Last updated on 2026-11-10 04:53:11 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 | 40.16 | 205.56 | 245.72 | 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 | 43.00 | 165.65 | 208.65 | 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 | 19.00 | 72.00 | 91.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 85.00 | 389.00 | 474.00 | 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:
Traceback:
1: 1: eval(c.expr, envir = args, enclos = envir)eval(c.expr, envir = args, enclos = envir)
2: 2: eval(c.expr, envir = args, enclos = envir)eval(c.expr, envir = args, enclos = envir)
3: 3: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
4: 4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
tryCatchOne(expr, names, parentenv, handlers[[1L]]) 5:
tryCatchList(expr, classes, parentenv, handlers) 5:
tryCatchList(expr, classes, parentenv, handlers) 6:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) 6:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) 7:
FUN(X[[i]], ...) 7:
FUN(X[[i]], ...) 8:
lapply(X = S, FUN = FUN, ...) 8:
lapply(X = S, FUN = FUN, ...) 9:
doTryCatch(return(expr), name, parentenv, handler) 9:
doTryCatch(return(expr), name, parentenv, handler)10:
tryCatchOne(expr, names, parentenv, handlers[[1L]])10:
tryCatchOne(expr, names, parentenv, handlers[[1L]])11:
tryCatchList(expr, classes, parentenv, handlers)11:
tryCatchList(expr, classes, parentenv, handlers)12:
tryCatch(expr, error = function(e) {12: call <- conditionCall(e)tryCatch(expr, error = function(e) { if (!is.null(call)) { call <- conditionCall(e) if (identical(call[[1L]], quote(doTryCatch))) if (!is.null(call)) { call <- sys.call(-4L) if (identical(call[[1L]], quote(doTryCatch))) dcall <- deparse(call, nlines = 1L) call <- sys.call(-4L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L dcall <- deparse(call, nlines = 1L) sm <- strsplit(conditionMessage(e), "\n")[[1L]] prefix <- paste("Error in", dcall, ": ") w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") LONG <- 75L if (is.na(w)) sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") type = "b") if (is.na(w)) if (w > LONG) prefix <- paste0(prefix, "\n ") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b") else prefix <- "Error : " if (w > LONG) msg <- paste0(prefix, conditionMessage(e), "\n") prefix <- paste0(prefix, "\n ") .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) { else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") cat(msg, file = outFile) .Internal(seterrmessage(msg[1L])) .Internal(printDeferredWarnings()) if (!silent && isTRUE(getOption("show.error.messages"))) { } cat(msg, file = outFile) invisible(structure(msg, class = "try-error", condition = e)) .Internal(printDeferredWarnings())}) } invisible(structure(msg, class = "try-error", condition = e))
})13:
try(lapply(X = S, FUN = FUN, ...), silent = TRUE)13:
try(lapply(X = S, FUN = FUN, ...), silent = TRUE)14:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))14:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)15:
FUN(X[[i]], ...)16:
16: lapply(seq_len(cores), inner.do)lapply(seq_len(cores), inner.do)
17: 17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores) mc.silent = silent, mc.cores = cores)
18: 18: e$fun(obj, substitute(ex), parent.frame(), e$data)e$fun(obj, substitute(ex), parent.frame(), e$data)
19: 19: foreach(i = 1:ncores) %dopar% {foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], infile, paste0(outfile, "_tmp.", i), center2, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, 1) } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], else { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, 1) infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, else { 1) if (bgenInfo$LayoutFlag == 2) { } .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, } else { obj$Sigma_iX, obj$cov, infile, paste0(outfile, if (bgenInfo$LayoutFlag == 2) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, miss.cutoff, miss.method, nperbatch, select, "_tmp.", i), center2, MAF.range[1], MAF.range[2], threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], } bgenInfo$N, bgenInfo$CompressionFlag, 1) else { } .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, else { obj$Sigma_iX, obj$cov, infile, paste0(outfile, .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) bgenInfo$N, bgenInfo$CompressionFlag, 1) } } } }}
}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)21: eval(code, test_env)
22:
22: eval(code, test_env)
eval(code, test_env)23:
withCallingHandlers({ eval(code, test_env)23: withCallingHandlers({ new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { eval(code, test_env) skip("On CRAN") new_expectations <- the$test_expectations > starting_expectations } else if (!new_expectations && skip_on_empty) { if (snapshot_skipped) { skip_empty() skip("On CRAN") }}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { else if (!new_expectations && skip_on_empty) { skip(paste0("{", e$package, "} is not installed.")) } skip_empty()}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, expectation = handle_expectation, packageNotFoundError = function(e) {}, skip = handle_skip, warning = handle_warning, message = handle_message, if (on_cran()) { error = handle_error, interrupt = handle_interrupt)
skip(paste0("{", e$package, "} is not installed."))24: }doTryCatch(return(expr), name, parentenv, handler)
}, snapshot_on_cran = function(cnd) {25: tryCatchOne(expr, names, parentenv, handlers[[1L]]) snapshot_skipped <<- TRUE
26: invokeRestart("muffle_cran_snapshot")tryCatchList(expr, classes, parentenv, handlers)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt)27:
tryCatch(withCallingHandlers({24: eval(code, test_env) new_expectations <- the$test_expectations > starting_expectationsdoTryCatch(return(expr), name, parentenv, handler) if (snapshot_skipped) {
skip("On CRAN")25: } else if (!new_expectations && skip_on_empty) {tryCatchOne(expr, names, parentenv, handlers[[1L]])
skip_empty()26: tryCatchList(expr, classes, parentenv, handlers) }
27: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatch(withCallingHandlers({ if (on_cran()) { eval(code, test_env) skip(paste0("{", e$package, "} is not installed.")) new_expectations <- the$test_expectations > starting_expectations } if (snapshot_skipped) {}, snapshot_on_cran = function(cnd) { skip("On CRAN") snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") } else if (!new_expectations && skip_on_empty) {}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) skip_empty()
}28: }, expectation = handle_expectation, packageNotFoundError = function(e) {doWithOneRestart(return(expr), restart) if (on_cran()) {
29: skip(paste0("{", e$package, "} is not installed."))withOneRestart(expr, restarts[[1L]])
}30: }, snapshot_on_cran = function(cnd) {withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) snapshot_skipped <<- TRUE new_expectations <- the$test_expectations > starting_expectations invokeRestart("muffle_cran_snapshot") if (snapshot_skipped) { skip("On CRAN")}, skip = handle_skip, warning = handle_warning, message = handle_message, } else if (!new_expectations && skip_on_empty) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal) skip_empty()
}}, expectation = handle_expectation, packageNotFoundError = function(e) {28: if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))doWithOneRestart(return(expr), restart) }
}, snapshot_on_cran = function(cnd) {29: withOneRestart(expr, restarts[[1L]])
snapshot_skipped <<- TRUE30: withRestarts(tryCatch(withCallingHandlers({ invokeRestart("muffle_cran_snapshot") eval(code, test_env)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { end_test = function() { skip("On CRAN") }) }
31: else if (!new_expectations && skip_on_empty) {test_code(code, parent.frame()) skip_empty() }
}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) {32: skip(paste0("{", e$package, "} is not installed."))test_that("cross-sectional id le 400 binomial", { plinkfiles <- strsplit(system.file("extdata", "geno.bed", }}, snapshot_on_cran = function(cnd) { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") snapshot_skipped <<- TRUE samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") }) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")
txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT")31: data(example)test_code(code, parent.frame()) suppressWarnings(RNGversion("3.5.0"))
set.seed(123)32: pheno <- rbind(example$pheno, example$pheno[1:100, ])test_that("cross-sectional id le 400 binomial", { pheno$id <- 1:500 plinkfiles <- strsplit(system.file("extdata", "geno.bed", pheno$disease[sample(1:500, 20)] <- NA package = "GMMAT"), ".bed", fixed = TRUE)[[1]] pheno$age[sample(1:500, 20)] <- NA bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") pheno$sex[sample(1:500, 20)] <- NA samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") pheno <- pheno[sample(1:500, 450), ] gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") pheno <- pheno[pheno$id <= 400, ] txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 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") select <- match(1:400, unique(obj1$id_include)) data(example) suppressWarnings(RNGversion("3.5.0")) set.seed(123) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() pheno <- rbind(example$pheno, example$pheno[1:100, ]) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) pheno$id <- 1:500 obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, pheno$disease[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() pheno$age[sample(1:500, 20)] <- NA expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, pheno$sex[sample(1:500, 20)] <- NA ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] unlink(obj1.outfile.bed.noselect.1.tmp) kins <- example$GRM obj1.outfile.bed.select.1 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) select[is.na(select)] <- 0 obj1.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.outfile.bed.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.bed.noselect.1.tmp) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() unlink(obj1.outfile.bgen.noselect.1.tmp) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, select = select, outfile = obj1.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", outfile = obj1.outfile.bgen.noselect.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) obj1.outfile.bgen.noselect.1.tmp <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, ncores = 2) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) select = select, outfile = obj1.outfile.bgen.select.1) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.outfile.txt.select.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.gds.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, unlink(obj1.outfile.txt.select.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.1 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, "Allele2")) 0.986534857))) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt1.select.1) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.outfile.txt2.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 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.txt.select.1 <- read.table(obj1.outfile.txt.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.1)) select = select, infile.header.print = c("SNP", "Allele1", skip_on_cran() "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", unlink(obj1.outfile.txt.select.1.tmp) method.optim = "AI") obj1.outfile.txt1.select.1 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt1.select.1) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj2.outfile.bgen.noselect.1) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt2.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.outfile.txt2.select.1)) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, skip_on_cran() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) id = "id", family = binomial(link = "logit"), method = "REML", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", method.optim = "AI") quietly = TRUE)) { select <- match(1:400, unique(obj2$id_include)) obj2.outfile.gds.noselect.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.bed.noselect.1 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) 0.996996766))) obj2.outfile.bgen.noselect.1 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, outfile = obj2.outfile.bgen.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt1.select.1 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.1) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj2.outfile.gds.select.1 <- tempfile() idx <- sample(nrow(pheno)) pheno <- pheno[idx, ] glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select[is.na(select)] <- 0 expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj1.outfile.bed.noselect.2 <- tempfile() 0.996996766))) } obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.1 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, select = select, outfile = obj1.outfile.bgen.select.2) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) idx <- sample(nrow(pheno)) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) pheno <- pheno[idx, ] if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.gds.noselect.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) method.optim = "AI") obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.bed.noselect.2 <- tempfile() obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.bgen.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) outfile = obj1.outfile.bgen.noselect.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, select = select, outfile = obj1.outfile.bgen.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select <- match(1:400, unique(obj2$id_include)) quietly = TRUE)) { select[is.na(select)] <- 0 obj1.outfile.gds.noselect.2 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.select.1, obj1.gds.select.2) outfile = obj2.outfile.bgen.noselect.2) } obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, select = select, outfile = obj2.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj1.txt.select.1, obj1.txt.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) "Allele2")) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.outfile.txt2.select.2 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.gds.select.1, obj2.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.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.txt2.select.1, obj1.txt2.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, "Allele2")) 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(obj2$id_include)) obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.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) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) idx <- sample(nrow(kins)) kins <- kins[idx, idx] obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.bgen.select.2 <- tempfile() select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) select = select, outfile = obj2.outfile.bgen.select.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.gds.noselect.2 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) outfile = obj1.outfile.bgen.noselect.3) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3) obj2.gds.select.2 <- read.table(obj2.outfile.gds.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.bgen.select.1, obj1.bgen.select.3) expect_equal(obj2.gds.select.1, obj2.gds.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { } obj1.outfile.gds.noselect.3 <- tempfile() obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) 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")) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.txt1.select.2 <- tempfile() } glmm.score(obj2, infile = txtfile1, outfile = obj2.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, 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.2 <- read.table(obj2.outfile.txt1.select.2, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.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")) obj1.txt1.select.3 <- read.table(obj1.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(obj1.txt1.select.1, obj1.txt1.select.3) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1.outfile.txt2.select.3 <- tempfile() idx <- sample(nrow(kins)) glmm.score(obj1, infile = txtfile2, outfile = obj1.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") obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) select[is.na(select)] <- 0 obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.bed.noselect.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) method.optim = "AI") obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj2.outfile.bed.noselect.3 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, 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(obj2.bed.noselect.1, obj2.bed.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.3 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1, obj2.bed.select.3) outfile = obj1.outfile.bgen.noselect.3) obj2.outfile.bgen.noselect.3 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.3 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, 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(obj1.bgen.select.1, obj1.bgen.select.3) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { quietly = TRUE)) { obj1.outfile.gds.noselect.3 <- tempfile() obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) expect_equal(obj2.gds.select.1, obj2.gds.select.3) } obj1.outfile.txt.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj2.outfile.txt.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, "Allele2")) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.3) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.outfile.txt1.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt2.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.1)) method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, select[is.na(select)] <- 0 obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj2.outfile.bed.noselect.3 <- tempfile() obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.outfile.txt2.select.3)) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.txt2.select.3)) obj2.outfile.bgen.noselect.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.3) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.outfile.bgen.select.3 <- tempfile()}) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
select = select, outfile = obj2.outfile.bgen.select.3)33: eval(code, test_env) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE)
34: eval(code, test_env) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)
35: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) {withCallingHandlers({ eval(code, test_env) obj2.outfile.gds.noselect.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) if (snapshot_skipped) { skip("On CRAN") obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { skip_empty() } expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) if (on_cran()) { obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, skip(paste0("{", e$package, "} is not installed.")) } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj2.gds.select.1, obj2.gds.select.3) snapshot_skipped <<- TRUE } obj2.outfile.txt.select.3 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3,
36: header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3)doTryCatch(return(expr), name, parentenv, handler)
obj2.outfile.txt1.select.3 <- tempfile()37: tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3,
38: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", tryCatchList(expr, classes, parentenv, handlers)
"Allele2"))39: obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE)tryCatch(withCallingHandlers({ expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) eval(code, test_env) obj2.outfile.txt2.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) skip_empty() } obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, }}, snapshot_on_cran = function(cnd) { obj2.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, snapshot_skipped <<- TRUE obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2,
obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, 40: obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, doWithOneRestart(return(expr), restart)
obj2.outfile.txt2.select.2))41: unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, withOneRestart(expr, restarts[[1L]]) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3,
obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.3))42: unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, withRestarts(tryCatch(withCallingHandlers({ obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, eval(code, test_env) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, new_expectations <- the$test_expectations > starting_expectations obj2.outfile.txt2.select.3)) if (snapshot_skipped) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", skip("On CRAN") quietly = TRUE)) } unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, else if (!new_expectations && skip_on_empty) { skip_empty() obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, } obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) skip(paste0("{", e$package, "} is not installed."))}) }
}, snapshot_on_cran = function(cnd) {33: snapshot_skipped <<- TRUEeval(code, test_env) invokeRestart("muffle_cran_snapshot")
34: }, skip = handle_skip, warning = handle_warning, message = handle_message, eval(code, test_env)
error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 35: end_test = function() {withCallingHandlers({ })
eval(code, test_env)43: test_code(code = exprs, env = env, reporter = get_reporter() %||% new_expectations <- the$test_expectations > starting_expectations StopReporter$new()) if (snapshot_skipped) {
44: skip("On CRAN") }source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call) else if (!new_expectations && skip_on_empty) {
45: skip_empty()FUN(X[[i]], ...) }
46: }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call) }}, snapshot_on_cran = function(cnd) {
47: snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")doTryCatch(return(expr), name, parentenv, handler)
}, skip = handle_skip, warning = handle_warning, message = handle_message, 48: error = handle_error, interrupt = handle_interrupt)tryCatchOne(expr, names, parentenv, handlers[[1L]])
36:
doTryCatch(return(expr), name, parentenv, handler)49:
tryCatchList(expr, classes, parentenv, handlers)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])50:
tryCatch(code, testthat_abort_reporter = function(cnd) {38: cat(conditionMessage(cnd), "\n")tryCatchList(expr, classes, parentenv, handlers) NULL})
39: 51: tryCatch(withCallingHandlers({with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {52: skip("On CRAN")test_files_serial(test_dir = test_dir, test_package = test_package, } else if (!new_expectations && skip_on_empty) { test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, skip_empty() env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, } error_call = error_call)
}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) {53: test_files(test_dir = path, test_paths = test_paths, test_package = package, skip(paste0("{", e$package, "} is not installed.")) } reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, }, snapshot_on_cran = function(cnd) { load_package = load_package, parallel = parallel, shuffle = shuffle) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")54: }, skip = handle_skip, warning = handle_warning, message = handle_message, test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed") error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
55: 40: test_check("GMMAT")
doWithOneRestart(return(expr), restart)An irrecoverable exception occurred. R is aborting now ...
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