip_empty() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, expectation = handle_expectation, packageNotFoundError = function(e) { "Allele2")) if (on_cran()) { obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)}, snapshot_on_cran = function(cnd) { obj1.outfile.txt.select.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, snapshot_skipped <<- TRUE infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot") select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), unlink(obj1.outfile.txt.select.1.tmp) end_test = function() { obj1.outfile.txt1.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, 31: select = select, infile.header.print = c("SNP", "Allele1", test_code(code, parent.frame()) "Allele2"))
obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, 32: header = TRUE, as.is = TRUE)test_that("cross-sectional id le 400 binomial", { expect_equal(obj1.txt.select.1, obj1.txt1.select.1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.outfile.txt2.select.1 <- tempfile() package = "GMMAT"), ".bed", fixed = TRUE)[[1]] glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") select = select, infile.header.print = c("SNP", "Allele1", gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") "Allele2")) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") expect_equal(obj1.txt.select.1, obj1.txt2.select.1) data(example) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, suppressWarnings(RNGversion("3.5.0")) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, set.seed(123) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.outfile.txt2.select.1)) pheno$id <- 1:500 skip_on_cran() pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, pheno$sex[sample(1:500, 20)] <- NA id = "id", family = binomial(link = "logit"), method = "REML", pheno <- pheno[sample(1:500, 450), ] method.optim = "AI") pheno <- pheno[pheno$id <= 400, ] select <- match(1:400, unique(obj2$id_include)) kins <- example$GRM select[is.na(select)] <- 0 obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.bed.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj1.outfile.bed.noselect.1 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.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(obj2.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.bed.noselect.1.tmp <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, outfile = obj2.outfile.bgen.noselect.1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj2.outfile.bgen.select.1 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) outfile = obj1.outfile.bgen.noselect.1) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj1.outfile.bgen.noselect.1.tmp <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "VAR", "PVAL")]) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) unlink(obj1.outfile.bgen.noselect.1.tmp) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj1.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.select.1 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj2.gds.select.1 <- read.table(obj2.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(obj2.gds.noselect.1, obj2.gds.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, "VAR", "PVAL")]) 0.996996766))) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1.outfile.gds.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.outfile.gds.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) ncores = 2) obj2.outfile.txt1.select.1 <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.gds.noselect.1.tmp) "Allele2")) obj1.outfile.gds.select.1 <- tempfile() obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) 0.986534857))) obj2.outfile.txt2.select.1 <- tempfile() unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.txt.select.1.tmp <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.bed.noselect.2 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.outfile.txt1.select.1 <- tempfile() obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.outfile.bed.select.2 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 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.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1.outfile.bgen.select.2 <- tempfile() unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, select = select, outfile = obj1.outfile.bgen.select.2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.outfile.txt2.select.1)) skip_on_cran() header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) method.optim = "AI") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select <- match(1:400, unique(obj2$id_include)) quietly = TRUE)) { select[is.na(select)] <- 0 obj1.outfile.gds.noselect.2 <- tempfile() obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2.outfile.bed.select.1 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj2.outfile.bgen.noselect.1) obj1.outfile.txt.select.2 <- tempfile() obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj2.outfile.bgen.select.1) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", select = select, infile.header.print = c("SNP", "Allele1", "VAR", "PVAL")]) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.outfile.gds.noselect.1 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) "Allele2")) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, id = "id", family = binomial(link = "logit"), method = "REML", 0.996996766))) method.optim = "AI") } select <- match(1:400, unique(obj2$id_include)) obj2.outfile.txt.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.outfile.bed.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.outfile.txt1.select.1 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, 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.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.2 <- tempfile() "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, 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.bgen.noselect.1, obj2.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj2.outfile.bgen.select.2) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) idx <- sample(nrow(pheno)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", pheno <- pheno[idx, ] quietly = TRUE)) { obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.gds.noselect.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) method.optim = "AI") obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.outfile.bed.noselect.2 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.outfile.bed.select.2 <- tempfile() } obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj1.outfile.bgen.select.2) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.outfile.txt2.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) "Allele2")) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.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(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1.outfile.gds.select.2 <- tempfile() idx <- sample(nrow(kins)) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) kins <- kins[idx, idx] obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") expect_equal(obj1.gds.select.1, obj1.gds.select.2) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 } obj1.outfile.bed.noselect.3 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.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.noselect.1, obj1.bed.noselect.3) "Allele2")) obj1.outfile.bed.select.3 <- tempfile() obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, expect_equal(obj1.txt.select.1, obj1.txt.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.3) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() 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 = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj1.outfile.bgen.noselect.3) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, 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.3) obj1.outfile.txt2.select.2 <- tempfile() obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, 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.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, "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.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) quietly = TRUE)) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.gds.noselect.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) method.optim = "AI") obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 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.3) obj2.outfile.bed.noselect.2 <- tempfile() obj1.outfile.gds.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.bed.select.2 <- tempfile() } glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.outfile.txt.select.3 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.select.1, obj2.bed.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, outfile = obj2.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, expect_equal(obj1.txt.select.1, obj1.txt.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, 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, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt2.select.3 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj2.outfile.gds.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.outfile.gds.select.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.gds.select.1, obj2.gds.select.2) select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.3 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) "Allele2")) obj2.outfile.bed.select.3 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, 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.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.outfile.bgen.noselect.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", outfile = obj2.outfile.bgen.noselect.3) "Allele2")) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.txt2.select.2 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.outfile.gds.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, idx <- sample(nrow(kins)) header = TRUE, as.is = TRUE) kins <- kins[idx, idx] expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.gds.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) method.optim = "AI") obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj1.outfile.bed.noselect.3 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.txt.select.3 <- tempfile() obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.txt1.select.3 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, 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.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, select = select, outfile = obj1.outfile.bgen.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj1.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, } obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj1.outfile.txt.select.3 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, "Allele2")) obj1.outfile.txt2.select.3)) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj1.outfile.txt1.select.3 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, 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", unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, "Allele2")) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj1.outfile.txt2.select.3 <- tempfile()}) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 33: select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2"))
obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, 34: eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)35: obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, withCallingHandlers({ id = "id", family = binomial(link = "logit"), method = "REML", eval(code, test_env) method.optim = "AI") new_expectations <- the$test_expectations > starting_expectations select <- match(1:400, unique(obj2$id_include)) if (snapshot_skipped) { select[is.na(select)] <- 0 skip("On CRAN") obj2.outfile.bed.noselect.3 <- tempfile() } glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) else if (!new_expectations && skip_on_empty) { obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.bed.select.3 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) skip(paste0("{", e$package, "} is not installed.")) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE expect_equal(obj2.bed.select.1, obj2.bed.select.3) invokeRestart("muffle_cran_snapshot") obj2.outfile.bgen.noselect.3 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, error = handle_error, interrupt = handle_interrupt) outfile = obj2.outfile.bgen.noselect.3)
obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 36: header = TRUE, as.is = TRUE)doTryCatch(return(expr), name, parentenv, handler) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)
obj2.outfile.bgen.select.3 <- tempfile()37: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, tryCatchOne(expr, names, parentenv, handlers[[1L]]) select = select, outfile = obj2.outfile.bgen.select.3)
obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, 38: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 39: quietly = TRUE)) {tryCatch(withCallingHandlers({ obj2.outfile.gds.noselect.3 <- tempfile() eval(code, test_env) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) new_expectations <- the$test_expectations > starting_expectations obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) } obj2.outfile.gds.select.3 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) skip_empty() obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.gds.select.1, obj2.gds.select.3) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt.select.3 <- tempfile() } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, }, snapshot_on_cran = function(cnd) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, snapshot_skipped <<- TRUE select = select, infile.header.print = c("SNP", "Allele1", invokeRestart("muffle_cran_snapshot") "Allele2"))}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt.select.1, obj2.txt.select.3)40: doWithOneRestart(return(expr), restart) obj2.outfile.txt1.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, 41: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, withOneRestart(expr, restarts[[1L]]) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))42: obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, withRestarts(tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) new_expectations <- the$test_expectations > starting_expectations obj2.outfile.txt2.select.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() } obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) skip(paste0("{", e$package, "} is not installed.")) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, } obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, }, snapshot_on_cran = function(cnd) { obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, snapshot_skipped <<- TRUE obj2.outfile.txt2.select.1)) invokeRestart("muffle_cran_snapshot") unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, end_test = function() { obj1.outfile.txt2.select.2)) }) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,
obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, 43: obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, test_code(code = exprs, env = env, reporter = get_reporter() %||% obj2.outfile.txt2.select.2)) StopReporter$new()) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3,
obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, 44: obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj1.outfile.txt2.select.3)) error_call = error_call) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3,
obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, 45: obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, FUN(X[[i]], ...) obj2.outfile.txt2.select.3))
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 46: quietly = TRUE)) lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, error_call = error_call) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2,
obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, 47: obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))
})48:
tryCatchOne(expr, names, parentenv, handlers[[1L]])33:
eval(code, test_env)49:
tryCatchList(expr, classes, parentenv, handlers)34:
eval(code, test_env)50:
tryCatch(code, testthat_abort_reporter = function(cnd) {35: cat(conditionMessage(cnd), "\n")withCallingHandlers({ NULL eval(code, test_env)}) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {51: skip("On CRAN")with_reporter(reporters$multi, lapply(test_paths, test_one_file, } env = env, desc = desc, shuffle = shuffle, error_call = error_call)) else if (!new_expectations && skip_on_empty) {
skip_empty()52: }test_files_serial(test_dir = test_dir, test_package = test_package, }, expectation = handle_expectation, packageNotFoundError = function(e) { test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, if (on_cran()) { env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, skip(paste0("{", e$package, "} is not installed.")) desc = desc, load_package = load_package, shuffle = shuffle, } 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) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,
load_package = load_package, parallel = parallel, shuffle = shuffle)36:
doTryCatch(return(expr), name, parentenv, handler)54:
test_dir("testthat", package = package, reporter = reporter, 37: ..., load_package = "installed")tryCatchOne(expr, names, parentenv, handlers[[1L]])
55: 38: test_check("GMMAT")tryCatchList(expr, classes, parentenv, handlers)
An irrecoverable exception occurred. R is aborting now ...
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64