Last updated on 2026-10-14 17:54:05 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.06 | 240.59 | 283.65 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.79 | 212.40 | 251.19 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 156.48 | 186.48 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 168.79 | 210.79 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.86 | 231.73 | 283.59 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 50.71 | 234.78 | 285.49 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 70.00 | 314.00 | 384.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 77.00 | 94.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 90.00 | 396.00 | 486.00 | 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,
Traceback:
1) 1: }eval(c.expr, envir = args, enclos = envir) }
else { 2: if (bgenInfo$LayoutFlag == 2) {eval(c.expr, envir = args, enclos = envir) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$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$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1)
} 4: else {tryCatchOne(expr, names, parentenv, handlers[[1L]]) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 5: "_tmp.", i), center2, MAF.range[1], MAF.range[2], tryCatchList(expr, classes, parentenv, handlers) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 6: bgenInfo$N, bgenInfo$CompressionFlag, 1) }tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) }
} 7:
FUN(X[[i]], ...)20:
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 8: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)lapply(X = S, FUN = FUN, ...)
21: 9: eval(code, test_env)doTryCatch(return(expr), name, parentenv, handler)
22: 10: eval(code, test_env)tryCatchOne(expr, names, parentenv, handlers[[1L]])
23:
withCallingHandlers({ eval(code, test_env)11: new_expectations <- the$test_expectations > starting_expectationstryCatchList(expr, classes, parentenv, handlers) if (snapshot_skipped) {
skip("On CRAN")12: } else if (!new_expectations && skip_on_empty) {tryCatch(expr, error = function(e) { skip_empty() call <- conditionCall(e) } if (!is.null(call)) {}, expectation = handle_expectation, packageNotFoundError = function(e) { if (identical(call[[1L]], quote(doTryCatch))) if (on_cran()) { call <- sys.call(-4L) skip(paste0("{", e$package, "} is not installed.")) dcall <- deparse(call, nlines = 1L) } prefix <- paste("Error in", dcall, ": ")}, snapshot_on_cran = function(cnd) { LONG <- 75L snapshot_skipped <<- TRUE sm <- strsplit(conditionMessage(e), "\n")[[1L]] invokeRestart("muffle_cran_snapshot") w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")}, skip = handle_skip, warning = handle_warning, message = handle_message, if (is.na(w)) error = handle_error, interrupt = handle_interrupt) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")24: if (w > LONG) doTryCatch(return(expr), name, parentenv, handler) prefix <- paste0(prefix, "\n ")
}25: else prefix <- "Error : "tryCatchOne(expr, names, parentenv, handlers[[1L]])
msg <- paste0(prefix, conditionMessage(e), "\n")26: .Internal(seterrmessage(msg[1L]))tryCatchList(expr, classes, parentenv, handlers) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)27: .Internal(printDeferredWarnings())tryCatch(withCallingHandlers({ } eval(code, test_env) invisible(structure(msg, class = "try-error", condition = e)) new_expectations <- the$test_expectations > starting_expectations}) if (snapshot_skipped) {
skip("On CRAN")13: }try(lapply(X = S, FUN = FUN, ...), silent = TRUE) else if (!new_expectations && skip_on_empty) {
skip_empty()14: }sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))15: }FUN(X[[i]], ...)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE16: invokeRestart("muffle_cran_snapshot")lapply(seq_len(cores), inner.do)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal)17:
mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, 28: mc.silent = silent, mc.cores = cores)doWithOneRestart(return(expr), restart)
18: 29: e$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$end[i], else if (!new_expectations && skip_on_empty) { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, skip_empty() 1) }}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { else { skip(paste0("{", e$package, "} is not installed.")) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, } infile, paste0(outfile, "_tmp.", i), center2, }, snapshot_on_cran = function(cnd) { MAF.range[1], MAF.range[2], miss.cutoff, miss.method, snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") nperbatch, select, threadInfo$begin[i], threadInfo$end[i], }, skip = handle_skip, warning = handle_warning, message = handle_message, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 1) end_test = function() { } }) }
else {31: if (bgenInfo$LayoutFlag == 2) {test_code(code, parent.frame()) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i,
obj$Sigma_iX, obj$cov, infile, paste0(outfile, 32: "_tmp.", i), center2, MAF.range[1], MAF.range[2], test_that("cross-sectional id le 400 binomial", { miss.cutoff, miss.method, nperbatch, select, plinkfiles <- strsplit(system.file("extdata", "geno.bed", threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenInfo$N, bgenInfo$CompressionFlag, 1) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") } samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") else { gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj$Sigma_iX, obj$cov, infile, paste0(outfile, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") "_tmp.", i), center2, MAF.range[1], MAF.range[2], data(example) miss.cutoff, miss.method, nperbatch, select, suppressWarnings(RNGversion("3.5.0")) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], set.seed(123) bgenInfo$N, bgenInfo$CompressionFlag, 1) pheno <- rbind(example$pheno, example$pheno[1:100, ]) } pheno$id <- 1:500 } pheno$disease[sample(1:500, 20)] <- NA} pheno$age[sample(1:500, 20)] <- NA
pheno$sex[sample(1:500, 20)] <- NA20: pheno <- pheno[sample(1:500, 450), ]glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) pheno <- pheno[pheno$id <= 400, ]
kins <- example$GRM21: obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, eval(code, test_env) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI")22: select <- match(1:400, unique(obj1$id_include))eval(code, test_env) select[is.na(select)] <- 0
obj1.outfile.bed.noselect.1 <- tempfile()23: glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)withCallingHandlers({ obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.bed.noselect.1.tmp <- tempfile() if (snapshot_skipped) { expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, skip("On CRAN") ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") } unlink(obj1.outfile.bed.noselect.1.tmp) else if (!new_expectations && skip_on_empty) { obj1.outfile.bed.select.1 <- tempfile() skip_empty() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.bgen.noselect.1 <- tempfile() if (on_cran()) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip(paste0("{", e$package, "} is not installed.")) outfile = obj1.outfile.bgen.noselect.1) } obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE obj1.outfile.bgen.noselect.1.tmp <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }, skip = handle_skip, warning = handle_warning, message = handle_message, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) error = handle_error, interrupt = handle_interrupt) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp,
header = TRUE, as.is = TRUE)24: expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)doTryCatch(return(expr), name, parentenv, handler) unlink(obj1.outfile.bgen.noselect.1.tmp)
obj1.outfile.bgen.select.1 <- tempfile()25: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, tryCatchOne(expr, names, parentenv, handlers[[1L]]) select = select, outfile = obj1.outfile.bgen.select.1)
obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, 26: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)
expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", 27: "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, tryCatch(withCallingHandlers({ c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", eval(code, test_env) "VAR", "PVAL")]) new_expectations <- the$test_expectations > starting_expectations if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (snapshot_skipped) { quietly = TRUE)) { skip("On CRAN") obj1.outfile.gds.noselect.1 <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) else if (!new_expectations && skip_on_empty) { obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, skip_empty() header = TRUE, as.is = TRUE) } obj1.outfile.gds.noselect.1.tmp <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, if (on_cran()) { ncores = 2) skip(paste0("{", e$package, "} is not installed.")) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) snapshot_skipped <<- TRUE unlink(obj1.outfile.gds.noselect.1.tmp) invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.select.1 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1,
header = TRUE, as.is = TRUE)28: expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))doWithOneRestart(return(expr), restart) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942,
0.986534857)))29: unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))withOneRestart(expr, restarts[[1L]]) }
obj1.outfile.txt.select.1 <- tempfile()30: glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, withRestarts(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$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) else if (!new_expectations && skip_on_empty) { obj1.outfile.txt.select.1.tmp <- tempfile() skip_empty() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, infile.header.print = c("SNP", "Allele1", if (on_cran()) { "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, snapshot_on_cran = function(cnd) { select = select, infile.header.print = c("SNP", "Allele1", snapshot_skipped <<- TRUE "Allele2")) invokeRestart("muffle_cran_snapshot") obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), expect_equal(obj1.txt.select.1, obj1.txt1.select.1) end_test = function() { obj1.outfile.txt2.select.1 <- tempfile() }) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, 32: header = TRUE, as.is = TRUE)test_that("cross-sectional id le 400 binomial", { expect_equal(obj1.txt.select.1, obj1.txt2.select.1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj1.outfile.txt2.select.1)) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") skip_on_cran() txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") id = "id", family = binomial(link = "logit"), method = "REML", txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") method.optim = "AI") data(example) suppressWarnings(RNGversion("3.5.0")) select <- match(1:400, unique(obj2$id_include)) set.seed(123) select[is.na(select)] <- 0 pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj2.outfile.bed.noselect.1 <- tempfile() pheno$id <- 1:500 glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) pheno$disease[sample(1:500, 20)] <- NA obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, pheno$age[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() pheno$sex[sample(1:500, 20)] <- NA glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) pheno <- pheno[sample(1:500, 450), ] obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, pheno <- pheno[pheno$id <= 400, ] header = TRUE, as.is = TRUE) kins <- example$GRM expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.bgen.noselect.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj2.outfile.bgen.select.1 <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.bed.noselect.1.tmp <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.outfile.bed.select.1 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bgen.noselect.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.noselect.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) unlink(obj1.outfile.bgen.noselect.1.tmp) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj1.outfile.bgen.select.1 <- tempfile() 0.996996766))) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj1.outfile.bgen.select.1) obj2.outfile.txt.select.1 <- tempfile() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "Allele2")) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt1.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.gds.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.gds.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) ncores = 2) obj2.outfile.txt2.select.1 <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.gds.noselect.1.tmp) "Allele2")) obj1.outfile.gds.select.1 <- tempfile() obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) idx <- sample(nrow(pheno)) pheno <- pheno[idx, ] expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, 0.986534857))) select <- match(1:400, unique(obj1$id_include)) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) select[is.na(select)] <- 0 } obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.txt.select.1.tmp <- tempfile() obj1.outfile.bgen.noselect.2 <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, select = select, outfile = obj1.outfile.bgen.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt2.select.1) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, "Allele2")) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt1.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.outfile.bed.noselect.1 <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, outfile = obj2.outfile.bgen.noselect.1) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) obj2.outfile.bgen.select.1 <- tempfile() select[is.na(select)] <- 0 obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select = select, outfile = obj2.outfile.bgen.select.1) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.outfile.bed.select.2 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj2.bed.select.1, obj2.bed.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bgen.noselect.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.gds.noselect.1 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) 0.996996766))) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) { obj2.outfile.txt.select.1 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, 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) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, 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(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, expect_equal(obj2.gds.select.1, obj2.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt1.select.1) "Allele2")) obj2.outfile.txt2.select.1 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "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) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, idx <- sample(nrow(pheno)) header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.txt2.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj1$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj1.outfile.bed.noselect.2 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) kins <- kins[idx, idx] obj1.outfile.bed.select.2 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.bed.select.1, obj1.bed.select.2) select[is.na(select)] <- 0 obj1.outfile.bgen.noselect.2 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) outfile = obj1.outfile.bgen.noselect.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) outfile = obj1.outfile.bgen.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.bgen.select.3 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.3) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.outfile.gds.noselect.3 <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.outfile.txt.select.2 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 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, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj1.outfile.txt1.select.2 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.txt.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.3) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.outfile.txt1.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt2.select.3 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, select <- match(1:400, unique(obj2$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.noselect.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") obj2.outfile.bed.select.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) select[is.na(select)] <- 0 obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.bed.select.3 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.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.bgen.noselect.1, obj2.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.3 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, outfile = obj2.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) quietly = TRUE)) { obj2.outfile.bgen.select.3 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) select = select, outfile = obj2.outfile.bgen.select.3) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj2.outfile.gds.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) quietly = TRUE)) { obj2.gds.select.2 <- read.table(obj2.outfile.gds.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.gds.select.1, obj2.gds.select.2) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.outfile.gds.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, expect_equal(obj2.gds.select.1, obj2.gds.select.3) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt.select.3 <- tempfile() obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt1.select.3 <- tempfile() obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2.outfile.txt2.select.3 <- tempfile() idx <- sample(nrow(kins)) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, kins <- kins[idx, idx] infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select = select, infile.header.print = c("SNP", "Allele1", id = "id", family = binomial(link = "logit"), method = "REML", "Allele2")) method.optim = "AI") obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj1.outfile.bed.noselect.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1)) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj1.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt2.select.2)) outfile = obj1.outfile.bgen.noselect.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.bgen.select.3 <- tempfile() obj1.outfile.txt2.select.3)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3)) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) quietly = TRUE)) { unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.3 <- tempfile() obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)}) obj1.outfile.gds.select.3 <- tempfile()
glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)33: obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.select.1, obj1.gds.select.3)34: }eval(code, test_env) obj1.outfile.txt.select.3 <- tempfile()
glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, 35: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, withCallingHandlers({ select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2")) new_expectations <- the$test_expectations > starting_expectations obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj1.txt.select.1, obj1.txt.select.3) } else if (!new_expectations && skip_on_empty) { obj1.outfile.txt1.select.3 <- tempfile() skip_empty() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", "Allele2"))}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) } obj1.outfile.txt2.select.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, snapshot_skipped <<- TRUE infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot") select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2")) error = handle_error, interrupt = handle_interrupt) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3,
header = TRUE, as.is = TRUE)36: expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)doTryCatch(return(expr), name, parentenv, handler) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL,
id = "id", family = binomial(link = "logit"), method = "REML", 37: method.optim = "AI")tryCatchOne(expr, names, parentenv, handlers[[1L]]) select <- match(1:400, unique(obj2$id_include))
select[is.na(select)] <- 038: obj2.outfile.bed.noselect.3 <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)
obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE)39: expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile()tryCatch(withCallingHandlers({ glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj2.bed.select.1, obj2.bed.select.3) if (snapshot_skipped) { obj2.outfile.bgen.noselect.3 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj2.outfile.bgen.noselect.3) else if (!new_expectations && skip_on_empty) { obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.bgen.select.3 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, skip(paste0("{", e$package, "} is not installed.")) select = select, outfile = obj2.outfile.bgen.select.3) } obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) invokeRestart("muffle_cran_snapshot") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, skip = handle_skip, warning = handle_warning, message = handle_message, quietly = TRUE)) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj2.outfile.gds.noselect.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)40: obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, doWithOneRestart(return(expr), restart) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)41: obj2.outfile.gds.select.3 <- tempfile()withOneRestart(expr, restarts[[1L]])
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)42: obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, withRestarts(tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj2.gds.select.1, obj2.gds.select.3) new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) { obj2.outfile.txt.select.3 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", skip_empty() "Allele2")) } obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.txt.select.1, obj2.txt.select.3) skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt1.select.3 <- tempfile() } glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.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.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), header = TRUE, as.is = TRUE) end_test = function() { expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) }) obj2.outfile.txt2.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 43: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, test_code(code = exprs, env = env, reporter = get_reporter() %||% select = select, infile.header.print = c("SNP", "Allele1", StopReporter$new()) "Allele2"))
obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE)44: expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)source_file(path, env = env(env), desc = desc, shuffle = shuffle, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, error_call = error_call) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1,
obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, 45: obj2.outfile.txt2.select.1))FUN(X[[i]], ...) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2,
obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, 46: obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, obj1.outfile.txt2.select.2)) error_call = error_call) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,
obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, 47: obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.txt2.select.2))
unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, 48: obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3,
obj1.outfile.txt2.select.3))49: unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, tryCatchList(expr, classes, parentenv, handlers) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3,
obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, 50: obj2.outfile.txt2.select.3))tryCatch(code, testthat_abort_reporter = function(cnd) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", cat(conditionMessage(cnd), "\n") quietly = TRUE)) NULL unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, }) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2,
obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, 51: obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, with_reporter(reporters$multi, lapply(test_paths, test_one_file, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) env = env, desc = desc, shuffle = shuffle, error_call = error_call))})
52: 33: test_files_serial(test_dir = test_dir, test_package = test_package, eval(code, test_env) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 34: desc = desc, load_package = load_package, shuffle = shuffle, eval(code, test_env) error_call = error_call)
35: 53: withCallingHandlers({test_files(test_dir = path, test_paths = test_paths, test_package = package, eval(code, test_env) reporter = reporter, load_helpers = load_helpers, env = env, new_expectations <- the$test_expectations > starting_expectations stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, if (snapshot_skipped) { load_package = load_package, parallel = parallel, shuffle = shuffle) skip("On CRAN")
}54: else if (!new_expectations && skip_on_empty) {test_dir("testthat", package = package, reporter = reporter, skip_empty() ..., load_package = "installed") }
}, expectation = handle_expectation, packageNotFoundError = function(e) {55: if (on_cran()) {test_check("GMMAT") skip(paste0("{", e$package, "} is not installed."))
}}, snapshot_on_cran = function(cnd) {An irrecoverable exception occurred. R is aborting now ...
snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
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