Last updated on 2026-09-24 14:56:56 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 42.10 | 239.65 | 281.75 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 37.55 | 212.87 | 250.42 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 153.51 | 182.51 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 162.06 | 204.06 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 72.00 | 391.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.93 | 241.79 | 293.72 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 51.95 | 232.79 | 284.74 | 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 | 310.00 | 380.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 16.00 | 75.00 | 91.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 383.00 | 469.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/1s]
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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
27: tryCatch(withCallingHandlers({ eval(code, test_env)
Traceback:
new_expectations <- the$test_expectations > starting_expectations 1: if (snapshot_skipped) {eval(c.expr, envir = args, enclos = envir) skip("On CRAN")
} else if (!new_expectations && skip_on_empty) { 2: eval(c.expr, envir = args, enclos = envir) skip_empty()
} 3: }, expectation = handle_expectation, packageNotFoundError = function(e) {doTryCatch(return(expr), name, parentenv, handler) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed.")) 4: }tryCatchOne(expr, names, parentenv, handlers[[1L]])}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE 5: invokeRestart("muffle_cran_snapshot")tryCatchList(expr, classes, parentenv, handlers)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal) 6:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)28:
doWithOneRestart(return(expr), restart) 7:
FUN(X[[i]], ...)29:
withOneRestart(expr, restarts[[1L]]) 8:
lapply(X = S, FUN = FUN, ...)30:
withRestarts(tryCatch(withCallingHandlers({ 9: eval(code, test_env)doTryCatch(return(expr), name, parentenv, handler) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {10: 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$package, "} is not installed.")) call <- conditionCall(e) } if (!is.null(call)) {}, snapshot_on_cran = function(cnd) { if (identical(call[[1L]], quote(doTryCatch))) snapshot_skipped <<- TRUE call <- sys.call(-4L) invokeRestart("muffle_cran_snapshot") dcall <- deparse(call, nlines = 1L)}, skip = handle_skip, warning = handle_warning, message = handle_message, prefix <- paste("Error in", dcall, ": ") error = handle_error, interrupt = handle_interrupt), error = handle_fatal), LONG <- 75L end_test = function() { sm <- strsplit(conditionMessage(e), "\n")[[1L]] }) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 31: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], test_code(code, parent.frame()) type = "b")
if (w > LONG) 32: prefix <- paste0(prefix, "\n ")test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", else prefix <- "Error : " package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") msg <- paste0(prefix, conditionMessage(e), "\n") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") .Internal(seterrmessage(msg[1L])) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") if (!silent && isTRUE(getOption("show.error.messages"))) { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") cat(msg, file = outFile) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") .Internal(printDeferredWarnings()) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") } data(example) invisible(structure(msg, class = "try-error", condition = e)) suppressWarnings(RNGversion("3.5.0"))}) set.seed(123)
pheno <- rbind(example$pheno, example$pheno[1:100, ])13: pheno$id <- 1:500try(lapply(X = S, FUN = FUN, ...), silent = TRUE) pheno$disease[sample(1:500, 20)] <- NA
14: pheno$age[sample(1:500, 20)] <- NAsendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
pheno$sex[sample(1:500, 20)] <- NA15: pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] kins <- example$GRMFUN(X[[i]], ...) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 16: method.optim = "AI")lapply(seq_len(cores), inner.do) select <- match(1:400, unique(obj1$id_include))
17: select[is.na(select)] <- 0mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, obj1.outfile.bed.noselect.1 <- tempfile() mc.silent = silent, mc.cores = cores) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1)
obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, 18: header = TRUE, as.is = TRUE)e$fun(obj, substitute(ex), parent.frame(), e$data) obj1.outfile.bed.noselect.1.tmp <- tempfile()
expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, 19: ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")foreach(i = 1:ncores) %dopar% { unlink(obj1.outfile.bed.noselect.1.tmp) if (!is.null(obj$P)) { obj1.outfile.bed.select.1 <- tempfile() if (bgenInfo$LayoutFlag == 2) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, infile, paste0(outfile, "_tmp.", i), center2, header = TRUE, as.is = TRUE) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj1.outfile.bgen.noselect.1 <- tempfile() threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 1) outfile = obj1.outfile.bgen.noselect.1) } obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, else { header = TRUE, as.is = TRUE) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, obj1.outfile.bgen.noselect.1.tmp <- tempfile() infile, paste0(outfile, "_tmp.", i), center2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, header = TRUE, as.is = TRUE) 1) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) } } unlink(obj1.outfile.bgen.noselect.1.tmp) else { obj1.outfile.bgen.select.1 <- tempfile() if (bgenInfo$LayoutFlag == 2) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, select = select, outfile = obj1.outfile.bgen.select.1) obj$Sigma_iX, obj$cov, infile, paste0(outfile, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.select.1) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", bgenInfo$N, bgenInfo$CompressionFlag, 1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, } c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", else { "VAR", "PVAL")]) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj$Sigma_iX, obj$cov, infile, paste0(outfile, quietly = TRUE)) { "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj1.outfile.gds.noselect.1 <- tempfile() miss.cutoff, miss.method, nperbatch, select, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, bgenInfo$N, bgenInfo$CompressionFlag, 1) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() } glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, } ncores = 2)} obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp,
header = TRUE, as.is = TRUE)20: expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(obj1.outfile.gds.noselect.1.tmp) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.gds.select.1 <- tempfile()
glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)21: obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)22: expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))eval(code, test_env) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942,
0.986534857)))23: unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))withCallingHandlers({ } eval(code, test_env) obj1.outfile.txt.select.1 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, if (snapshot_skipped) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip("On CRAN") select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) else if (!new_expectations && skip_on_empty) { obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.txt.select.1.tmp <- tempfile() if (on_cran()) { expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, skip(paste0("{", e$package, "} is not installed.")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", }}, snapshot_on_cran = function(cnd) { "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") snapshot_skipped <<- TRUE unlink(obj1.outfile.txt.select.1.tmp) invokeRestart("muffle_cran_snapshot") obj1.outfile.txt1.select.1 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, error = handle_error, interrupt = handle_interrupt) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 24: "Allele2"))doTryCatch(return(expr), name, parentenv, handler) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1,
header = TRUE, as.is = TRUE)25: expect_equal(obj1.txt.select.1, obj1.txt1.select.1)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.outfile.txt2.select.1 <- tempfile()
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 26: 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"))27: obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, if (snapshot_skipped) { obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, skip("On CRAN") obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, } obj1.outfile.txt2.select.1)) else if (!new_expectations && skip_on_empty) { skip_on_cran() skip_empty() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, } id = "id", family = binomial(link = "logit"), method = "REML", }, expectation = handle_expectation, packageNotFoundError = function(e) { method.optim = "AI") if (on_cran()) { select <- match(1:400, unique(obj2$id_include)) skip(paste0("{", e$package, "} is not installed.")) select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.1 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) snapshot_skipped <<- TRUE obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.bed.select.1 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)
obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, 28: header = TRUE, as.is = TRUE)doWithOneRestart(return(expr), restart) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1)
obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 29: outfile = obj2.outfile.bgen.noselect.1)withOneRestart(expr, restarts[[1L]]) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1,
header = TRUE, as.is = TRUE)30: obj2.outfile.bgen.select.1 <- tempfile()withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, eval(code, test_env) select = select, outfile = obj2.outfile.bgen.select.1) new_expectations <- the$test_expectations > starting_expectations obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) } expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", else if (!new_expectations && skip_on_empty) { "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, skip_empty() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", } "VAR", "PVAL")])}, expectation = handle_expectation, packageNotFoundError = function(e) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (on_cran()) { quietly = TRUE)) { skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.gds.noselect.1 <- tempfile() } glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1)}, snapshot_on_cran = function(cnd) { obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") obj2.outfile.gds.select.1 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, end_test = function() { header = TRUE, as.is = TRUE) }) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1)
expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL))31: expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, test_code(code, parent.frame()) 0.996996766)))
}32: obj2.outfile.txt.select.1 <- tempfile()test_that("cross-sectional id le 400 binomial", { glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, plinkfiles <- strsplit(system.file("extdata", "geno.bed", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] select = select, infile.header.print = c("SNP", "Allele1", bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") "Allele2")) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, 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") glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, data(example) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, suppressWarnings(RNGversion("3.5.0")) select = select, infile.header.print = c("SNP", "Allele1", set.seed(123) "Allele2")) pheno <- rbind(example$pheno, example$pheno[1:100, ]) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, pheno$id <- 1:500 header = TRUE, as.is = TRUE) pheno$disease[sample(1:500, 20)] <- NA expect_equal(obj2.txt.select.1, obj2.txt1.select.1) pheno$age[sample(1:500, 20)] <- NA obj2.outfile.txt2.select.1 <- tempfile() pheno$sex[sample(1:500, 20)] <- NA glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, pheno <- pheno[sample(1:500, 450), ] infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- pheno[pheno$id <= 400, ] select = select, infile.header.print = c("SNP", "Allele1", kins <- example$GRM "Allele2")) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 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(obj1$id_include)) idx <- sample(nrow(pheno)) select[is.na(select)] <- 0 pheno <- pheno[idx, ] obj1.outfile.bed.noselect.1 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) id = "id", family = binomial(link = "logit"), method = "REML", obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) obj1.outfile.bed.noselect.1.tmp <- tempfile() select[is.na(select)] <- 0 expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj1.outfile.bed.noselect.2 <- tempfile() ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, obj1.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj1.outfile.bgen.noselect.1 <- 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) obj1.outfile.bgen.noselect.2 <- tempfile() obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.2) obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) select = select, outfile = obj1.outfile.bgen.select.2) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, outfile = obj1.outfile.bgen.select.1) quietly = TRUE)) { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.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.gds.noselect.1, obj1.gds.noselect.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) } obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj1.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.outfile.gds.noselect.1.tmp <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, select = select, infile.header.print = c("SNP", "Allele1", ncores = 2) "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.outfile.txt1.select.2 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.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) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.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(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.outfile.txt2.select.2 <- tempfile() 0.986534857))) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) select <- match(1:400, unique(obj2$id_include)) obj1.outfile.txt.select.1.tmp <- tempfile() select[is.na(select)] <- 0 expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj2.outfile.bed.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, unlink(obj1.outfile.txt.select.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj2.outfile.bed.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, expect_equal(obj2.bed.select.1, obj2.bed.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt1.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt2.select.1 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) "Allele2")) obj2.outfile.bgen.select.2 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, 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.bgen.select.1, obj2.bgen.select.2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { skip_on_cran() obj2.outfile.gds.noselect.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) select[is.na(select)] <- 0 obj2.outfile.gds.select.2 <- tempfile() obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.outfile.bed.select.1 <- tempfile() } glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.outfile.txt.select.2 <- tempfile() obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.noselect.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, outfile = obj2.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, expect_equal(obj2.txt.select.1, obj2.txt.select.2) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, outfile = obj2.outfile.bgen.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.outfile.txt2.select.2 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, "VAR", "PVAL")]) 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")) obj2.outfile.gds.noselect.1 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, 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(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) obj2.outfile.gds.select.1 <- tempfile() kins <- kins[idx, idx] glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) select[is.na(select)] <- 0 expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, obj1.outfile.bed.noselect.3 <- tempfile() 0.996996766))) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) } obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.txt.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) "Allele2")) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, outfile = obj1.outfile.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.txt2.select.1 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.outfile.gds.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) idx <- sample(nrow(pheno)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) pheno <- pheno[idx, ] obj1.outfile.gds.select.3 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj1$id_include)) expect_equal(obj1.gds.select.1, obj1.gds.select.3) select[is.na(select)] <- 0 } obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.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.bed.noselect.1, obj1.bed.noselect.2) "Allele2")) obj1.outfile.bed.select.2 <- tempfile() obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, 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, expect_equal(obj1.txt.select.1, obj1.txt.select.3) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.3 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj1.outfile.bgen.noselect.2 <- tempfile() 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", outfile = obj1.outfile.bgen.noselect.2) "Allele2")) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, select = select, outfile = obj1.outfile.bgen.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj1.outfile.gds.noselect.2 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) id = "id", family = binomial(link = "logit"), method = "REML", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) select[is.na(select)] <- 0 obj1.outfile.gds.select.2 <- tempfile() obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) } obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.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.txt.select.2 <- read.table(obj1.outfile.txt.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.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.bgen.noselect.1, obj2.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.bgen.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj2.outfile.bgen.select.3) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.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.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) obj1.outfile.txt2.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) "Allele2")) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.outfile.gds.select.3 <- tempfile() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.gds.select.1, obj2.gds.select.3) select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.2 <- tempfile() obj2.outfile.txt.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) "Allele2")) obj2.outfile.bed.select.2 <- tempfile() obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, expect_equal(obj2.txt.select.1, obj2.txt.select.3) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj2.outfile.bgen.noselect.2 <- 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", outfile = obj2.outfile.bgen.noselect.2) "Allele2")) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.outfile.bgen.select.2 <- tempfile() obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, select = select, outfile = obj2.outfile.bgen.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.outfile.gds.noselect.2 <- tempfile() unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1)) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj2.outfile.gds.select.2 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj2.gds.select.2 <- read.table(obj2.outfile.gds.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.gds.select.1, obj2.gds.select.2) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, } obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt.select.2 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, "Allele2")) obj1.outfile.txt2.select.3)) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, "Allele2")) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.outfile.txt2.select.2 <- tempfile()}) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 33: select = select, infile.header.print = c("SNP", "Allele1", eval(code, test_env) "Allele2"))
obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, 34: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)
idx <- sample(nrow(kins))35: kins <- kins[idx, idx]withCallingHandlers({ obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, eval(code, test_env) id = "id", family = binomial(link = "logit"), method = "REML", new_expectations <- the$test_expectations > starting_expectations method.optim = "AI") if (snapshot_skipped) { select <- match(1:400, unique(obj1$id_include)) skip("On CRAN") select[is.na(select)] <- 0 } obj1.outfile.bed.noselect.3 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) skip_empty() obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) if (on_cran()) { obj1.outfile.bed.select.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) } obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj1.bed.select.1, obj1.bed.select.3) invokeRestart("muffle_cran_snapshot") obj1.outfile.bgen.noselect.3 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, error = handle_error, interrupt = handle_interrupt) outfile = obj1.outfile.bgen.noselect.3)
obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, 36: header = TRUE, as.is = TRUE)doTryCatch(return(expr), name, parentenv, handler) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)
obj1.outfile.bgen.select.3 <- tempfile()37: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, tryCatchOne(expr, names, parentenv, handlers[[1L]]) select = select, outfile = obj1.outfile.bgen.select.3)
obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, 38: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 39: quietly = TRUE)) {tryCatch(withCallingHandlers({ obj1.outfile.gds.noselect.3 <- tempfile() eval(code, test_env) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) new_expectations <- the$test_expectations > starting_expectations obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) } obj1.outfile.gds.select.3 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) skip_empty() obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.gds.select.1, obj1.gds.select.3) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt.select.3 <- tempfile() } glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, }, snapshot_on_cran = function(cnd) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, snapshot_skipped <<- TRUE select = select, infile.header.print = c("SNP", "Allele1", invokeRestart("muffle_cran_snapshot") "Allele2"))}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt.select.1, obj1.txt.select.3)40: obj1.outfile.txt1.select.3 <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 41: select = select, infile.header.print = c("SNP", "Allele1", withOneRestart(expr, restarts[[1L]]) "Allele2"))
obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, 42: header = TRUE, as.is = TRUE)withRestarts(tryCatch(withCallingHandlers({ expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) eval(code, test_env) obj1.outfile.txt2.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, if (snapshot_skipped) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip("On CRAN") select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) else if (!new_expectations && skip_on_empty) { obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, if (on_cran()) { id = "id", family = binomial(link = "logit"), method = "REML", skip(paste0("{", e$package, "} is not installed.")) method.optim = "AI") } select <- match(1:400, unique(obj2$id_include))}, snapshot_on_cran = function(cnd) { select[is.na(select)] <- 0 snapshot_skipped <<- TRUE obj2.outfile.bed.noselect.3 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), header = TRUE, as.is = TRUE) end_test = function() { expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) }) obj2.outfile.bed.select.3 <- tempfile()
glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3)43: obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, test_code(code = exprs, env = env, reporter = get_reporter() %||% header = TRUE, as.is = TRUE) StopReporter$new()) expect_equal(obj2.bed.select.1, obj2.bed.select.3)
obj2.outfile.bgen.noselect.3 <- tempfile()44: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, source_file(path, env = env(env), desc = desc, shuffle = shuffle, outfile = obj2.outfile.bgen.noselect.3) error_call = error_call) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3,
header = TRUE, as.is = TRUE)45: expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)FUN(X[[i]], ...) obj2.outfile.bgen.select.3 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 46: select = select, outfile = obj2.outfile.bgen.select.3)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, error_call = error_call) header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)47: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", doTryCatch(return(expr), name, parentenv, handler) quietly = TRUE)) {
obj2.outfile.gds.noselect.3 <- tempfile()48: glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3,
header = TRUE, as.is = TRUE)49: expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)tryCatchList(expr, classes, parentenv, handlers) obj2.outfile.gds.select.3 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)50: obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, tryCatch(code, testthat_abort_reporter = function(cnd) { header = TRUE, as.is = TRUE) cat(conditionMessage(cnd), "\n") expect_equal(obj2.gds.select.1, obj2.gds.select.3) NULL }}) obj2.outfile.txt.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 51: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, with_reporter(reporters$multi, lapply(test_paths, test_one_file, select = select, infile.header.print = c("SNP", "Allele1", env = env, desc = desc, shuffle = shuffle, error_call = error_call)) "Allele2"))
obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, 52: header = TRUE, as.is = TRUE)test_files_serial(test_dir = test_dir, test_package = test_package, expect_equal(obj2.txt.select.1, obj2.txt.select.3) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, obj2.outfile.txt1.select.3 <- tempfile() env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, desc = desc, load_package = load_package, shuffle = shuffle, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, error_call = error_call) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))53: obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, test_files(test_dir = path, test_paths = test_paths, test_package = package, header = TRUE, as.is = TRUE) reporter = reporter, load_helpers = load_helpers, env = env, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj2.outfile.txt2.select.3 <- tempfile() load_package = load_package, parallel = parallel, shuffle = shuffle) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 54: select = select, infile.header.print = c("SNP", "Allele1", test_dir("testthat", package = package, reporter = reporter, "Allele2")) ..., load_package = "installed") obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,
header = TRUE, as.is = TRUE)55: expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)test_check("GMMAT") unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,
obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, An irrecoverable exception occurred. R is aborting now ...
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