Last updated on 2026-10-17 08:50:18 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 44.69 | 248.52 | 293.21 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.25 | 225.46 | 264.71 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 158.92 | 187.92 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 168.79 | 210.79 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.56 | 239.28 | 290.84 | 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 | 69.00 | 319.00 | 388.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 74.00 | 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 | 90.00 | 398.00 | 488.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
22: eval(code, test_env)
23: withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
27: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
28: doWithOneRestart(return(expr), restart)
29: withOneRestart(expr, restarts[[1L]])
30: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
31: test_code(code, parent.frame())
32: test_that("cross-sectional id le 400 binomial", { plinkfiles <- strsplit(system.file("extdata", "geno.bed", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) suppressWarnings(RNGversion("3.5.0")) set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) skip_on_cran() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.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.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) } obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1",
Traceback:
"Allele2")) 1: obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, eval(c.expr, envir = args, enclos = envir) header = TRUE, as.is = TRUE)
expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) 2: obj2.outfile.txt1.select.1 <- tempfile()eval(c.expr, envir = args, enclos = envir) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 3: select = select, infile.header.print = c("SNP", "Allele1", doTryCatch(return(expr), name, parentenv, handler) "Allele2"))
obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, 4: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj2.txt.select.1, obj2.txt1.select.1)
obj2.outfile.txt2.select.1 <- tempfile() 5: glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2"))tryCatchList(expr, classes, parentenv, handlers) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1,
header = TRUE, as.is = TRUE) 6: expect_equal(obj2.txt.select.1, obj2.txt2.select.1)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) idx <- sample(nrow(pheno))
pheno <- pheno[idx, ] 7: obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, FUN(X[[i]], ...) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI") 8: lapply(X = S, FUN = FUN, ...) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0
obj1.outfile.bed.noselect.2 <- tempfile() 9: glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2)doTryCatch(return(expr), name, parentenv, handler) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2)10: obj1.outfile.bed.select.2 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2)
obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, 11: header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2)tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.bgen.noselect.2 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 12: outfile = obj1.outfile.bgen.noselect.2)tryCatch(expr, error = function(e) { obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, call <- conditionCall(e) header = TRUE, as.is = TRUE) if (!is.null(call)) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) if (identical(call[[1L]], quote(doTryCatch))) obj1.outfile.bgen.select.2 <- tempfile() call <- sys.call(-4L) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, dcall <- deparse(call, nlines = 1L) select = select, outfile = obj1.outfile.bgen.select.2) prefix <- paste("Error in", dcall, ": ") obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] header = TRUE, as.is = TRUE) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (is.na(w)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], quietly = TRUE)) { type = "b") obj1.outfile.gds.noselect.2 <- tempfile() if (w > LONG) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) prefix <- paste0(prefix, "\n ") obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, } header = TRUE, as.is = TRUE) else prefix <- "Error : " expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) msg <- paste0(prefix, conditionMessage(e), "\n") obj1.outfile.gds.select.2 <- tempfile() .Internal(seterrmessage(msg[1L])) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) if (!silent && isTRUE(getOption("show.error.messages"))) { obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, cat(msg, file = outFile) header = TRUE, as.is = TRUE) .Internal(printDeferredWarnings()) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } } invisible(structure(msg, class = "try-error", condition = e)) obj1.outfile.txt.select.2 <- tempfile()}) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", 13: "Allele2"))try(lapply(X = S, FUN = FUN, ...), silent = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2,
header = TRUE, as.is = TRUE)14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) expect_equal(obj1.txt.select.1, obj1.txt.select.2)
obj1.outfile.txt1.select.2 <- tempfile()15: glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, FUN(X[[i]], ...) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 16: "Allele2"))lapply(seq_len(cores), inner.do) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2,
17: header = TRUE, as.is = TRUE)mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2)
obj1.outfile.txt2.select.2 <- tempfile()18: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, e$fun(obj, substitute(ex), parent.frame(), e$data) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))19: obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE)foreach(i = 1:ncores) %dopar% { expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) if (!is.null(obj$P)) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, if (bgenInfo$LayoutFlag == 2) { id = "id", family = binomial(link = "logit"), method = "REML", .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, select[is.na(select)] <- 0 nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj2.outfile.bed.noselect.2 <- tempfile() threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) 1) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, } header = TRUE, as.is = TRUE) else { expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, obj2.outfile.bed.select.2 <- tempfile() MAF.range[1], MAF.range[2], miss.cutoff, miss.method, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, header = TRUE, as.is = TRUE) 1) expect_equal(obj2.bed.select.1, obj2.bed.select.2) } obj2.outfile.bgen.noselect.2 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, else { outfile = obj2.outfile.bgen.noselect.2) if (bgenInfo$LayoutFlag == 2) { obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, header = TRUE, as.is = TRUE) obj$Sigma_iX, obj$cov, infile, paste0(outfile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj2.outfile.bgen.select.2 <- tempfile() miss.cutoff, miss.method, nperbatch, select, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], select = select, outfile = obj2.outfile.bgen.select.2) bgenInfo$N, bgenInfo$CompressionFlag, 1) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, } header = TRUE, as.is = TRUE) else { expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj$Sigma_iX, obj$cov, infile, paste0(outfile, quietly = TRUE)) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj2.outfile.gds.noselect.2 <- tempfile() miss.cutoff, miss.method, nperbatch, select, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, bgenInfo$N, bgenInfo$CompressionFlag, 1) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) } obj2.outfile.gds.select.2 <- tempfile()} glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2)
obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, 20: header = TRUE, as.is = TRUE)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.select.1, obj2.gds.select.2) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) }
obj2.outfile.txt.select.2 <- tempfile()21: glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, eval(code, test_env)
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 22: eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))23: obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj2.txt.select.1, obj2.txt.select.2) new_expectations <- the$test_expectations > starting_expectations obj2.outfile.txt1.select.2 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) if (on_cran()) { obj2.outfile.txt2.select.2 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, snapshot_on_cran = function(cnd) { select = select, infile.header.print = c("SNP", "Allele1", snapshot_skipped <<- TRUE "Allele2")) invokeRestart("muffle_cran_snapshot") obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)
idx <- sample(nrow(kins))24: kins <- kins[idx, idx]doTryCatch(return(expr), name, parentenv, handler) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 25: method.optim = "AI")tryCatchOne(expr, names, parentenv, handlers[[1L]]) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 026: obj1.outfile.bed.noselect.3 <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3)
obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, 27: header = TRUE, as.is = TRUE)tryCatch(withCallingHandlers({ expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) eval(code, test_env) obj1.outfile.bed.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) if (snapshot_skipped) { obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1, obj1.bed.select.3) else if (!new_expectations && skip_on_empty) { obj1.outfile.bgen.noselect.3 <- tempfile() skip_empty() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) } obj1.outfile.bgen.select.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE select = select, outfile = obj1.outfile.bgen.select.3) invokeRestart("muffle_cran_snapshot") obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) {28: obj1.outfile.gds.noselect.3 <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)
obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 29: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)
obj1.outfile.gds.select.3 <- tempfile()30: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)withRestarts(tryCatch(withCallingHandlers({ obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.gds.select.1, obj1.gds.select.3) if (snapshot_skipped) { } skip("On CRAN") obj1.outfile.txt.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, else if (!new_expectations && skip_on_empty) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_empty() select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.txt.select.1, obj1.txt.select.3) skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt1.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, }, snapshot_on_cran = function(cnd) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, snapshot_skipped <<- TRUE select = select, infile.header.print = c("SNP", "Allele1", invokeRestart("muffle_cran_snapshot") "Allele2"))}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), header = TRUE, as.is = TRUE) end_test = function() { expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) }) obj1.outfile.txt2.select.3 <- tempfile()
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, 31: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, test_code(code, parent.frame()) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))32: obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, test_that("cross-sectional id le 400 binomial", { header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] id = "id", family = binomial(link = "logit"), method = "REML", bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") method.optim = "AI") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") select <- match(1:400, unique(obj2$id_include)) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") select[is.na(select)] <- 0 txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj2.outfile.bed.noselect.3 <- tempfile() txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, data(example) header = TRUE, as.is = TRUE) suppressWarnings(RNGversion("3.5.0")) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) set.seed(123) obj2.outfile.bed.select.3 <- tempfile() pheno <- rbind(example$pheno, example$pheno[1:100, ]) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) pheno$id <- 1:500 obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, pheno$disease[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$age[sample(1:500, 20)] <- NA expect_equal(obj2.bed.select.1, obj2.bed.select.3) pheno$sex[sample(1:500, 20)] <- NA obj2.outfile.bgen.noselect.3 <- tempfile() pheno <- pheno[sample(1:500, 450), ] glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, pheno <- pheno[pheno$id <= 400, ] outfile = obj2.outfile.bgen.noselect.3) kins <- example$GRM obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) method.optim = "AI") obj2.outfile.bgen.select.3 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select[is.na(select)] <- 0 select = select, outfile = obj2.outfile.bgen.select.3) obj1.outfile.bed.noselect.1 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.bed.noselect.1.tmp <- tempfile() quietly = TRUE)) { expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, unlink(obj1.outfile.bed.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj2.outfile.gds.select.3 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3 <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) select = select, infile.header.print = c("SNP", "Allele1", obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) unlink(obj1.outfile.bgen.noselect.1.tmp) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj1.outfile.bgen.select.1 <- tempfile() obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj1.bgen.select.1 <- read.table(obj1.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", expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) "Allele2")) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, header = TRUE, as.is = TRUE) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) "VAR", "PVAL")]) obj2.outfile.txt2.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, quietly = TRUE)) { 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) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj1.outfile.gds.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) ncores = 2) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj2.outfile.txt2.select.1)) unlink(obj1.outfile.gds.noselect.1.tmp) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.gds.select.1 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, obj1.outfile.txt2.select.2)) 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$PVAL)) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) } unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.txt.select.1 <- tempfile() obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.outfile.txt2.select.3)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.3)) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, quietly = TRUE)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.txt.select.1.tmp <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")33: unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile()eval(code, test_env) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 34: "Allele2"))eval(code, test_env) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1,
header = TRUE, as.is = TRUE)35: expect_equal(obj1.txt.select.1, obj1.txt1.select.1)withCallingHandlers({ obj1.outfile.txt2.select.1 <- tempfile() eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, if (snapshot_skipped) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", skip("On CRAN") "Allele2")) } obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.txt.select.1, obj1.txt2.select.1) skip_empty() } unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, } obj1.outfile.txt2.select.1))}, snapshot_on_cran = function(cnd) { skip_on_cran() snapshot_skipped <<- TRUE obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, invokeRestart("muffle_cran_snapshot") id = "id", family = binomial(link = "logit"), method = "REML", }, skip = handle_skip, warning = handle_warning, message = handle_message, method.optim = "AI") error = handle_error, interrupt = handle_interrupt) select <- match(1:400, unique(obj2$id_include))
select[is.na(select)] <- 036: obj2.outfile.bed.noselect.1 <- tempfile()doTryCatch(return(expr), name, parentenv, handler) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1)
obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, 37: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.outfile.bed.select.1 <- tempfile()
glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)38: obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, tryCatchList(expr, classes, parentenv, handlers) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1)
obj2.outfile.bgen.noselect.1 <- tempfile()39: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, tryCatch(withCallingHandlers({ outfile = obj2.outfile.bgen.noselect.1) eval(code, test_env) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { obj2.outfile.bgen.select.1 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj2.outfile.bgen.select.1) else if (!new_expectations && skip_on_empty) { obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", if (on_cran()) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, skip(paste0("{", e$package, "} is not installed.")) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", } "VAR", "PVAL")])}, snapshot_on_cran = function(cnd) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") quietly = TRUE)) {}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, 40: header = TRUE, as.is = TRUE)doWithOneRestart(return(expr), restart) obj2.outfile.gds.select.1 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1)41: withOneRestart(expr, restarts[[1L]]) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.noselect.1, obj2.gds.select.1)42: expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL))withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, new_expectations <- the$test_expectations > starting_expectations 0.996996766))) if (snapshot_skipped) { } skip("On CRAN") obj2.outfile.txt.select.1 <- tempfile() } else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_empty() select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) } obj2.outfile.txt1.select.1 <- tempfile()}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, invokeRestart("muffle_cran_snapshot") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2")) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, }) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt.select.1, obj2.txt1.select.1)43: obj2.outfile.txt2.select.1 <- tempfile()test_code(code = exprs, env = env, reporter = get_reporter() %||% glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, StopReporter$new()) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 44: "Allele2"))source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, error_call = error_call) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno))45: pheno <- pheno[idx, ]FUN(X[[i]], ...) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 46: method.optim = "AI")lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, select <- match(1:400, unique(obj1$id_include)) error_call = error_call) select[is.na(select)] <- 0
obj1.outfile.bed.noselect.2 <- tempfile()47: glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2)48: obj1.outfile.bed.select.2 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2,
header = TRUE, as.is = TRUE)49: expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
50: outfile = obj1.outfile.bgen.noselect.2)tryCatch(code, testthat_abort_reporter = function(cnd) { obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, cat(conditionMessage(cnd), "\n") header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) NULL obj1.outfile.bgen.select.2 <- tempfile()}) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2)
obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, 51: header = TRUE, as.is = TRUE)with_reporter(reporters$multi, lapply(test_paths, test_one_file, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {52: obj1.outfile.gds.noselect.2 <- tempfile()test_files_serial(test_dir = test_dir, test_package = test_package, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, header = TRUE, as.is = TRUE) error_call = error_call) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2)
obj1.outfile.gds.select.2 <- tempfile()53: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2)test_files(test_dir = path, test_paths = test_paths, test_package = package, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, reporter = reporter, load_helpers = load_helpers, env = env, header = TRUE, as.is = TRUE) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, expect_equal(obj1.gds.select.1, obj1.gds.select.2) load_package = load_package, parallel = parallel, shuffle = shuffle) }
obj1.outfile.txt.select.2 <- tempfile()54: glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, test_dir("testthat", package = package, reporter = reporter, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, ..., load_package = "installed") select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))55: obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, test_check("GMMAT") header = TRUE, as.is = TRUE)
expect_equal(obj1.txt.select.1, obj1.txt.select.2)An irrecoverable exception occurred. R is aborting now ...
obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) } obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) idx <- sample(nrow(kins)) kins <- kins[idx, idx] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) obj1.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) } obj1.outfile.txt.select.3 <- tempfile() 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", "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.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", quietly = TRUE)) { obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.3) } obj2.outfile.txt.select.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.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", "Allele2")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) 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) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2)) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, 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)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.3)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
33: eval(code, test_env)
34: eval(code, test_env)
35: 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)
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64