Last updated on 2026-11-06 06:01:02 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.25 | 242.88 | 286.13 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 39.06 | 204.12 | 243.18 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 26.00 | 141.72 | 167.72 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 166.67 | 208.67 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | OK | ||||
| r-release-linux-x86_64 | 1.5.0 | 54.79 | 229.43 | 284.22 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 77.00 | 94.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 87.00 | 387.00 | 474.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
Traceback:
1: 28: eval(c.expr, envir = args, enclos = envir)doWithOneRestart(return(expr), restart)
29: 2: withOneRestart(expr, restarts[[1L]])eval(c.expr, envir = args, enclos = envir)
30: 3: withRestarts(tryCatch(withCallingHandlers({doTryCatch(return(expr), name, parentenv, handler) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations 4: if (snapshot_skipped) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) skip("On CRAN")
5: }tryCatchList(expr, classes, parentenv, handlers) else if (!new_expectations && skip_on_empty) {
skip_empty() 6: }tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)}, expectation = handle_expectation, packageNotFoundError = function(e) {
if (on_cran()) { 7: skip(paste0("{", e$package, "} is not installed."))FUN(X[[i]], ...) }
}, snapshot_on_cran = function(cnd) { 8: snapshot_skipped <<- TRUElapply(X = S, FUN = FUN, ...) invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 9: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })doTryCatch(return(expr), name, parentenv, handler)
31: 10: test_code(code, parent.frame())tryCatchOne(expr, names, parentenv, handlers[[1L]])
32: 11: test_that("cross-sectional id le 400 binomial", {tryCatchList(expr, classes, parentenv, handlers) plinkfiles <- strsplit(system.file("extdata", "geno.bed",
package = "GMMAT"), ".bed", fixed = TRUE)[[1]]12: bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT")tryCatch(expr, error = function(e) { samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") call <- conditionCall(e) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") if (!is.null(call)) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") if (identical(call[[1L]], quote(doTryCatch))) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") call <- sys.call(-4L) data(example) suppressWarnings(RNGversion("3.5.0")) dcall <- deparse(call, nlines = 1L) set.seed(123) prefix <- paste("Error in", dcall, ": ") pheno <- rbind(example$pheno, example$pheno[1:100, ]) LONG <- 75L pheno$id <- 1:500 sm <- strsplit(conditionMessage(e), "\n")[[1L]] pheno$disease[sample(1:500, 20)] <- NA w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") pheno$age[sample(1:500, 20)] <- NA if (is.na(w)) pheno$sex[sample(1:500, 20)] <- NA w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], pheno <- pheno[sample(1:500, 450), ] type = "b") pheno <- pheno[pheno$id <= 400, ] if (w > LONG) kins <- example$GRM prefix <- paste0(prefix, "\n ") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, } id = "id", family = binomial(link = "logit"), method = "REML", else prefix <- "Error : " method.optim = "AI") msg <- paste0(prefix, conditionMessage(e), "\n") select <- match(1:400, unique(obj1$id_include)) .Internal(seterrmessage(msg[1L])) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() if (!silent && isTRUE(getOption("show.error.messages"))) { glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) cat(msg, file = outFile) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, .Internal(printDeferredWarnings()) header = TRUE, as.is = TRUE) } obj1.outfile.bed.noselect.1.tmp <- tempfile() invisible(structure(msg, class = "try-error", condition = e)) 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)13: obj1.outfile.bed.select.1 <- tempfile()try(lapply(X = S, FUN = FUN, ...), silent = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)
obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, 14: header = TRUE, as.is = TRUE)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)
obj1.outfile.bgen.noselect.1 <- tempfile()15: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, FUN(X[[i]], ...) outfile = obj1.outfile.bgen.noselect.1)
obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, 16: header = TRUE, as.is = TRUE)lapply(seq_len(cores), inner.do) obj1.outfile.bgen.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 17: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, mc.silent = silent, mc.cores = cores)
18: header = TRUE, as.is = TRUE)e$fun(obj, substitute(ex), parent.frame(), e$data)
expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)19: unlink(obj1.outfile.bgen.noselect.1.tmp)foreach(i = 1:ncores) %dopar% { obj1.outfile.bgen.select.1 <- tempfile() if (!is.null(obj$P)) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (bgenInfo$LayoutFlag == 2) { select = select, outfile = obj1.outfile.bgen.select.1) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 1) "VAR", "PVAL")]) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) infile, paste0(outfile, "_tmp.", i), center2, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, header = TRUE, as.is = TRUE) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj1.outfile.gds.noselect.1.tmp <- tempfile() threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, ncores = 2) 1) 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) else { unlink(obj1.outfile.gds.noselect.1.tmp) if (bgenInfo$LayoutFlag == 2) { obj1.outfile.gds.select.1 <- tempfile() .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, "_tmp.", i), center2, MAF.range[1], MAF.range[2], header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, bgenInfo$N, bgenInfo$CompressionFlag, 1) 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, else { select = select, infile.header.print = c("SNP", "Allele1", .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, "Allele2")) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, "_tmp.", i), center2, MAF.range[1], MAF.range[2], header = TRUE, as.is = TRUE) miss.cutoff, miss.method, nperbatch, select, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj1.outfile.txt.select.1.tmp <- tempfile() bgenInfo$N, bgenInfo$CompressionFlag, 1) 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, 20: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) 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, 21: eval(code, test_env) 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, 22: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj1.txt.select.1, obj1.txt2.select.1)
unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, 23: obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, withCallingHandlers({ obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations skip_on_cran() if (snapshot_skipped) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip("On CRAN") id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI") else if (!new_expectations && skip_on_empty) { skip_empty() select <- match(1:400, unique(obj2$id_include)) } select[is.na(select)] <- 0}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.bed.noselect.1 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) skip(paste0("{", e$package, "} is not installed.")) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) } obj2.outfile.bed.select.1 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.bgen.noselect.1 <- tempfile()
24: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1)doTryCatch(return(expr), name, parentenv, handler) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE)
obj2.outfile.bgen.select.1 <- tempfile()25: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, tryCatchOne(expr, names, parentenv, handlers[[1L]]) select = select, outfile = obj2.outfile.bgen.select.1)
obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, 26: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)
expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", 27: "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, tryCatch(withCallingHandlers({ c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", eval(code, test_env) "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() skip("On CRAN") } glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) else if (!new_expectations && skip_on_empty) { 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() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) if (on_cran()) { 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, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) 0.996996766))) }
28: obj2.outfile.txt.select.1 <- tempfile()doWithOneRestart(return(expr), restart)
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 29: withOneRestart(expr, restarts[[1L]]) select = select, infile.header.print = c("SNP", "Allele1",
30: "Allele2"))withRestarts(tryCatch(withCallingHandlers({ obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, eval(code, test_env) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) new_expectations <- the$test_expectations > starting_expectations obj2.outfile.txt1.select.1 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) else if (!new_expectations && skip_on_empty) { obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt1.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.txt2.select.1 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip(paste0("{", e$package, "} is not installed.")) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) } obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") idx <- sample(nrow(pheno)) pheno <- pheno[idx, ]}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { id = "id", family = binomial(link = "logit"), method = "REML", }) method.optim = "AI")
select <- match(1:400, unique(obj1$id_include))31: select[is.na(select)] <- 0test_code(code, parent.frame())
obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2)32: obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, test_that("cross-sectional id le 400 binomial", { header = TRUE, as.is = TRUE) plinkfiles <- strsplit(system.file("extdata", "geno.bed", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] obj1.outfile.bed.select.2 <- tempfile() bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") outfile = obj1.outfile.bgen.noselect.2) data(example) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, suppressWarnings(RNGversion("3.5.0")) header = TRUE, as.is = TRUE) set.seed(123) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj1.outfile.bgen.select.2 <- tempfile() pheno$id <- 1:500 glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, pheno$disease[sample(1:500, 20)] <- NA select = select, outfile = obj1.outfile.bgen.select.2) pheno$age[sample(1:500, 20)] <- NA obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, pheno$sex[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno <- pheno[sample(1:500, 450), ] expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) pheno <- pheno[pheno$id <= 400, ] if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", kins <- example$GRM quietly = TRUE)) { obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.gds.noselect.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) method.optim = "AI") obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.bed.noselect.1 <- tempfile() obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) 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.") obj1.outfile.txt.select.2 <- tempfile() unlink(obj1.outfile.bed.noselect.1.tmp) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.bed.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, outfile = obj1.outfile.bgen.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.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.txt1.select.1, obj1.txt1.select.2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(obj1.outfile.bgen.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.select.1 <- tempfile() "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) method.optim = "AI") expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", select <- match(1:400, unique(obj2$id_include)) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, select[is.na(select)] <- 0 c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bed.noselect.2 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj2.outfile.bed.select.2 <- tempfile() obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, 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.bed.select.1, obj2.bed.select.2) ncores = 2) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, unlink(obj1.outfile.gds.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.1 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj2.outfile.bgen.select.2 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 0.986534857))) quietly = TRUE)) { unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj2.outfile.gds.noselect.2 <- tempfile() } glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj1.outfile.txt.select.1 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj1.outfile.txt.select.1.tmp <- tempfile() } expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj2.outfile.txt.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.txt.select.1.tmp) "Allele2")) obj1.outfile.txt1.select.1 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.outfile.txt2.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, idx <- sample(nrow(kins)) obj1.outfile.txt2.select.1)) kins <- kins[idx, idx] skip_on_cran() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj2.outfile.bed.select.1 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.bgen.select.3 <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "VAR", "PVAL")]) quietly = TRUE)) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.3 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.gds.noselect.1 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) } expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj1.outfile.txt.select.3 <- tempfile() expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, 0.996996766))) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt.select.1, obj1.txt.select.3) "Allele2")) obj1.outfile.txt1.select.3 <- tempfile() obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt1.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) "Allele2")) obj1.outfile.txt2.select.3 <- tempfile() obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.txt.select.1, obj2.txt2.select.1) select <- match(1:400, unique(obj2$id_include)) idx <- sample(nrow(pheno)) select[is.na(select)] <- 0 pheno <- pheno[idx, ] obj2.outfile.bed.noselect.3 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) select[is.na(select)] <- 0 obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj2.outfile.bgen.noselect.3 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) outfile = obj2.outfile.bgen.noselect.3) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj1.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) select = select, outfile = obj2.outfile.bgen.select.3) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj1.outfile.bgen.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { select = select, outfile = obj1.outfile.bgen.select.2) obj2.outfile.gds.noselect.3 <- tempfile() obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) quietly = TRUE)) { obj2.outfile.gds.select.3 <- tempfile() obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.3) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) } obj1.outfile.gds.select.2 <- tempfile() obj2.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.select.1, obj1.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj1.outfile.txt.select.2 <- tempfile() obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt.select.1, obj2.txt.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt1.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, 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.2 <- read.table(obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, "Allele2")) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, 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.txt2.select.1, obj1.txt2.select.2) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt2.select.2)) method.optim = "AI") unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, select <- match(1:400, unique(obj2$id_include)) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, select[is.na(select)] <- 0 obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.bed.noselect.2 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.outfile.txt2.select.3)) obj2.outfile.bed.select.2 <- tempfile() unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.3)) expect_equal(obj2.bed.select.1, obj2.bed.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bgen.noselect.2 <- tempfile() quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2)}) obj2.outfile.bgen.select.2 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 33: select = select, outfile = obj2.outfile.bgen.select.2)eval(code, test_env) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2,
header = TRUE, as.is = TRUE)34: expect_equal(obj2.bgen.select.1, obj2.bgen.select.2)eval(code, test_env) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {35: obj2.outfile.gds.noselect.2 <- tempfile()withCallingHandlers({ glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) eval(code, test_env) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) skip("On CRAN") obj2.outfile.gds.select.2 <- tempfile() } glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) else if (!new_expectations && skip_on_empty) { obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.select.1, obj2.gds.select.2)}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { obj2.outfile.txt.select.2 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.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.txt.select.2 <- read.table(obj2.outfile.txt.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.txt.select.1, obj2.txt.select.2)
obj2.outfile.txt1.select.2 <- tempfile()36: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, 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.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2,
header = TRUE, as.is = TRUE)38: expect_equal(obj2.txt1.select.1, obj2.txt1.select.2)tryCatchList(expr, classes, parentenv, handlers)
obj2.outfile.txt2.select.2 <- tempfile()39: glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, tryCatch(withCallingHandlers({ infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1", new_expectations <- the$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) else if (!new_expectations && skip_on_empty) { idx <- sample(nrow(kins)) skip_empty() kins <- kins[idx, idx] } obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, }, expectation = handle_expectation, packageNotFoundError = function(e) { id = "id", family = binomial(link = "logit"), method = "REML", if (on_cran()) { method.optim = "AI") skip(paste0("{", e$package, "} is not installed.")) select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0}, snapshot_on_cran = function(cnd) { obj1.outfile.bed.noselect.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) invokeRestart("muffle_cran_snapshot") obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3)
obj1.outfile.bed.select.3 <- tempfile()40: glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3)doWithOneRestart(return(expr), restart) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3,
header = TRUE, as.is = TRUE)41: expect_equal(obj1.bed.select.1, obj1.bed.select.3)withOneRestart(expr, restarts[[1L]]) obj1.outfile.bgen.noselect.3 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 42: outfile = obj1.outfile.bgen.noselect.3)withRestarts(tryCatch(withCallingHandlers({ obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) if (snapshot_skipped) { obj1.outfile.bgen.select.3 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj1.outfile.bgen.select.3) else if (!new_expectations && skip_on_empty) { obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, expectation = handle_expectation, packageNotFoundError = function(e) { quietly = TRUE)) { if (on_cran()) { obj1.outfile.gds.noselect.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) } obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, }, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.gds.select.3 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) end_test = function() { obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, }) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3)
}43: obj1.outfile.txt.select.3 <- tempfile()test_code(code = exprs, env = env, reporter = get_reporter() %||% glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, StopReporter$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, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, error_call = error_call) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt.select.1, obj1.txt.select.3)45: obj1.outfile.txt1.select.3 <- tempfile()FUN(X[[i]], ...) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 46: select = select, infile.header.print = c("SNP", "Allele1", lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call) "Allele2"))
47: obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)48: tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.outfile.txt2.select.3 <- tempfile()
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, 49: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, tryCatchList(expr, classes, parentenv, handlers) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))50: obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, tryCatch(code, testthat_abort_reporter = function(cnd) { header = TRUE, as.is = TRUE) cat(conditionMessage(cnd), "\n") expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) NULL obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, }) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI")51: select <- match(1:400, unique(obj2$id_include))with_reporter(reporters$multi, lapply(test_paths, test_one_file, select[is.na(select)] <- 0 env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj2.outfile.bed.noselect.3 <- tempfile()
glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)52: obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, test_files_serial(test_dir = test_dir, test_package = test_package, header = TRUE, as.is = TRUE) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj2.outfile.bed.select.3 <- tempfile() desc = desc, load_package = load_package, shuffle = shuffle, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) error_call = error_call) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3,
53: header = TRUE, as.is = TRUE)test_files(test_dir = path, test_paths = test_paths, test_package = package, expect_equal(obj2.bed.select.1, obj2.bed.select.3) reporter = reporter, load_helpers = load_helpers, env = env, obj2.outfile.bgen.noselect.3 <- tempfile() stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, load_package = load_package, parallel = parallel, shuffle = shuffle) outfile = obj2.outfile.bgen.noselect.3)
obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 54: header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)test_dir("testthat", package = package, reporter = reporter, obj2.outfile.bgen.select.3 <- tempfile() ..., load_package = "installed") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
select = select, outfile = obj2.outfile.bgen.select.3)55: obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, test_check("GMMAT") header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)An irrecoverable exception occurred. R is aborting now ...
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