Last updated on 2026-11-08 12:52:41 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.64 | 240.29 | 285.93 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 36.65 | 219.80 | 256.45 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 26.00 | 141.72 | 167.72 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 166.67 | 208.67 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | OK | ||||
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | ERROR | ||||
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 88.00 | 384.00 | 472.00 | 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$P,
Traceback:
infile, paste0(outfile, "_tmp.", i), center2, 1: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, eval(c.expr, envir = args, enclos = envir) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 2: eval(c.expr, envir = args, enclos = envir) 1) }
3: else {doTryCatch(return(expr), name, parentenv, handler) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 4: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, tryCatchOne(expr, names, parentenv, handlers[[1L]]) nperbatch, select, threadInfo$begin[i], threadInfo$end[i],
threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 5: 1) }tryCatchList(expr, classes, parentenv, handlers) }
else { 6: if (bgenInfo$LayoutFlag == 2) {tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i,
7: obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], FUN(X[[i]], ...) miss.cutoff, miss.method, nperbatch, select,
threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], 8: bgenInfo$N, bgenInfo$CompressionFlag, 1) }lapply(X = S, FUN = FUN, ...) else {
.Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, 9: obj$Sigma_iX, obj$cov, infile, paste0(outfile, doTryCatch(return(expr), name, parentenv, handler) "_tmp.", i), center2, MAF.range[1], MAF.range[2],
miss.cutoff, miss.method, nperbatch, select, 10: threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], tryCatchOne(expr, names, parentenv, handlers[[1L]])
bgenInfo$N, bgenInfo$CompressionFlag, 1)11: }tryCatchList(expr, classes, parentenv, handlers)
}12: }tryCatch(expr, error = function(e) {
call <- conditionCall(e)20: if (!is.null(call)) {glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (identical(call[[1L]], quote(doTryCatch))) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) call <- sys.call(-4L)
dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ")21: LONG <- 75Leval(code, test_env) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")22: if (is.na(w)) eval(code, test_env) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")23: if (w > LONG) withCallingHandlers({ prefix <- paste0(prefix, "\n ") eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations else prefix <- "Error : " if (snapshot_skipped) { msg <- paste0(prefix, conditionMessage(e), "\n") skip("On CRAN") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { } cat(msg, file = outFile) else if (!new_expectations && skip_on_empty) { .Internal(printDeferredWarnings()) skip_empty() } }}, expectation = handle_expectation, packageNotFoundError = function(e) { invisible(structure(msg, class = "try-error", condition = e)) if (on_cran()) {}) skip(paste0("{", e$package, "} is not installed."))
13: }try(lapply(X = S, FUN = FUN, ...), silent = TRUE)}, snapshot_on_cran = function(cnd) {
snapshot_skipped <<- TRUE14: invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) error = handle_error, interrupt = handle_interrupt)
15: 24: FUN(X[[i]], ...)doTryCatch(return(expr), name, parentenv, handler)
16: 25: lapply(seq_len(cores), inner.do)tryCatchOne(expr, names, parentenv, handlers[[1L]])
17: 26: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, tryCatchList(expr, classes, parentenv, handlers) mc.silent = silent, mc.cores = cores)
27: 18: tryCatch(withCallingHandlers({e$fun(obj, substitute(ex), parent.frame(), e$data) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations19: if (snapshot_skipped) {foreach(i = 1:ncores) %dopar% { skip("On CRAN") if (!is.null(obj$P)) { } if (bgenInfo$LayoutFlag == 2) { else if (!new_expectations && skip_on_empty) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, skip_empty() infile, paste0(outfile, "_tmp.", i), center2, } MAF.range[1], MAF.range[2], miss.cutoff, miss.method, }, expectation = handle_expectation, packageNotFoundError = function(e) { nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) if (on_cran()) { } skip(paste0("{", e$package, "} is not installed.")) else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, } infile, paste0(outfile, "_tmp.", i), center2, }, snapshot_on_cran = function(cnd) { MAF.range[1], MAF.range[2], miss.cutoff, miss.method, snapshot_skipped <<- TRUE nperbatch, select, threadInfo$begin[i], threadInfo$end[i], invokeRestart("muffle_cran_snapshot") threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, }, skip = handle_skip, warning = handle_warning, message = handle_message, 1) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) }
}28: else {doWithOneRestart(return(expr), restart) if (bgenInfo$LayoutFlag == 2) {
.Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, 29: obj$Sigma_iX, obj$cov, infile, paste0(outfile, withOneRestart(expr, restarts[[1L]]) "_tmp.", i), center2, MAF.range[1], MAF.range[2],
miss.cutoff, miss.method, nperbatch, select, 30: threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], withRestarts(tryCatch(withCallingHandlers({ bgenInfo$N, bgenInfo$CompressionFlag, 1) eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations else { if (snapshot_skipped) { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, skip("On CRAN") obj$Sigma_iX, obj$cov, infile, paste0(outfile, } "_tmp.", i), center2, MAF.range[1], MAF.range[2], else if (!new_expectations && skip_on_empty) { miss.cutoff, miss.method, nperbatch, select, skip_empty() threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], } bgenInfo$N, bgenInfo$CompressionFlag, 1)}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { } skip(paste0("{", e$package, "} is not installed."))} }
}, snapshot_on_cran = function(cnd) {20: snapshot_skipped <<- TRUEglmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, invokeRestart("muffle_cran_snapshot") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)}, skip = handle_skip, warning = handle_warning, message = handle_message,
error = handle_error, interrupt = handle_interrupt), error = handle_fatal), 21: end_test = function() {eval(code, test_env) })
22: eval(code, test_env)31:
test_code(code, parent.frame())23:
withCallingHandlers({32: eval(code, test_env) new_expectations <- the$test_expectations > starting_expectationstest_that("cross-sectional id le 400 binomial", { if (snapshot_skipped) { plinkfiles <- strsplit(system.file("extdata", "geno.bed", skip("On CRAN") package = "GMMAT"), ".bed", fixed = TRUE)[[1]] } bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") else if (!new_expectations && skip_on_empty) { samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") skip_empty() gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") } txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")}, expectation = handle_expectation, packageNotFoundError = function(e) { txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") if (on_cran()) { txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") skip(paste0("{", e$package, "} is not installed.")) } data(example)}, snapshot_on_cran = function(cnd) { suppressWarnings(RNGversion("3.5.0")) snapshot_skipped <<- TRUE set.seed(123) invokeRestart("muffle_cran_snapshot") pheno <- rbind(example$pheno, example$pheno[1:100, ])}, skip = handle_skip, warning = handle_warning, message = handle_message, pheno$id <- 1:500 error = handle_error, interrupt = handle_interrupt) pheno$disease[sample(1:500, 20)] <- NA
pheno$age[sample(1:500, 20)] <- NA24: pheno$sex[sample(1:500, 20)] <- NAdoTryCatch(return(expr), name, parentenv, handler) pheno <- pheno[sample(1:500, 450), ]
pheno <- pheno[pheno$id <= 400, ]25: kins <- example$GRMtryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins,
id = "id", family = binomial(link = "logit"), method = "REML", 26: method.optim = "AI")tryCatchList(expr, classes, parentenv, handlers) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 027: obj1.outfile.bed.noselect.1 <- tempfile()tryCatch(withCallingHandlers({ glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) eval(code, test_env) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { obj1.outfile.bed.noselect.1.tmp <- tempfile() skip("On CRAN") 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.") else if (!new_expectations && skip_on_empty) { skip_empty() unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() } glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE obj1.outfile.bgen.noselect.1 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }, skip = handle_skip, warning = handle_warning, message = handle_message, outfile = obj1.outfile.bgen.noselect.1) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1,
header = TRUE, as.is = TRUE)28: obj1.outfile.bgen.noselect.1.tmp <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, 29: withOneRestart(expr, restarts[[1L]]) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)
unlink(obj1.outfile.bgen.noselect.1.tmp)30: obj1.outfile.bgen.select.1 <- tempfile()withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations select = select, outfile = obj1.outfile.bgen.select.1) if (snapshot_skipped) { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) else if (!new_expectations && skip_on_empty) { expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", skip_empty() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, }}, expectation = handle_expectation, packageNotFoundError = function(e) { c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", if (on_cran()) { "VAR", "PVAL")]) skip(paste0("{", e$package, "} is not installed.")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) {}, snapshot_on_cran = function(cnd) { obj1.outfile.gds.noselect.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) invokeRestart("muffle_cran_snapshot") obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.outfile.gds.noselect.1.tmp <- tempfile() end_test = function() { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, }) ncores = 2)
obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, 31: header = TRUE, as.is = TRUE)test_code(code, parent.frame()) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp)
unlink(obj1.outfile.gds.noselect.1.tmp)32: obj1.outfile.gds.select.1 <- tempfile()test_that("cross-sectional id le 400 binomial", { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") 0.986534857))) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") } data(example) obj1.outfile.txt.select.1 <- tempfile() suppressWarnings(RNGversion("3.5.0")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, set.seed(123) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- rbind(example$pheno, example$pheno[1:100, ]) select = select, infile.header.print = c("SNP", "Allele1", pheno$id <- 1:500 "Allele2")) pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, pheno <- pheno[sample(1:500, 450), ] header = TRUE, as.is = TRUE) pheno <- pheno[pheno$id <= 400, ] expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) kins <- example$GRM obj1.outfile.txt.select.1.tmp <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, id = "id", family = binomial(link = "logit"), method = "REML", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, method.optim = "AI") select = select, infile.header.print = c("SNP", "Allele1", select <- match(1:400, unique(obj1$id_include)) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.outfile.txt1.select.1 <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.noselect.1.tmp <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, "Allele2")) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, unlink(obj1.outfile.bed.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt1.select.1) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.outfile.txt2.select.1 <- tempfile() obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, outfile = obj1.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.bgen.noselect.1.tmp <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt2.select.1)) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) skip_on_cran() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) method.optim = "AI") unlink(obj1.outfile.bgen.noselect.1.tmp) select <- match(1:400, unique(obj2$id_include)) obj1.outfile.bgen.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bed.noselect.1 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.1) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj2.outfile.bed.select.1 <- tempfile() expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { outfile = obj2.outfile.bgen.noselect.1) obj1.outfile.gds.noselect.1 <- tempfile() obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, 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, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.1.tmp <- tempfile() select = select, outfile = obj2.outfile.bgen.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) unlink(obj1.outfile.gds.noselect.1.tmp) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.outfile.gds.select.1 <- tempfile() "VAR", "PVAL")]) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() 0.986534857))) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) header = TRUE, as.is = TRUE) } obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, select = select, infile.header.print = c("SNP", "Allele1", 0.996996766))) "Allele2")) } obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, 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.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") "Allele2")) unlink(obj1.outfile.txt.select.1.tmp) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.1 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt1.select.1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj2.outfile.txt2.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) idx <- sample(nrow(pheno)) skip_on_cran() pheno <- pheno[idx, ] obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj2.bed.select.1 <- read.table(obj2.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(obj2.bed.noselect.1, obj2.bed.select.1) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1, obj1.bed.select.2) outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, select = select, outfile = obj2.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.2 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, select = select, outfile = obj1.outfile.bgen.select.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, "VAR", "PVAL")]) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) quietly = TRUE)) { if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.gds.noselect.1 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj1.outfile.gds.noselect.2 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(obj1.gds.select.1, obj1.gds.select.2) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) } } obj1.outfile.txt.select.2 <- tempfile() obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.outfile.txt1.select.2 <- tempfile() obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.outfile.txt2.select.2 <- tempfile() obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, idx <- sample(nrow(pheno)) id = "id", family = binomial(link = "logit"), method = "REML", pheno <- pheno[idx, ] method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select <- match(1:400, unique(obj1$id_include)) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.outfile.bed.select.2 <- tempfile() obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.2 <- tempfile() outfile = obj1.outfile.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, select = select, outfile = obj2.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.2 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, outfile = obj1.outfile.bgen.select.2) quietly = TRUE)) { obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj2.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj1.outfile.gds.noselect.2 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } } obj2.outfile.txt.select.2 <- tempfile() obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj1.outfile.txt1.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.outfile.txt2.select.2 <- tempfile() 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, select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) "Allele2")) obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj2.txt2.select.2 <- read.table(obj2.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(obj2.txt2.select.1, obj2.txt2.select.2) "Allele2")) idx <- sample(nrow(kins)) kins <- kins[idx, idx] obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj1.outfile.bed.select.3 <- tempfile() obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) outfile = obj1.outfile.bgen.noselect.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj1.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.bgen.select.2 <- tempfile() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj1.outfile.gds.select.3 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.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(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj2.txt.select.1, obj2.txt.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt1.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, obj1.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt2.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) method.optim = "AI") idx <- sample(nrow(kins)) select <- match(1:400, unique(obj2$id_include)) kins <- kins[idx, idx] select[is.na(select)] <- 0 obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.bed.noselect.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) method.optim = "AI") obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj1.outfile.bed.noselect.3 <- tempfile() obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj1.outfile.bed.select.3 <- tempfile() obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) outfile = obj1.outfile.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) select = select, outfile = obj2.outfile.bgen.select.3) obj1.outfile.bgen.select.3 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.3) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj1.outfile.gds.noselect.3 <- tempfile() obj2.outfile.gds.select.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) } obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, expect_equal(obj1.gds.select.1, obj1.gds.select.3) 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.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt2.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, obj1.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, "Allele2")) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.outfile.txt2.select.1)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, select <- match(1:400, unique(obj2$id_include)) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, select[is.na(select)] <- 0 obj1.outfile.txt2.select.2)) obj2.outfile.bed.noselect.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2)) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj2.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, 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.bed.select.1, obj2.bed.select.3) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.bgen.noselect.3 <- tempfile() obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt2.select.3)) outfile = obj2.outfile.bgen.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, quietly = TRUE)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.bgen.select.3 <- tempfile() obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, }) header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)33: eval(code, test_env) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {34: obj2.outfile.gds.noselect.3 <- tempfile()eval(code, test_env) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)
obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, 35: header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) eval(code, test_env) obj2.outfile.gds.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) if (snapshot_skipped) { obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.select.1, obj2.gds.select.3) else if (!new_expectations && skip_on_empty) { } skip_empty() obj2.outfile.txt.select.3 <- tempfile() } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, }, expectation = handle_expectation, packageNotFoundError = function(e) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (on_cran()) { select = select, infile.header.print = c("SNP", "Allele1", skip(paste0("{", e$package, "} is not installed.")) "Allele2")) } obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt1.select.3 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, error = handle_error, interrupt = handle_interrupt) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))36: obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 37: 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"))38: obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, tryCatchList(expr, classes, parentenv, handlers) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)
unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, 39: obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, tryCatch(withCallingHandlers({ obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, eval(code, test_env) obj2.outfile.txt2.select.1)) new_expectations <- the$test_expectations > starting_expectations unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, if (snapshot_skipped) { obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, skip("On CRAN") obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, } obj1.outfile.txt2.select.2)) else if (!new_expectations && skip_on_empty) { unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, skip_empty() obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, } obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.txt2.select.2)) if (on_cran()) { unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, } obj1.outfile.txt2.select.3))}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, invokeRestart("muffle_cran_snapshot") obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj2.outfile.txt2.select.3))
if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 40: quietly = TRUE)) doWithOneRestart(return(expr), restart) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1,
obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, 41: obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, withOneRestart(expr, restarts[[1L]]) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3,
obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})42:
withRestarts(tryCatch(withCallingHandlers({33: eval(code, test_env)eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {34: skip("On CRAN")eval(code, test_env) }
else if (!new_expectations && skip_on_empty) {35: skip_empty()withCallingHandlers({ } eval(code, test_env)}, expectation = handle_expectation, packageNotFoundError = function(e) { new_expectations <- the$test_expectations > starting_expectations if (on_cran()) { if (snapshot_skipped) { skip(paste0("{", e$package, "} is not installed.")) skip("On CRAN") } }}, snapshot_on_cran = function(cnd) { else if (!new_expectations && skip_on_empty) { skip_empty() } snapshot_skipped <<- TRUE}, expectation = handle_expectation, packageNotFoundError = function(e) { invokeRestart("muffle_cran_snapshot") if (on_cran()) {}, skip = handle_skip, warning = handle_warning, message = handle_message, skip(paste0("{", e$package, "} is not installed.")) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), } end_test = function() {}, snapshot_on_cran = function(cnd) { }) snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")
}, skip = handle_skip, warning = handle_warning, message = handle_message, 43: error = handle_error, interrupt = handle_interrupt)test_code(code = exprs, env = env, reporter = get_reporter() %||%
StopReporter$new())36:
doTryCatch(return(expr), name, parentenv, handler)44:
source_file(path, env = env(env), desc = desc, shuffle = shuffle, 37: error_call = error_call)tryCatchOne(expr, names, parentenv, handlers[[1L]])
45: 38: FUN(X[[i]], ...)tryCatchList(expr, classes, parentenv, handlers)
46: 39: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, tryCatch(withCallingHandlers({ error_call = error_call) eval(code, test_env)
new_expectations <- the$test_expectations > starting_expectations47: if (snapshot_skipped) {doTryCatch(return(expr), name, parentenv, handler) skip("On CRAN")
}48: else if (!new_expectations && skip_on_empty) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) skip_empty()
}49: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatchList(expr, classes, parentenv, handlers)
50: if (on_cran()) {tryCatch(code, testthat_abort_reporter = function(cnd) { skip(paste0("{", e$package, "} is not installed.")) cat(conditionMessage(cnd), "\n") } NULL}, snapshot_on_cran = function(cnd) {}) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, }, skip = handle_skip, warning = handle_warning, message = handle_message, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
52: 40: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, doWithOneRestart(return(expr), restart)
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 41: desc = desc, load_package = load_package, shuffle = shuffle, withOneRestart(expr, restarts[[1L]]) error_call = error_call)
42:
withRestarts(tryCatch(withCallingHandlers({53: eval(code, test_env)test_files(test_dir = path, test_paths = test_paths, test_package = package, new_expectations <- the$test_expectations > starting_expectations reporter = reporter, load_helpers = load_helpers, env = env, if (snapshot_skipped) { stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, skip("On CRAN") load_package = load_package, parallel = parallel, shuffle = shuffle)
}54: else if (!new_expectations && skip_on_empty) {test_dir("testthat", package = package, reporter = reporter, skip_empty() ..., load_package = "installed") }
}, expectation = handle_expectation, packageNotFoundError = function(e) {55: if (on_cran()) {test_check("GMMAT")
skip(paste0("{", e$package, "} is not installed.")) }An irrecoverable exception occurred. R is aborting now ...
}, 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