Last updated on 2026-11-14 19:51:05 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.58 | 240.25 | 283.83 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.92 | 215.31 | 254.23 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 158.84 | 188.84 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 72.00 | 391.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 53.75 | 246.68 | 300.43 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 68.00 | 314.00 | 382.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 18.00 | 76.00 | 94.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 391.00 | 477.00 | 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,
Traceback:
header = TRUE, as.is = TRUE) 1: obj2.outfile.bed.select.1 <- tempfile()eval(c.expr, envir = args, enclos = envir) 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) 2: expect_equal(obj2.bed.noselect.1, obj2.bed.select.1)eval(c.expr, envir = args, enclos = envir) obj2.outfile.bgen.noselect.1 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 3: outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 4: select = select, outfile = obj2.outfile.bgen.select.1)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1,
header = TRUE, as.is = TRUE) 5: expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1",
"A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, 6: c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
"VAR", "PVAL")]) 7: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) {FUN(X[[i]], ...) 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, 8: header = TRUE, as.is = TRUE)lapply(X = S, FUN = FUN, ...) obj2.outfile.gds.select.1 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) 9: obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, doTryCatch(return(expr), name, parentenv, handler)
header = TRUE, as.is = TRUE)10: expect_equal(obj2.gds.noselect.1, obj2.gds.select.1)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL))
expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 11: 0.996996766)))tryCatchList(expr, classes, parentenv, handlers) }
obj2.outfile.txt.select.1 <- tempfile()12: glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, tryCatch(expr, error = function(e) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, call <- conditionCall(e) if (!is.null(call)) { select = select, infile.header.print = c("SNP", "Allele1", if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) "Allele2")) dcall <- deparse(call, nlines = 1L) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, prefix <- paste("Error in", dcall, ": ") header = TRUE, as.is = TRUE) LONG <- 75L expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, if (is.na(w)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], select = select, infile.header.print = c("SNP", "Allele1", type = "b") "Allele2")) if (w > LONG) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) prefix <- paste0(prefix, "\n ") expect_equal(obj2.txt.select.1, obj2.txt1.select.1) } obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, else prefix <- "Error : " infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, msg <- paste0(prefix, conditionMessage(e), "\n") select = select, infile.header.print = c("SNP", "Allele1", .Internal(seterrmessage(msg[1L])) "Allele2")) if (!silent && isTRUE(getOption("show.error.messages"))) { obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, cat(msg, file = outFile) header = TRUE, as.is = TRUE) .Internal(printDeferredWarnings()) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) } idx <- sample(nrow(pheno)) invisible(structure(msg, class = "try-error", condition = e)) pheno <- pheno[idx, ]}) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 13: method.optim = "AI")try(lapply(X = S, FUN = FUN, ...), silent = TRUE) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2)14: obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2)
obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2)15: FUN(X[[i]], ...)
obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, 16: header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2)lapply(seq_len(cores), inner.do) obj1.outfile.bgen.noselect.2 <- tempfile()
17: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2)mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, mc.silent = silent, mc.cores = cores) header = TRUE, as.is = TRUE)
expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2)18: e$fun(obj, substitute(ex), parent.frame(), e$data) obj1.outfile.bgen.select.2 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 19: select = select, outfile = obj1.outfile.bgen.select.2)foreach(i = 1:ncores) %dopar% { obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, if (!is.null(obj$P)) { header = TRUE, as.is = TRUE) if (bgenInfo$LayoutFlag == 2) { expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { infile, paste0(outfile, "_tmp.", i), center2, obj1.outfile.gds.noselect.2 <- tempfile() MAF.range[1], MAF.range[2], miss.cutoff, miss.method, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, header = TRUE, as.is = TRUE) 1) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) } obj1.outfile.gds.select.2 <- tempfile() else { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], } threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, 1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) else { obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, if (bgenInfo$LayoutFlag == 2) { header = TRUE, as.is = TRUE) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.outfile.txt1.select.2 <- tempfile() "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, bgenInfo$N, bgenInfo$CompressionFlag, 1) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj$Sigma_iX, obj$cov, infile, paste0(outfile, header = TRUE, as.is = TRUE) "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj1.outfile.txt2.select.2 <- tempfile() bgenInfo$N, bgenInfo$CompressionFlag, 1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))
20: obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2)
obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, 21: id = "id", family = binomial(link = "logit"), method = "REML", eval(code, test_env) method.optim = "AI")
select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 022: eval(code, test_env) 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, 23: header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() eval(code, test_env) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { skip("On CRAN") expect_equal(obj2.bed.select.1, obj2.bed.select.2) } obj2.outfile.bgen.noselect.2 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, skip_empty() } outfile = obj2.outfile.bgen.noselect.2)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, if (on_cran()) { header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) skip(paste0("{", e$package, "} is not installed.")) 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, }, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj2.bgen.select.1, obj2.bgen.select.2)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
24: quietly = TRUE)) {doTryCatch(return(expr), name, parentenv, handler)
obj2.outfile.gds.noselect.2 <- tempfile()25: glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, tryCatchOne(expr, names, parentenv, handlers[[1L]]) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2)
obj2.outfile.gds.select.2 <- tempfile()26: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2)tryCatchList(expr, classes, parentenv, handlers) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2,
header = TRUE, as.is = TRUE)27: expect_equal(obj2.gds.select.1, obj2.gds.select.2)tryCatch(withCallingHandlers({ } eval(code, test_env) obj2.outfile.txt.select.2 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, if (snapshot_skipped) { skip("On CRAN") } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj2.txt.select.1, obj2.txt.select.2) if (on_cran()) { obj2.outfile.txt1.select.2 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) 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", }, snapshot_on_cran = function(cnd) { "Allele2")) snapshot_skipped <<- TRUE obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.txt2.select.2 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 28: select = select, infile.header.print = c("SNP", "Allele1", doWithOneRestart(return(expr), restart) "Allele2"))
obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, 29: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)
idx <- sample(nrow(kins))30: kins <- kins[idx, idx]withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, new_expectations <- the$test_expectations > starting_expectations id = "id", family = binomial(link = "logit"), method = "REML", if (snapshot_skipped) { method.optim = "AI") skip("On CRAN") } select <- match(1:400, unique(obj1$id_include)) else if (!new_expectations && skip_on_empty) { select[is.na(select)] <- 0 skip_empty() obj1.outfile.bed.noselect.3 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) } obj1.outfile.bed.select.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) snapshot_skipped <<- TRUE obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.bed.select.1, obj1.bed.select.3) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.outfile.bgen.noselect.3 <- tempfile() end_test = function() { 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, 31: header = TRUE, as.is = TRUE)test_code(code, parent.frame()) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)
obj1.outfile.bgen.select.3 <- tempfile()32: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, test_that("cross-sectional id le 400 binomial", { select = select, outfile = obj1.outfile.bgen.select.3) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") quietly = TRUE)) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") header = TRUE, as.is = TRUE) data(example) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) suppressWarnings(RNGversion("3.5.0")) obj1.outfile.gds.select.3 <- tempfile() set.seed(123) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, pheno$id <- 1:500 header = TRUE, as.is = TRUE) pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA expect_equal(obj1.gds.select.1, obj1.gds.select.3) pheno$sex[sample(1:500, 20)] <- NA } pheno <- pheno[sample(1:500, 450), ] obj1.outfile.txt.select.3 <- tempfile() pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.bed.noselect.1 <- tempfile() obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, "Allele2")) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, unlink(obj1.outfile.bed.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.1 <- tempfile() expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.outfile.txt2.select.3 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj1.outfile.bgen.noselect.1) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, id = "id", family = binomial(link = "logit"), method = "REML", outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) method.optim = "AI") obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj2.outfile.bed.noselect.3 <- tempfile() unlink(obj1.outfile.bgen.noselect.1.tmp) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.outfile.bgen.select.1 <- tempfile() obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj2.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(obj2.bed.select.1, obj2.bed.select.3) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bgen.noselect.3 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", outfile = obj2.outfile.bgen.noselect.3) quietly = TRUE)) { obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj2.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.1.tmp <- tempfile() select = select, outfile = obj2.outfile.bgen.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, ncores = 2) header = TRUE, as.is = TRUE) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) quietly = TRUE)) { unlink(obj1.outfile.gds.noselect.1.tmp) obj2.outfile.gds.noselect.3 <- tempfile() obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, header = TRUE, as.is = TRUE) 0.986534857))) expect_equal(obj2.gds.select.1, obj2.gds.select.3) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } } obj2.outfile.txt.select.3 <- tempfile() obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj2.outfile.txt1.select.3 <- tempfile() obj1.outfile.txt.select.1.tmp <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, 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, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, unlink(obj1.outfile.txt.select.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, "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, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) "Allele2")) obj1.outfile.txt2.select.1 <- tempfile() obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, 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(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.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, obj2.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt2.select.2)) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj1.outfile.txt2.select.1)) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, skip_on_cran() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.txt2.select.2)) id = "id", family = binomial(link = "logit"), method = "REML", unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, select[is.na(select)] <- 0 obj1.outfile.txt2.select.3)) obj2.outfile.bed.noselect.1 <- tempfile() unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3)) obj2.outfile.bed.select.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) quietly = TRUE)) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))}) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE)
obj2.outfile.bgen.select.1 <- tempfile()33: eval(code, test_env) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
select = select, outfile = obj2.outfile.bgen.select.1)34: obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)35: expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", withCallingHandlers({ "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, eval(code, test_env) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", new_expectations <- the$test_expectations > starting_expectations "VAR", "PVAL")]) if (snapshot_skipped) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", skip("On CRAN") quietly = TRUE)) { } obj2.outfile.gds.noselect.1 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) skip_empty() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.gds.select.1 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) skip(paste0("{", e$package, "} is not installed.")) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) snapshot_skipped <<- TRUE expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) invokeRestart("muffle_cran_snapshot") expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, }, skip = handle_skip, warning = handle_warning, message = handle_message, 0.996996766))) error = handle_error, interrupt = handle_interrupt) }
obj2.outfile.txt.select.1 <- tempfile()36: glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, doTryCatch(return(expr), name, parentenv, handler) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 37: "Allele2"))tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1,
header = TRUE, as.is = TRUE)38: expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL)tryCatchList(expr, classes, parentenv, handlers) obj2.outfile.txt1.select.1 <- tempfile()
glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, 39: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, tryCatch(withCallingHandlers({ select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2")) new_expectations <- the$test_expectations > starting_expectations obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj2.txt.select.1, obj2.txt1.select.1) } obj2.outfile.txt2.select.1 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, skip_empty() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, expectation = handle_expectation, packageNotFoundError = function(e) { "Allele2")) if (on_cran()) { obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt2.select.1)}, snapshot_on_cran = function(cnd) { idx <- sample(nrow(pheno)) snapshot_skipped <<- TRUE pheno <- pheno[idx, ] invokeRestart("muffle_cran_snapshot") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, }, skip = handle_skip, warning = handle_warning, message = handle_message, id = "id", family = binomial(link = "logit"), method = "REML", error = handle_error, interrupt = handle_interrupt), error = handle_fatal) method.optim = "AI") select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 040: obj1.outfile.bed.noselect.2 <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2,
header = TRUE, as.is = TRUE)41: expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2)withOneRestart(expr, restarts[[1L]]) obj1.outfile.bed.select.2 <- tempfile()
glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2)42: obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE)withRestarts(tryCatch(withCallingHandlers({ expect_equal(obj1.bed.select.1, obj1.bed.select.2) eval(code, test_env) obj1.outfile.bgen.noselect.2 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (snapshot_skipped) { outfile = obj1.outfile.bgen.noselect.2) skip("On CRAN") obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) skip_empty() obj1.outfile.bgen.select.2 <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, outfile = obj1.outfile.bgen.select.2) if (on_cran()) { obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.select.1, obj1.bgen.select.2)}, snapshot_on_cran = function(cnd) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", snapshot_skipped <<- TRUE quietly = TRUE)) { invokeRestart("muffle_cran_snapshot") obj1.outfile.gds.noselect.2 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, end_test = function() { header = TRUE, as.is = TRUE) }) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2)
obj1.outfile.gds.select.2 <- tempfile()43: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2)test_code(code = exprs, env = env, reporter = get_reporter() %||% obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, StopReporter$new()) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.select.1, obj1.gds.select.2)44: }source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj1.outfile.txt.select.2 <- tempfile() error_call = error_call) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 45: select = select, infile.header.print = c("SNP", "Allele1", FUN(X[[i]], ...) "Allele2"))
obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, 46: header = TRUE, as.is = TRUE)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, expect_equal(obj1.txt.select.1, obj1.txt.select.2) error_call = error_call) obj1.outfile.txt1.select.2 <- tempfile()
glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, 47: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", doTryCatch(return(expr), name, parentenv, handler) "Allele2"))
obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, 48: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2)
obj1.outfile.txt2.select.2 <- tempfile()49: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, tryCatchList(expr, classes, parentenv, handlers) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 50: "Allele2"))tryCatch(code, testthat_abort_reporter = function(cnd) { obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, cat(conditionMessage(cnd), "\n") header = TRUE, as.is = TRUE) NULL 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", 51: method.optim = "AI")with_reporter(reporters$multi, lapply(test_paths, test_one_file, select <- match(1:400, unique(obj2$id_include)) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) select[is.na(select)] <- 0
obj2.outfile.bed.noselect.2 <- tempfile()52: glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2)test_files_serial(test_dir = test_dir, test_package = test_package, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, header = TRUE, as.is = TRUE) env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) error_call = error_call) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle) header = TRUE, as.is = TRUE)
54: expect_equal(obj2.bed.select.1, obj2.bed.select.2)test_dir("testthat", package = package, reporter = reporter, obj2.outfile.bgen.noselect.2 <- tempfile() ..., load_package = "installed") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj2.outfile.bgen.noselect.2)55: obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, test_check("GMMAT") header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2)An irrecoverable exception occurred. R is aborting now ...
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