Last updated on 2026-10-11 07:48:59 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 44.57 | 239.79 | 284.36 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.73 | 205.75 | 244.48 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 156.48 | 186.48 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 41.00 | 168.67 | 209.67 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.86 | 231.73 | 283.59 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 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 | 313.00 | 383.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 80.00 | 96.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 384.00 | 470.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
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: doTryCatch(return(expr), name, parentenv, handler) 3:
doTryCatch(return(expr), name, parentenv, handler) 4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
4: 5: tryCatchList(expr, classes, parentenv, handlers)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) 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:
9: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
10: 10: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: 11: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
12: 12: tryCatch(expr, error = function(e) { call <- conditionCall(e)tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) call <- sys.call(-4L) prefix <- paste("Error in", dcall, ": ") dcall <- deparse(call, nlines = 1L) LONG <- 75L prefix <- paste("Error in", dcall, ": ") sm <- strsplit(conditionMessage(e), "\n")[[1L]] LONG <- 75L w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") sm <- strsplit(conditionMessage(e), "\n")[[1L]] if (is.na(w)) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], if (is.na(w)) type = "b") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], if (w > LONG) type = "b") prefix <- paste0(prefix, "\n ") if (w > LONG) } prefix <- paste0(prefix, "\n ") else prefix <- "Error : " } msg <- paste0(prefix, conditionMessage(e), "\n") else prefix <- "Error : " .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(printDeferredWarnings()) .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) { invisible(structure(msg, class = "try-error", condition = e)) cat(msg, file = outFile)}) .Internal(printDeferredWarnings())
}13: invisible(structure(msg, class = "try-error", condition = e))try(lapply(X = S, FUN = FUN, ...), silent = TRUE)})
14: 13: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: 15: FUN(X[[i]], ...)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$P, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) 1) } } else { else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) 1) } } } } else { else { if (bgenInfo$LayoutFlag == 2) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, 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) } } else { else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, 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: 20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 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: 21: eval(code, test_env)eval(code, test_env)
22: 22: eval(code, test_env)eval(code, test_env)
23: 23: withCallingHandlers({withCallingHandlers({ eval(code, test_env) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { if (snapshot_skipped) { skip("On CRAN") skip("On CRAN") } } else if (!new_expectations && skip_on_empty) { else if (!new_expectations && skip_on_empty) { skip_empty() skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) skip(paste0("{", e$package, "} is not installed.")) } }}, snapshot_on_cran = function(cnd) {}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt) error = handle_error, interrupt = handle_interrupt)
24: 24: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
25: 25: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: 26: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
27: 27: tryCatch(withCallingHandlers({tryCatch(withCallingHandlers({ eval(code, test_env) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { if (snapshot_skipped) { skip("On CRAN") skip("On CRAN") } } else if (!new_expectations && skip_on_empty) { else if (!new_expectations && skip_on_empty) { skip_empty() skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) skip(paste0("{", e$package, "} is not installed.")) } }}, snapshot_on_cran = function(cnd) {}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
28: 28: doWithOneRestart(return(expr), restart)doWithOneRestart(return(expr), restart)
29: 29: withOneRestart(expr, restarts[[1L]])withOneRestart(expr, restarts[[1L]])
30: 30: withRestarts(tryCatch(withCallingHandlers({withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { if (snapshot_skipped) { skip("On CRAN") skip("On CRAN") } } else if (!new_expectations && skip_on_empty) { else if (!new_expectations && skip_on_empty) { skip_empty() skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) {}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) } skip(paste0("{", e$package, "} is not installed."))}, snapshot_on_cran = function(cnd) { } snapshot_skipped <<- TRUE}, snapshot_on_cran = function(cnd) { invokeRestart("muffle_cran_snapshot") snapshot_skipped <<- TRUE}, skip = handle_skip, warning = handle_warning, message = handle_message, invokeRestart("muffle_cran_snapshot") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), }, skip = handle_skip, warning = handle_warning, message = handle_message, end_test = function() { error = handle_error, interrupt = handle_interrupt), error = handle_fatal), }) end_test = function() {
})31:
test_code(code, parent.frame())31:
test_code(code, parent.frame())32:
test_that("cross-sectional id le 400 binomial", {32: plinkfiles <- strsplit(system.file("extdata", "geno.bed", test_that("cross-sectional id le 400 binomial", { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] plinkfiles <- strsplit(system.file("extdata", "geno.bed", bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") package = "GMMAT"), ".bed", fixed = TRUE)[[1]] samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") data(example) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") suppressWarnings(RNGversion("3.5.0")) data(example) suppressWarnings(RNGversion("3.5.0")) set.seed(123) set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno$id <- 1:500 pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.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.bed.noselect.1.tmp <- tempfile() obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 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) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 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(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) select = select, outfile = obj1.outfile.bgen.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, 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() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, ncores = 2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, ncores = 2) header = TRUE, as.is = TRUE) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.gds.noselect.1.tmp) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.outfile.gds.select.1 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.outfile.gds.select.1 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) 0.986534857))) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) 0.986534857))) } unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.outfile.txt.select.1 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.outfile.txt.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.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.txt.select.1 <- read.table(obj1.outfile.txt.select.1, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.outfile.txt.select.1.tmp <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.txt.select.1.tmp) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.txt1.select.1 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt.select.1, obj1.txt1.select.1) "Allele2")) obj1.outfile.txt2.select.1 <- tempfile() obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.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.txt.select.1, obj1.txt1.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt2.select.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.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(obj1.txt.select.1, obj1.txt2.select.1) "Allele2")) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.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(obj1.txt.select.1, obj1.txt2.select.1) obj1.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, skip_on_cran() obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt2.select.1)) method.optim = "AI") skip_on_cran() select <- match(1:400, unique(obj2$id_include)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.1 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) select <- match(1:400, unique(obj2$id_include)) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.1 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.outfile.bgen.noselect.1 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.1) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.1 <- tempfile() outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, select = select, outfile = obj2.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.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.bgen.noselect.1, obj2.bgen.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "VAR", "PVAL")]) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", quietly = TRUE)) { "VAR", "PVAL")]) obj2.outfile.gds.noselect.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) quietly = TRUE)) { obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.gds.select.1 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.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$PVAL)), signif(c(0.003738918, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) 0.996996766))) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) } expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj2.outfile.txt.select.1 <- tempfile() 0.996996766))) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) "Allele2")) obj2.outfile.txt2.select.1 <- tempfile() obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, 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.txt1.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.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.txt2.select.1) "Allele2")) idx <- sample(nrow(pheno)) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, pheno <- pheno[idx, ] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) id = "id", family = binomial(link = "logit"), method = "REML", idx <- sample(nrow(pheno)) method.optim = "AI") pheno <- pheno[idx, ] select <- match(1:400, unique(obj1$id_include)) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) method.optim = "AI") obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, 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.noselect.1, obj1.bed.noselect.2) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bed.select.2 <- tempfile() obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) outfile = obj1.outfile.bgen.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) select = select, outfile = obj1.outfile.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.2 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) } obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.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(obj1.gds.select.1, obj1.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj1.txt.select.1, obj1.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) select <- match(1:400, unique(obj2$id_include)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select <- match(1:400, unique(obj2$id_include)) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.2 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.bgen.select.2 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select = select, outfile = obj2.outfile.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.outfile.gds.select.2 <- tempfile() } glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.outfile.txt.select.2 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, 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(obj2.gds.select.1, obj2.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj2.outfile.txt.select.2 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, 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(obj2.txt.select.1, obj2.txt.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt1.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt.select.2) "Allele2")) obj2.outfile.txt1.select.2 <- tempfile() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.outfile.txt2.select.2 <- tempfile() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) "Allele2")) idx <- sample(nrow(kins)) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, kins <- kins[idx, idx] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) id = "id", family = binomial(link = "logit"), method = "REML", idx <- sample(nrow(kins)) method.optim = "AI") kins <- kins[idx, idx] select <- match(1:400, unique(obj1$id_include)) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.bed.noselect.3 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) select <- match(1:400, unique(obj1$id_include)) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.3 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.noselect.3 <- 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, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.noselect.3 <- tempfile() obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, 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.noselect.1, obj1.bgen.noselect.3) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj1.outfile.bgen.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { select = select, outfile = obj1.outfile.bgen.select.3) obj1.outfile.gds.noselect.3 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) quietly = TRUE)) { obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj1.outfile.gds.select.3 <- tempfile() } glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.txt.select.3 <- tempfile() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj1.txt.select.3 <- read.table(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", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj1.txt.select.1, obj1.txt.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.3 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) select <- match(1:400, unique(obj2$id_include)) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3 <- tempfile() 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.bed.noselect.1, obj2.bed.noselect.3) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.bed.select.3 <- tempfile() obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() outfile = obj2.outfile.bgen.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj2.outfile.bgen.select.3 <- tempfile() obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj2.outfile.gds.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.gds.select.1, obj2.gds.select.3) } quietly = TRUE)) { obj2.outfile.txt.select.3 <- tempfile() obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) "Allele2")) obj2.outfile.gds.select.3 <- tempfile() obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj2.txt.select.1, obj2.txt.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) "Allele2")) obj2.outfile.txt2.select.3 <- tempfile() obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 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.3) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.outfile.txt1.select.3 <- tempfile() obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, "Allele2")) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1)) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.outfile.txt2.select.3 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.2)) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, "Allele2")) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2)) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj1.outfile.txt2.select.3)) obj2.outfile.txt2.select.1)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj2.outfile.txt2.select.3)) obj1.outfile.txt2.select.2)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, quietly = TRUE)) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.txt2.select.2)) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, }) obj1.outfile.txt2.select.3))
unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, 33: obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, eval(code, test_env) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3,
obj2.outfile.txt2.select.3))34: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", eval(code, test_env) quietly = TRUE))
unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, 35: obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, withCallingHandlers({ obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, eval(code, test_env) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) new_expectations <- the$test_expectations > starting_expectations}) if (snapshot_skipped) {
skip("On CRAN")33: }eval(code, test_env) else if (!new_expectations && skip_on_empty) {
skip_empty()34: }eval(code, test_env)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {35: skip(paste0("{", e$package, "} is not installed."))withCallingHandlers({ } eval(code, test_env)}, snapshot_on_cran = function(cnd) { new_expectations <- the$test_expectations > starting_expectations snapshot_skipped <<- TRUE if (snapshot_skipped) { invokeRestart("muffle_cran_snapshot") skip("On CRAN")}, skip = handle_skip, warning = handle_warning, message = handle_message, } error = handle_error, interrupt = handle_interrupt) else if (!new_expectations && skip_on_empty) {
skip_empty()36: }doTryCatch(return(expr), name, parentenv, handler)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) {37: skip(paste0("{", e$package, "} is not installed."))tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
}, snapshot_on_cran = function(cnd) {38: snapshot_skipped <<- TRUEtryCatchList(expr, classes, parentenv, handlers) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 39: error = handle_error, interrupt = handle_interrupt)tryCatch(withCallingHandlers({ eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations36: if (snapshot_skipped) {doTryCatch(return(expr), name, parentenv, handler) skip("On CRAN")
}37: else if (!new_expectations && skip_on_empty) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) skip_empty()
}38: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatchList(expr, classes, parentenv, handlers) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))39: }tryCatch(withCallingHandlers({}, snapshot_on_cran = function(cnd) { eval(code, test_env) snapshot_skipped <<- TRUE new_expectations <- the$test_expectations > starting_expectations invokeRestart("muffle_cran_snapshot") if (snapshot_skipped) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip("On CRAN") error = handle_error, interrupt = handle_interrupt), error = handle_fatal) }
else if (!new_expectations && skip_on_empty) {40: skip_empty()doWithOneRestart(return(expr), restart) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {41: if (on_cran()) {withOneRestart(expr, restarts[[1L]]) skip(paste0("{", e$package, "} is not installed."))
}42: }, snapshot_on_cran = function(cnd) {withRestarts(tryCatch(withCallingHandlers({ snapshot_skipped <<- TRUE eval(code, test_env) invokeRestart("muffle_cran_snapshot") new_expectations <- the$test_expectations > starting_expectations}, skip = handle_skip, warning = handle_warning, message = handle_message, if (snapshot_skipped) { error = handle_error, interrupt = handle_interrupt), error = handle_fatal) skip("On CRAN")
}40: else if (!new_expectations && skip_on_empty) {doWithOneRestart(return(expr), restart) skip_empty()
}41: }, expectation = handle_expectation, packageNotFoundError = function(e) {withOneRestart(expr, restarts[[1L]]) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))42: }withRestarts(tryCatch(withCallingHandlers({}, snapshot_on_cran = function(cnd) { eval(code, test_env) snapshot_skipped <<- TRUE new_expectations <- the$test_expectations > starting_expectations invokeRestart("muffle_cran_snapshot") if (snapshot_skipped) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip("On CRAN") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), } end_test = function() { else if (!new_expectations && skip_on_empty) { }) skip_empty()
}43: }, expectation = handle_expectation, packageNotFoundError = function(e) {test_code(code = exprs, env = env, reporter = get_reporter() %||% if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) StopReporter$new()) }
}, snapshot_on_cran = function(cnd) {44: snapshot_skipped <<- TRUEsource_file(path, env = env(env), desc = desc, shuffle = shuffle, invokeRestart("muffle_cran_snapshot") error_call = error_call)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
end_test = function() {45: })FUN(X[[i]], ...)
43: 46: test_code(code = exprs, env = env, reporter = get_reporter() %||% lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call) StopReporter$new())
44: 47: source_file(path, env = env(env), desc = desc, shuffle = shuffle, doTryCatch(return(expr), name, parentenv, handler) error_call = error_call)
48: 45: tryCatchOne(expr, names, parentenv, handlers[[1L]])FUN(X[[i]], ...)
49: 46: tryCatchList(expr, classes, parentenv, handlers)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,
error_call = error_call)50:
tryCatch(code, testthat_abort_reporter = function(cnd) {47: cat(conditionMessage(cnd), "\n")doTryCatch(return(expr), name, parentenv, handler) NULL
})48:
tryCatchOne(expr, names, parentenv, handlers[[1L]])51:
with_reporter(reporters$multi, lapply(test_paths, test_one_file, 49: env = env, desc = desc, shuffle = shuffle, error_call = error_call))tryCatchList(expr, classes, parentenv, handlers)
52: 50: test_files_serial(test_dir = test_dir, test_package = test_package, tryCatch(code, testthat_abort_reporter = function(cnd) { test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, cat(conditionMessage(cnd), "\n") env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, NULL desc = desc, load_package = load_package, shuffle = shuffle, }) error_call = error_call)
51: 53: with_reporter(reporters$multi, lapply(test_paths, test_one_file, test_files(test_dir = path, test_paths = test_paths, test_package = package, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) reporter = reporter, load_helpers = load_helpers, env = env,
stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 52: load_package = load_package, parallel = parallel, shuffle = shuffle)test_files_serial(test_dir = test_dir, test_package = test_package,
test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, 54: env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, test_dir("testthat", package = package, reporter = reporter, desc = desc, load_package = load_package, shuffle = shuffle, ..., load_package = "installed") error_call = error_call)
55: 53: test_check("GMMAT")test_files(test_dir = path, test_paths = test_paths, test_package = package,
reporter = reporter, load_helpers = load_helpers, env = env, An irrecoverable exception occurred. R is aborting now ...
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