Last updated on 2026-11-09 01:52:17 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.64 | 240.29 | 285.93 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 42.09 | 204.80 | 246.89 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 26.00 | 141.72 | 167.72 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 166.67 | 208.67 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | OK | ||||
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 86.00 | 102.00 | 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 | 384.00 | 472.00 | 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, "_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$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, 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$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
Traceback:
21: 1: eval(c.expr, envir = args, enclos = envir)eval(code, test_env)
2: 22: eval(c.expr, envir = args, enclos = envir)eval(code, test_env)
3: 23: doTryCatch(return(expr), name, parentenv, handler)withCallingHandlers({
eval(code, test_env) 4: new_expectations <- the$test_expectations > starting_expectationstryCatchOne(expr, names, parentenv, handlers[[1L]]) if (snapshot_skipped) {
skip("On CRAN") 5: }tryCatchList(expr, classes, parentenv, handlers) else if (!new_expectations && skip_on_empty) {
skip_empty() 6: }tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) { 7: skip(paste0("{", e$package, "} is not installed."))FUN(X[[i]], ...) }
}, snapshot_on_cran = function(cnd) { 8: snapshot_skipped <<- TRUElapply(X = S, FUN = FUN, ...) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 9: error = handle_error, interrupt = handle_interrupt)doTryCatch(return(expr), name, parentenv, handler)
24: 10: tryCatchOne(expr, names, parentenv, handlers[[1L]])doTryCatch(return(expr), name, parentenv, handler)
11: 25: tryCatchList(expr, classes, parentenv, handlers)tryCatchOne(expr, names, parentenv, handlers[[1L]])
12: 26: tryCatch(expr, error = function(e) { call <- conditionCall(e)tryCatchList(expr, classes, parentenv, handlers) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 27: call <- sys.call(-4L)tryCatch(withCallingHandlers({ dcall <- deparse(call, nlines = 1L) eval(code, test_env) prefix <- paste("Error in", dcall, ": ") new_expectations <- the$test_expectations > starting_expectations LONG <- 75L if (snapshot_skipped) { sm <- strsplit(conditionMessage(e), "\n")[[1L]] skip("On CRAN") w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") } if (is.na(w)) else if (!new_expectations && skip_on_empty) { w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], skip_empty() type = "b") } if (w > LONG) }, expectation = handle_expectation, packageNotFoundError = function(e) { prefix <- paste0(prefix, "\n ") if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) else prefix <- "Error : " } msg <- paste0(prefix, conditionMessage(e), "\n")}, snapshot_on_cran = function(cnd) { .Internal(seterrmessage(msg[1L])) snapshot_skipped <<- TRUE if (!silent && isTRUE(getOption("show.error.messages"))) { invokeRestart("muffle_cran_snapshot") cat(msg, file = outFile)}, skip = handle_skip, warning = handle_warning, message = handle_message, .Internal(printDeferredWarnings()) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) }
invisible(structure(msg, class = "try-error", condition = e))28: })doWithOneRestart(return(expr), restart)
13: 29: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)withOneRestart(expr, restarts[[1L]])
14: 30: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)15: new_expectations <- the$test_expectations > starting_expectationsFUN(X[[i]], ...) if (snapshot_skipped) {
skip("On CRAN")16: }lapply(seq_len(cores), inner.do) else if (!new_expectations && skip_on_empty) {
skip_empty()17: }mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, }, expectation = handle_expectation, packageNotFoundError = function(e) { mc.silent = silent, mc.cores = cores) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))18: }e$fun(obj, substitute(ex), parent.frame(), e$data)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE19: invokeRestart("muffle_cran_snapshot")foreach(i = 1:ncores) %dopar% {}, skip = handle_skip, warning = handle_warning, message = handle_message, if (!is.null(obj$P)) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), if (bgenInfo$LayoutFlag == 2) { end_test = function() { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, }) infile, paste0(outfile, "_tmp.", i), center2,
31: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, test_code(code, parent.frame()) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 32: 1)test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", else { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") infile, paste0(outfile, "_tmp.", i), center2, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") MAF.range[1], MAF.range[2], miss.cutoff, miss.method, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") nperbatch, select, threadInfo$begin[i], threadInfo$end[i], txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") 1) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") } data(example) } suppressWarnings(RNGversion("3.5.0")) else { set.seed(123) if (bgenInfo$LayoutFlag == 2) { pheno <- rbind(example$pheno, example$pheno[1:100, ]) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, pheno$id <- 1:500 obj$Sigma_iX, obj$cov, infile, paste0(outfile, pheno$disease[sample(1:500, 20)] <- NA "_tmp.", i), center2, MAF.range[1], MAF.range[2], pheno$age[sample(1:500, 20)] <- NA miss.cutoff, miss.method, nperbatch, select, pheno$sex[sample(1:500, 20)] <- NA threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], pheno <- pheno[sample(1:500, 450), ] bgenInfo$N, bgenInfo$CompressionFlag, 1) pheno <- pheno[pheno$id <= 400, ] } kins <- example$GRM else { obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, id = "id", family = binomial(link = "logit"), method = "REML", obj$Sigma_iX, obj$cov, infile, paste0(outfile, method.optim = "AI") "_tmp.", i), center2, MAF.range[1], MAF.range[2], select <- match(1:400, unique(obj1$id_include)) miss.cutoff, miss.method, nperbatch, select, select[is.na(select)] <- 0 threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj1.outfile.bed.noselect.1 <- tempfile() bgenInfo$N, bgenInfo$CompressionFlag, 1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) } obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, } header = TRUE, as.is = TRUE)} obj1.outfile.bed.noselect.1.tmp <- tempfile()
expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(obj1.outfile.bed.noselect.1.tmp) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.bed.select.1 <- tempfile()
glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)21: obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)22: obj1.outfile.bgen.noselect.1 <- tempfile()eval(code, test_env) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1)23: obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) obj1.outfile.bgen.noselect.1.tmp <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (snapshot_skipped) { outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) skip("On CRAN") obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) skip_empty() unlink(obj1.outfile.bgen.noselect.1.tmp) } obj1.outfile.bgen.select.1 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (on_cran()) { select = select, outfile = obj1.outfile.bgen.select.1) skip(paste0("{", e$package, "} is not installed.")) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) snapshot_skipped <<- TRUE expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", invokeRestart("muffle_cran_snapshot") "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, }, skip = handle_skip, warning = handle_warning, message = handle_message, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", error = handle_error, interrupt = handle_interrupt) "VAR", "PVAL")])
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 24: quietly = TRUE)) {doTryCatch(return(expr), name, parentenv, handler) obj1.outfile.gds.noselect.1 <- tempfile()
glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)25: obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE)
26: obj1.outfile.gds.noselect.1.tmp <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp,
27: ncores = 2)tryCatch(withCallingHandlers({ obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) if (snapshot_skipped) { unlink(obj1.outfile.gds.noselect.1.tmp) skip("On CRAN") obj1.outfile.gds.select.1 <- tempfile() } glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) else if (!new_expectations && skip_on_empty) { obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) if (on_cran()) { expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, skip(paste0("{", e$package, "} is not installed.")) 0.986534857))) } unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE obj1.outfile.txt.select.1 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))28: obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, doWithOneRestart(return(expr), restart) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)29: obj1.outfile.txt.select.1.tmp <- tempfile()withOneRestart(expr, restarts[[1L]])
expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, 30: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, withRestarts(tryCatch(withCallingHandlers({ select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") new_expectations <- the$test_expectations > starting_expectations unlink(obj1.outfile.txt.select.1.tmp) if (snapshot_skipped) { obj1.outfile.txt1.select.1 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", skip_empty() "Allele2")) }}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) } obj1.outfile.txt2.select.1 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 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, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), "Allele2")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, end_test = function() { header = TRUE, as.is = TRUE) }) expect_equal(obj1.txt.select.1, obj1.txt2.select.1)
unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, 31: obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, test_code(code, parent.frame()) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1,
obj1.outfile.txt2.select.1))32: skip_on_cran()test_that("cross-sectional id le 400 binomial", { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, plinkfiles <- strsplit(system.file("extdata", "geno.bed", id = "id", family = binomial(link = "logit"), method = "REML", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] method.optim = "AI") bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") select <- match(1:400, unique(obj2$id_include)) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") select[is.na(select)] <- 0 gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj2.outfile.bed.noselect.1 <- tempfile() txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") header = TRUE, as.is = TRUE) data(example) obj2.outfile.bed.select.1 <- tempfile() suppressWarnings(RNGversion("3.5.0")) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) set.seed(123) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, pheno <- rbind(example$pheno, example$pheno[1:100, ]) header = TRUE, as.is = TRUE) pheno$id <- 1:500 expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) pheno$disease[sample(1:500, 20)] <- NA obj2.outfile.bgen.noselect.1 <- tempfile() pheno$age[sample(1:500, 20)] <- NA glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] outfile = obj2.outfile.bgen.noselect.1) pheno <- pheno[pheno$id <= 400, ] obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, kins <- example$GRM header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.bgen.select.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, method.optim = "AI") select = select, outfile = obj2.outfile.bgen.select.1) select <- match(1:400, unique(obj1$id_include)) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, header = TRUE, as.is = TRUE) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.outfile.bed.noselect.1.tmp <- tempfile() "VAR", "PVAL")]) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") quietly = TRUE)) { unlink(obj1.outfile.bed.noselect.1.tmp) obj2.outfile.gds.noselect.1 <- tempfile() obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.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$PVAL)), signif(c(0.003738918, obj1.outfile.bgen.noselect.1.tmp <- tempfile() 0.996996766))) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj2.outfile.txt.select.1 <- tempfile() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.bgen.noselect.1.tmp) "Allele2")) obj1.outfile.bgen.select.1 <- tempfile() obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, 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.bgen.noselect.1, obj1.bgen.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", select = select, infile.header.print = c("SNP", "Allele1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, "Allele2")) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, "VAR", "PVAL")]) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt.select.1, obj2.txt1.select.1) quietly = TRUE)) { obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.1.tmp <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, ncores = 2) header = TRUE, as.is = TRUE) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) header = TRUE, as.is = TRUE) idx <- sample(nrow(pheno)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) pheno <- pheno[idx, ] unlink(obj1.outfile.gds.noselect.1.tmp) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 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(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) obj1.outfile.bed.noselect.2 <- tempfile() expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.txt.select.1 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) outfile = obj1.outfile.bgen.noselect.2) obj1.outfile.txt.select.1.tmp <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.select.2 <- tempfile() "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(obj1.outfile.txt.select.1.tmp) select = select, outfile = obj1.outfile.bgen.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) 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.2 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) "Allele2")) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.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.txt.select.1, obj1.txt2.select.1) expect_equal(obj1.gds.select.1, obj1.gds.select.2) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, } obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.2 <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, skip_on_cran() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, 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(obj2$id_include)) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj1.outfile.txt1.select.2 <- tempfile() obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) "Allele2")) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, outfile = obj2.outfile.bgen.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) obj2.outfile.bgen.select.1 <- tempfile() obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) method.optim = "AI") expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", select <- match(1:400, unique(obj2$id_include)) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, select[is.na(select)] <- 0 c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bed.noselect.2 <- tempfile() "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) quietly = TRUE)) { obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj2.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, outfile = obj2.outfile.bgen.noselect.2) 0.996996766))) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.outfile.bgen.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj2.outfile.bgen.select.2) "Allele2")) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.outfile.txt1.select.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) "Allele2")) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.gds.select.2 <- tempfile() obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.gds.select.1, obj2.gds.select.2) "Allele2")) } obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj2.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, idx <- sample(nrow(pheno)) select = select, infile.header.print = c("SNP", "Allele1", pheno <- pheno[idx, ] "Allele2")) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.txt.select.1, obj2.txt.select.2) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.txt1.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj1.outfile.bed.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.2 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, expect_equal(obj1.bed.select.1, obj1.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj1.outfile.bgen.noselect.2) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() idx <- sample(nrow(kins)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, kins <- kins[idx, idx] select = select, outfile = obj1.outfile.bgen.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select <- match(1:400, unique(obj1$id_include)) quietly = TRUE)) { select[is.na(select)] <- 0 obj1.outfile.gds.noselect.2 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.2 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.bed.select.3 <- tempfile() obj1.gds.select.2 <- read.table(obj1.outfile.gds.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.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) outfile = obj1.outfile.bgen.noselect.3) obj1.txt.select.2 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.bgen.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) select = select, outfile = obj1.outfile.bgen.select.3) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.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.select.1, obj1.bgen.select.3) obj1.outfile.txt2.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) "Allele2")) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.gds.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) method.optim = "AI") obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) } obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.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.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) 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, header = TRUE, as.is = TRUE) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) quietly = TRUE)) { obj1.outfile.txt2.select.3 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.gds.select.2 <- read.table(obj2.outfile.gds.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.gds.select.1, obj2.gds.select.2) } method.optim = "AI") obj2.outfile.txt.select.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) "Allele2")) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.bed.select.3 <- tempfile() obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.select.1, obj2.bed.select.3) "Allele2")) obj2.outfile.bgen.noselect.3 <- tempfile() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj2.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.outfile.bgen.select.3 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) kins <- kins[idx, idx] if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, quietly = TRUE)) { id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.gds.noselect.3 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.3 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.gds.select.3 <- tempfile() obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) 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.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj1.outfile.bgen.noselect.3) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.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.txt.select.1, obj2.txt.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.outfile.txt1.select.3 <- tempfile() obj1.outfile.bgen.select.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, 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) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) quietly = TRUE)) { obj2.outfile.txt2.select.3 <- tempfile() obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj1.outfile.gds.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.txt2.select.1)) } unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.txt.select.3 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.2)) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, "Allele2")) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2)) expect_equal(obj1.txt.select.1, obj1.txt.select.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.3)) "Allele2")) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.outfile.txt2.select.3)) obj1.outfile.txt2.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, quietly = TRUE)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, "Allele2")) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", }) method.optim = "AI")
select <- match(1:400, unique(obj2$id_include))33: select[is.na(select)] <- 0eval(code, test_env) obj2.outfile.bed.noselect.3 <- tempfile()
glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)34: obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)35: obj2.outfile.bed.select.3 <- tempfile()withCallingHandlers({ glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) eval(code, test_env) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj2.bed.select.1, obj2.bed.select.3) skip("On CRAN") obj2.outfile.bgen.noselect.3 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, else if (!new_expectations && skip_on_empty) { outfile = obj2.outfile.bgen.noselect.3) skip_empty() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) if (on_cran()) { obj2.outfile.bgen.select.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj2.outfile.bgen.select.3)}, snapshot_on_cran = function(cnd) { obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", error = handle_error, interrupt = handle_interrupt) quietly = TRUE)) {
obj2.outfile.gds.noselect.3 <- tempfile()36: glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)
obj2.outfile.gds.select.3 <- tempfile()37: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3,
header = TRUE, as.is = TRUE)38: expect_equal(obj2.gds.select.1, obj2.gds.select.3)tryCatchList(expr, classes, parentenv, handlers) }
obj2.outfile.txt.select.3 <- tempfile()39: glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, tryCatch(withCallingHandlers({ 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", new_expectations <- the$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt.select.3) else if (!new_expectations && skip_on_empty) { obj2.outfile.txt1.select.3 <- tempfile() skip_empty() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, infile.header.print = c("SNP", "Allele1", if (on_cran()) { "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)}, snapshot_on_cran = function(cnd) { obj2.outfile.txt2.select.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 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")) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,
header = TRUE, as.is = TRUE)40: expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)doWithOneRestart(return(expr), restart) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,
obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, 41: obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, withOneRestart(expr, restarts[[1L]]) obj2.outfile.txt2.select.1))
unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, 42: obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, withRestarts(tryCatch(withCallingHandlers({ obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, eval(code, test_env) obj1.outfile.txt2.select.2)) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, if (snapshot_skipped) { obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, skip("On CRAN") obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) } unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, else if (!new_expectations && skip_on_empty) { obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, skip_empty() obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, } obj1.outfile.txt2.select.3))}, expectation = handle_expectation, packageNotFoundError = function(e) { unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, if (on_cran()) { obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, } obj2.outfile.txt2.select.3))}, snapshot_on_cran = function(cnd) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", snapshot_skipped <<- TRUE quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) end_test = function() {}) })
33: eval(code, test_env)43:
test_code(code = exprs, env = env, reporter = get_reporter() %||% 34: StopReporter$new())eval(code, test_env)
44: 35: source_file(path, env = env(env), desc = desc, shuffle = shuffle, withCallingHandlers({ error_call = error_call) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) {45: skip("On CRAN")FUN(X[[i]], ...) } else if (!new_expectations && skip_on_empty) {
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$package, "} is not installed."))
}47: }, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUEdoTryCatch(return(expr), name, parentenv, handler) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 48: error = handle_error, interrupt = handle_interrupt)tryCatchOne(expr, names, parentenv, handlers[[1L]])
36: 49: doTryCatch(return(expr), name, parentenv, handler)tryCatchList(expr, classes, parentenv, handlers)
37: 50: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatch(code, testthat_abort_reporter = function(cnd) {
cat(conditionMessage(cnd), "\n")38: NULLtryCatchList(expr, classes, parentenv, handlers)})
39: 51: tryCatch(withCallingHandlers({with_reporter(reporters$multi, lapply(test_paths, test_one_file, eval(code, test_env) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {52: skip("On CRAN")test_files_serial(test_dir = test_dir, test_package = test_package, } else if (!new_expectations && skip_on_empty) { test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, skip_empty() } env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, }, expectation = handle_expectation, packageNotFoundError = function(e) { desc = desc, load_package = load_package, shuffle = shuffle, if (on_cran()) { error_call = error_call) skip(paste0("{", e$package, "} is not installed."))
}53: }, snapshot_on_cran = function(cnd) {test_files(test_dir = path, test_paths = test_paths, test_package = package, snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") reporter = reporter, load_helpers = load_helpers, env = env, }, skip = handle_skip, warning = handle_warning, message = handle_message, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) load_package = load_package, parallel = parallel, shuffle = shuffle)
40: 54: doWithOneRestart(return(expr), restart)
test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")41:
withOneRestart(expr, restarts[[1L]])55:
test_check("GMMAT")42: withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)An irrecoverable exception occurred. R is aborting now ...
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