Last updated on 2026-10-16 10:54:16 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.06 | 240.59 | 283.65 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.50 | 208.87 | 247.37 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 158.92 | 187.92 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 168.79 | 210.79 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 73.00 | 393.00 | 466.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.56 | 239.28 | 290.84 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 50.71 | 234.78 | 285.49 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 70.00 | 314.00 | 384.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 76.00 | 93.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 90.00 | 398.00 | 488.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
22: eval(code, test_env)
23: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
Traceback:
27: 1: tryCatch(withCallingHandlers({eval(c.expr, envir = args, enclos = envir) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations 2: if (snapshot_skipped) {eval(c.expr, envir = args, enclos = envir) skip("On CRAN")
} 3: else if (!new_expectations && skip_on_empty) {doTryCatch(return(expr), name, parentenv, handler) skip_empty()
} 4: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed.")) 5: }tryCatchList(expr, classes, parentenv, handlers)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") 6: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
7: 28: FUN(X[[i]], ...)
doWithOneRestart(return(expr), restart) 8:
lapply(X = S, FUN = FUN, ...)29: withOneRestart(expr, restarts[[1L]])
9: 30: doTryCatch(return(expr), name, parentenv, handler)withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations10: if (snapshot_skipped) { skip("On CRAN")tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
else if (!new_expectations && skip_on_empty) {11: skip_empty()tryCatchList(expr, classes, parentenv, handlers) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {12: if (on_cran()) {tryCatch(expr, error = function(e) { skip(paste0("{", e$package, "} is not installed.")) call <- conditionCall(e) } if (!is.null(call)) {}, snapshot_on_cran = function(cnd) { if (identical(call[[1L]], quote(doTryCatch))) snapshot_skipped <<- TRUE call <- sys.call(-4L) invokeRestart("muffle_cran_snapshot") dcall <- deparse(call, nlines = 1L)}, skip = handle_skip, warning = handle_warning, message = handle_message, prefix <- paste("Error in", dcall, ": ") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), LONG <- 75L end_test = function() { sm <- strsplit(conditionMessage(e), "\n")[[1L]] }) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 31: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], test_code(code, parent.frame()) type = "b")
if (w > LONG) 32: prefix <- paste0(prefix, "\n ")test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", else prefix <- "Error : " package = "GMMAT"), ".bed", fixed = TRUE)[[1]] msg <- paste0(prefix, conditionMessage(e), "\n") bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") .Internal(seterrmessage(msg[1L])) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") if (!silent && isTRUE(getOption("show.error.messages"))) { gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") cat(msg, file = outFile) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") .Internal(printDeferredWarnings()) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") } txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") invisible(structure(msg, class = "try-error", condition = e)) data(example)}) suppressWarnings(RNGversion("3.5.0"))
set.seed(123)13: pheno <- rbind(example$pheno, example$pheno[1:100, ])try(lapply(X = S, FUN = FUN, ...), silent = TRUE) pheno$id <- 1:500
pheno$disease[sample(1:500, 20)] <- NA14: pheno$age[sample(1:500, 20)] <- NAsendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) pheno$sex[sample(1:500, 20)] <- NA
pheno <- pheno[sample(1:500, 450), ]15: pheno <- pheno[pheno$id <= 400, ]FUN(X[[i]], ...) kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 16: method.optim = "AI")lapply(seq_len(cores), inner.do) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 017: obj1.outfile.bed.noselect.1 <- tempfile()mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) mc.silent = silent, mc.cores = cores) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1,
header = TRUE, as.is = TRUE)18: obj1.outfile.bed.noselect.1.tmp <- tempfile()e$fun(obj, substitute(ex), parent.frame(), e$data) 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.")19: unlink(obj1.outfile.bed.noselect.1.tmp)foreach(i = 1:ncores) %dopar% { obj1.outfile.bed.select.1 <- tempfile() if (!is.null(obj$P)) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) if (bgenInfo$LayoutFlag == 2) { obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, header = TRUE, as.is = TRUE) infile, paste0(outfile, "_tmp.", i), center2, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, obj1.outfile.bgen.noselect.1 <- tempfile() nperbatch, select, threadInfo$begin[i], threadInfo$end[i], glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, outfile = obj1.outfile.bgen.noselect.1) 1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, } header = TRUE, as.is = TRUE) else { obj1.outfile.bgen.noselect.1.tmp <- tempfile() .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile, paste0(outfile, "_tmp.", i), center2, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], header = TRUE, as.is = TRUE) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) 1) 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) else { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, if (bgenInfo$LayoutFlag == 2) { header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj$Sigma_iX, obj$cov, infile, paste0(outfile, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, "_tmp.", i), center2, MAF.range[1], MAF.range[2], c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", miss.cutoff, miss.method, nperbatch, select, "VAR", "PVAL")]) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", bgenInfo$N, bgenInfo$CompressionFlag, 1) quietly = TRUE)) { } obj1.outfile.gds.noselect.1 <- tempfile() else { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj$Sigma_iX, obj$cov, infile, paste0(outfile, header = TRUE, as.is = TRUE) "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj1.outfile.gds.noselect.1.tmp <- tempfile() miss.cutoff, miss.method, nperbatch, select, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], ncores = 2) bgenInfo$N, bgenInfo$CompressionFlag, 1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, } header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)} unlink(obj1.outfile.gds.noselect.1.tmp)
obj1.outfile.gds.select.1 <- tempfile()20: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)
expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))21: eval(code, test_env) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942,
0.986534857)))22: eval(code, test_env) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))
}23: obj1.outfile.txt.select.1 <- tempfile()withCallingHandlers({ glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, eval(code, test_env) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, new_expectations <- the$test_expectations > starting_expectations select = select, infile.header.print = c("SNP", "Allele1", if (snapshot_skipped) { "Allele2")) skip("On CRAN") obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) skip_empty() obj1.outfile.txt.select.1.tmp <- tempfile() } expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, }, expectation = handle_expectation, packageNotFoundError = function(e) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (on_cran()) { select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") skip(paste0("{", e$package, "} is not installed.")) unlink(obj1.outfile.txt.select.1.tmp) }}, snapshot_on_cran = function(cnd) { obj1.outfile.txt1.select.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot") select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2")) error = handle_error, interrupt = handle_interrupt) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1,
header = TRUE, as.is = TRUE)24: expect_equal(obj1.txt.select.1, obj1.txt1.select.1)doTryCatch(return(expr), name, parentenv, handler) obj1.outfile.txt2.select.1 <- tempfile()
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 25: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, 26: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj1.txt.select.1, obj1.txt2.select.1)
unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, 27: obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, tryCatch(withCallingHandlers({ obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, eval(code, test_env) obj1.outfile.txt2.select.1)) skip_on_cran() new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip("On CRAN") id = "id", family = binomial(link = "logit"), method = "REML", } else if (!new_expectations && skip_on_empty) { method.optim = "AI") skip_empty() select <- match(1:400, unique(obj2$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$package, "} is not installed.")) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) snapshot_skipped <<- TRUE obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1)
obj2.outfile.bgen.noselect.1 <- tempfile()28: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, doWithOneRestart(return(expr), restart) outfile = obj2.outfile.bgen.noselect.1)
obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, 29: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]])
obj2.outfile.bgen.select.1 <- tempfile()30: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.1)withRestarts(tryCatch(withCallingHandlers({ obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, eval(code, test_env) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", if (snapshot_skipped) { skip("On CRAN") "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, } c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", else if (!new_expectations && skip_on_empty) { "VAR", "PVAL")]) skip_empty() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) {}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.gds.noselect.1 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) skip(paste0("{", e$package, "} is not installed.")) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { obj2.outfile.gds.select.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) invokeRestart("muffle_cran_snapshot") obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) end_test = function() { expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) }) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918,
0.996996766)))31: }test_code(code, parent.frame()) obj2.outfile.txt.select.1 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, 32: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, test_that("cross-sectional id le 400 binomial", { select = select, infile.header.print = c("SNP", "Allele1", plinkfiles <- strsplit(system.file("extdata", "geno.bed", "Allele2")) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj2.outfile.txt1.select.1 <- tempfile() txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, suppressWarnings(RNGversion("3.5.0")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, set.seed(123) select = select, infile.header.print = c("SNP", "Allele1", pheno <- rbind(example$pheno, example$pheno[1:100, ]) "Allele2")) pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, pheno$age[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$sex[sample(1:500, 20)] <- NA expect_equal(obj2.txt.select.1, obj2.txt1.select.1) pheno <- pheno[sample(1:500, 450), ] obj2.outfile.txt2.select.1 <- tempfile() pheno <- pheno[pheno$id <= 400, ] glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, kins <- example$GRM infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", method.optim = "AI") "Allele2")) select <- match(1:400, unique(obj1$id_include)) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) idx <- sample(nrow(pheno)) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, pheno <- pheno[idx, ] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.bed.noselect.1.tmp <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, method.optim = "AI") ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") select <- match(1:400, unique(obj1$id_include)) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.outfile.bed.noselect.2 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) obj1.outfile.bed.select.2 <- tempfile() obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.outfile.bgen.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1, obj1.bed.select.2) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.bgen.noselect.2 <- tempfile() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) outfile = obj1.outfile.bgen.noselect.2) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) select = select, outfile = obj1.outfile.bgen.select.1) obj1.outfile.bgen.select.2 <- tempfile() obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, 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(obj1.bgen.select.1, obj1.bgen.select.2) 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.2 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.gds.noselect.1 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.2 <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, ncores = 2) header = TRUE, as.is = TRUE) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_equal(obj1.gds.select.1, obj1.gds.select.2) header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.outfile.txt.select.2 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.gds.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, "Allele2")) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) header = TRUE, as.is = TRUE) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_equal(obj1.txt.select.1, obj1.txt.select.2) 0.986534857))) obj1.outfile.txt1.select.2 <- tempfile() unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt.select.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, unlink(obj1.outfile.txt.select.1.tmp) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt1.select.1 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, select <- match(1:400, unique(obj2$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj2.outfile.bed.select.2 <- tempfile() obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, outfile = obj2.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj1.outfile.txt2.select.1)) header = TRUE, as.is = TRUE) skip_on_cran() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", id = "id", family = binomial(link = "logit"), method = "REML", quietly = TRUE)) { method.optim = "AI") obj2.outfile.gds.noselect.2 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.bed.select.1 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.bed.select.1 <- read.table(obj2.outfile.bed.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(obj2.bed.noselect.1, obj2.bed.select.1) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.outfile.bgen.noselect.1 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.outfile.bgen.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj2.outfile.bgen.select.1) "Allele2")) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.outfile.txt1.select.2 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", select = select, infile.header.print = c("SNP", "Allele1", "VAR", "PVAL")]) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.txt2.select.2 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) "Allele2")) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) idx <- sample(nrow(kins)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, kins <- kins[idx, idx] 0.996996766))) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") } obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, select <- match(1:400, unique(obj1$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.outfile.bed.noselect.3 <- tempfile() obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.1 <- tempfile() expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.bed.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.3) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) "Allele2")) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, idx <- sample(nrow(pheno)) select = select, outfile = obj1.outfile.bgen.select.3) pheno <- pheno[idx, ] obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) method.optim = "AI") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select <- match(1:400, unique(obj1$id_include)) quietly = TRUE)) { select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.outfile.gds.select.3 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.2) obj1.outfile.txt.select.3 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select = select, outfile = obj1.outfile.bgen.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.txt2.select.3 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.select.2 <- tempfile() "Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, 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.gds.select.1, obj1.gds.select.2) method.optim = "AI") } select <- match(1:400, unique(obj2$id_include)) obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, select[is.na(select)] <- 0 infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bed.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) "Allele2")) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.select.1, obj2.bed.select.3) "Allele2")) obj2.outfile.bgen.noselect.3 <- tempfile() obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.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.bgen.noselect.1, obj2.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.3 <- tempfile() "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) id = "id", family = binomial(link = "logit"), method = "REML", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", method.optim = "AI") quietly = TRUE)) { select <- match(1:400, unique(obj2$id_include)) obj2.outfile.gds.noselect.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.outfile.bed.noselect.2 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) header = TRUE, as.is = TRUE) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.3) expect_equal(obj2.bed.select.1, obj2.bed.select.2) } obj2.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.txt.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, outfile = obj2.outfile.bgen.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt.select.1, obj2.txt.select.3) select = select, outfile = obj2.outfile.bgen.select.2) obj2.outfile.txt1.select.3 <- tempfile() obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.txt2.select.3 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.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) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) expect_equal(obj2.gds.select.1, obj2.gds.select.2) 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.2 <- tempfile() obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj2.outfile.txt2.select.1)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, "Allele2")) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, "Allele2")) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj1.outfile.txt2.select.3)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt2.select.2 <- tempfile() obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj2.outfile.txt2.select.3)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", quietly = TRUE)) "Allele2")) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, idx <- sample(nrow(kins)) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) kins <- kins[idx, idx]})
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 33: id = "id", family = binomial(link = "logit"), method = "REML", eval(code, test_env) method.optim = "AI")
select <- match(1:400, unique(obj1$id_include))34: select[is.na(select)] <- 0eval(code, test_env) obj1.outfile.bed.noselect.3 <- tempfile()
glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, 35: header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) eval(code, test_env) obj1.outfile.bed.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) if (snapshot_skipped) { obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1, obj1.bed.select.3) else if (!new_expectations && skip_on_empty) { obj1.outfile.bgen.noselect.3 <- tempfile() skip_empty() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) } obj1.outfile.bgen.select.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE select = select, outfile = obj1.outfile.bgen.select.3) invokeRestart("muffle_cran_snapshot") obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 36: quietly = TRUE)) {doTryCatch(return(expr), name, parentenv, handler) obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)
obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 37: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)
obj1.outfile.gds.select.3 <- tempfile()38: glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)tryCatchList(expr, classes, parentenv, handlers) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3,
header = TRUE, as.is = TRUE)39: expect_equal(obj1.gds.select.1, obj1.gds.select.3)tryCatch(withCallingHandlers({ } eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.txt.select.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", skip_empty() "Allele2")) } obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.txt.select.1, obj1.txt.select.3) skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt1.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, }, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot") select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2")) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3,
header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)40: obj1.outfile.txt2.select.3 <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 41: select = select, infile.header.print = c("SNP", "Allele1", withOneRestart(expr, restarts[[1L]])
"Allele2"))42: obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, withRestarts(tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) new_expectations <- the$test_expectations > starting_expectations obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, if (snapshot_skipped) { id = "id", family = binomial(link = "logit"), method = "REML", skip("On CRAN") method.optim = "AI") } select <- match(1:400, unique(obj2$id_include)) else if (!new_expectations && skip_on_empty) { select[is.na(select)] <- 0 skip_empty() obj2.outfile.bed.noselect.3 <- tempfile() } glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) } expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)}, snapshot_on_cran = function(cnd) { obj2.outfile.bed.select.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) invokeRestart("muffle_cran_snapshot") obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), expect_equal(obj2.bed.select.1, obj2.bed.select.3) end_test = function() { obj2.outfile.bgen.noselect.3 <- tempfile() }) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj2.outfile.bgen.noselect.3)43: obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, test_code(code = exprs, env = env, reporter = get_reporter() %||% header = TRUE, as.is = TRUE) StopReporter$new()) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 44: select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE)source_file(path, env = env(env), desc = desc, shuffle = shuffle, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) error_call = error_call) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {45: FUN(X[[i]], ...) 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, 46: header = TRUE, as.is = TRUE)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) error_call = error_call) obj2.outfile.gds.select.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)47: obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.select.1, obj2.gds.select.3)48: tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
obj2.outfile.txt.select.3 <- tempfile()49: glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 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) { obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, cat(conditionMessage(cnd), "\n") NULL header = TRUE, as.is = TRUE)}) expect_equal(obj2.txt.select.1, obj2.txt.select.3)
obj2.outfile.txt1.select.3 <- tempfile()51: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, with_reporter(reporters$multi, lapply(test_paths, test_one_file, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))52: test_files_serial(test_dir = test_dir, test_package = test_package, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, 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, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) desc = desc, load_package = load_package, shuffle = shuffle, obj2.outfile.txt2.select.3 <- tempfile() error_call = error_call) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 53: select = select, infile.header.print = c("SNP", "Allele1", test_files(test_dir = path, test_paths = test_paths, test_package = package, "Allele2")) reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) load_package = load_package, parallel = parallel, shuffle = shuffle) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)
unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, 54: test_dir("testthat", package = package, reporter = reporter, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, ..., load_package = "installed") 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, 55: obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, test_check("GMMAT") obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2,
obj1.outfile.txt2.select.2))An irrecoverable exception occurred. R is aborting now ...
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