Last updated on 2026-11-14 21:51:48 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.58 | 240.25 | 283.83 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.92 | 215.31 | 254.23 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 158.84 | 188.84 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 72.00 | 391.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 53.75 | 246.68 | 300.43 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 68.00 | 314.00 | 382.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 77.00 | 94.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 391.00 | 477.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i],
Traceback:
bgenInfo$N, bgenInfo$CompressionFlag, 1) 1: } }eval(c.expr, envir = args, enclos = envir)
} 2:
eval(c.expr, envir = args, enclos = envir)20:
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 3: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)doTryCatch(return(expr), name, parentenv, handler)
21: 4: eval(code, test_env)tryCatchOne(expr, names, parentenv, handlers[[1L]])
22: 5: eval(code, test_env)tryCatchList(expr, classes, parentenv, handlers)
23:
withCallingHandlers({ 6: eval(code, test_env)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) { 7: skip("On CRAN")FUN(X[[i]], ...) }
else if (!new_expectations && skip_on_empty) { 8: skip_empty()lapply(X = S, FUN = FUN, ...) }
9: }, expectation = handle_expectation, packageNotFoundError = function(e) {doTryCatch(return(expr), name, parentenv, handler) if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))
}10: }, snapshot_on_cran = function(cnd) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, 11: error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
tryCatchList(expr, classes, parentenv, handlers)25:
tryCatchOne(expr, names, parentenv, handlers[[1L]])12:
tryCatch(expr, error = function(e) {26: tryCatchList(expr, classes, parentenv, handlers) call <- conditionCall(e)
if (!is.null(call)) {27: if (identical(call[[1L]], quote(doTryCatch))) tryCatch(withCallingHandlers({ call <- sys.call(-4L) eval(code, test_env) dcall <- deparse(call, nlines = 1L) new_expectations <- the$test_expectations > starting_expectations prefix <- paste("Error in", dcall, ": ") if (snapshot_skipped) { LONG <- 75L skip("On CRAN") 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], else if (!new_expectations && skip_on_empty) { type = "b") skip_empty() if (w > LONG) } prefix <- paste0(prefix, "\n ")}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { else prefix <- "Error : " skip(paste0("{", e$package, "} is not installed.")) msg <- paste0(prefix, conditionMessage(e), "\n") }}, snapshot_on_cran = function(cnd) { .Internal(seterrmessage(msg[1L])) snapshot_skipped <<- TRUE if (!silent && isTRUE(getOption("show.error.messages"))) { invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, cat(msg, file = outFile) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) .Internal(printDeferredWarnings())
} invisible(structure(msg, class = "try-error", condition = e))})28:
doWithOneRestart(return(expr), restart)13:
try(lapply(X = S, FUN = FUN, ...), silent = TRUE)29:
withOneRestart(expr, restarts[[1L]])14:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))30:
withRestarts(tryCatch(withCallingHandlers({15: eval(code, test_env)FUN(X[[i]], ...) new_expectations <- the$test_expectations > starting_expectations
if (snapshot_skipped) {16: skip("On CRAN")lapply(seq_len(cores), inner.do) }
else if (!new_expectations && skip_on_empty) {17: skip_empty()mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, } mc.silent = silent, mc.cores = cores)}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) {
skip(paste0("{", e$package, "} is not installed."))18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: }foreach(i = 1:ncores) %dopar% {}, snapshot_on_cran = function(cnd) { if (!is.null(obj$P)) { snapshot_skipped <<- TRUE if (bgenInfo$LayoutFlag == 2) { invokeRestart("muffle_cran_snapshot") .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, }, skip = handle_skip, warning = handle_warning, message = handle_message, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), nperbatch, select, threadInfo$begin[i], threadInfo$end[i], end_test = function() { threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, }) 1)
}31: else {test_code(code, parent.frame()) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 32: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, test_that("cross-sectional id le 400 binomial", { plinkfiles <- strsplit(system.file("extdata", "geno.bed", nperbatch, select, threadInfo$begin[i], threadInfo$end[i], package = "GMMAT"), ".bed", fixed = TRUE)[[1]] threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") 1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") } gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") } else { txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") if (bgenInfo$LayoutFlag == 2) { txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], data(example) miss.cutoff, miss.method, nperbatch, select, suppressWarnings(RNGversion("3.5.0")) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], set.seed(123) bgenInfo$N, bgenInfo$CompressionFlag, 1) pheno <- rbind(example$pheno, example$pheno[1:100, ]) } pheno$id <- 1:500 else { pheno$disease[sample(1:500, 20)] <- NA .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, pheno$age[sample(1:500, 20)] <- NA obj$Sigma_iX, obj$cov, infile, paste0(outfile, pheno$sex[sample(1:500, 20)] <- NA "_tmp.", i), center2, MAF.range[1], MAF.range[2], pheno <- pheno[sample(1:500, 450), ] miss.cutoff, miss.method, nperbatch, select, pheno <- pheno[pheno$id <= 400, ] threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], kins <- example$GRM bgenInfo$N, bgenInfo$CompressionFlag, 1) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, } id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI")} select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 020: obj1.outfile.bed.noselect.1 <- tempfile()glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1,
header = TRUE, as.is = TRUE)21: eval(code, test_env) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp,
ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")22: unlink(obj1.outfile.bed.noselect.1.tmp)eval(code, test_env) obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)
obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, 23: withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) new_expectations <- the$test_expectations > starting_expectations obj1.outfile.bgen.noselect.1 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip("On CRAN") outfile = obj1.outfile.bgen.noselect.1) } else if (!new_expectations && skip_on_empty) { skip_empty() obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.outfile.bgen.noselect.1.tmp <- tempfile() if (on_cran()) { glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, skip(paste0("{", e$package, "} is not installed.")) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) } obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, }, snapshot_on_cran = function(cnd) { header = TRUE, as.is = TRUE) snapshot_skipped <<- TRUE expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) invokeRestart("muffle_cran_snapshot") unlink(obj1.outfile.bgen.noselect.1.tmp)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj1.outfile.bgen.select.1 <- tempfile() error = handle_error, interrupt = handle_interrupt) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
select = select, outfile = obj1.outfile.bgen.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, 24: header = TRUE, as.is = TRUE)doTryCatch(return(expr), name, parentenv, handler) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)
expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", 25: "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: "VAR", "PVAL")])tryCatchList(expr, classes, parentenv, handlers) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
27: tryCatch(withCallingHandlers({ eval(code, test_env) quietly = TRUE)) { new_expectations <- the$test_expectations > starting_expectations obj1.outfile.gds.noselect.1 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) skip("On CRAN") obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { obj1.outfile.gds.noselect.1.tmp <- tempfile() skip_empty() 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, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) skip(paste0("{", e$package, "} is not installed.")) unlink(obj1.outfile.gds.noselect.1.tmp) } obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1)}, snapshot_on_cran = function(cnd) { obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.gds.noselect.1, obj1.gds.select.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, 28: 0.986534857)))doWithOneRestart(return(expr), restart)
unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))29: }withOneRestart(expr, restarts[[1L]]) obj1.outfile.txt.select.1 <- tempfile()
30: glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, withRestarts(tryCatch(withCallingHandlers({ infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1", new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) if (on_cran()) { obj1.outfile.txt.select.1.tmp <- tempfile() skip(paste0("{", e$package, "} is not installed.")) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, 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), error = handle_fatal), infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, end_test = function() { select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) }) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE)
expect_equal(obj1.txt.select.1, obj1.txt1.select.1)31: obj1.outfile.txt2.select.1 <- tempfile()test_code(code, parent.frame()) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 32: select = select, infile.header.print = c("SNP", "Allele1", test_that("cross-sectional id le 400 binomial", { "Allele2")) plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.txt.select.1, obj1.txt2.select.1) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") obj1.outfile.txt2.select.1)) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") skip_on_cran() data(example) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, suppressWarnings(RNGversion("3.5.0")) id = "id", family = binomial(link = "logit"), method = "REML", set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA method.optim = "AI") pheno$age[sample(1:500, 20)] <- NA select <- match(1:400, unique(obj2$id_include)) pheno$sex[sample(1:500, 20)] <- NA select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() pheno <- pheno[sample(1:500, 450), ] glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) method.optim = "AI") obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.outfile.bed.noselect.1 <- tempfile() obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.1) obj1.outfile.bed.noselect.1.tmp <- tempfile() obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, header = TRUE, as.is = TRUE) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) obj2.outfile.bgen.select.1 <- tempfile() obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.1) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.outfile.bgen.noselect.1 <- tempfile() "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", outfile = obj1.outfile.bgen.noselect.1) "VAR", "PVAL")]) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) unlink(obj1.outfile.bgen.noselect.1.tmp) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj1.outfile.bgen.select.1 <- tempfile() expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) 0.996996766))) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, select = select, infile.header.print = c("SNP", "Allele1", c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "Allele2")) "VAR", "PVAL")]) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj1.outfile.gds.noselect.1 <- tempfile() obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.1.tmp <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, ncores = 2) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj2.outfile.txt2.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(obj1.outfile.gds.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.select.1 <- tempfile() "Allele2")) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) idx <- sample(nrow(pheno)) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) pheno <- pheno[idx, ] expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 0.986534857))) id = "id", family = binomial(link = "logit"), method = "REML", unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) method.optim = "AI") } select <- match(1:400, unique(obj1$id_include)) obj1.outfile.txt.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) "Allele2")) obj1.outfile.bed.select.2 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, 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"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, unlink(obj1.outfile.txt.select.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.1 <- tempfile() expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj1.outfile.bgen.select.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", select = select, outfile = obj1.outfile.bgen.select.2) "Allele2")) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.txt2.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, obj1.outfile.gds.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1)) expect_equal(obj1.gds.select.1, obj1.gds.select.2) skip_on_cran() } obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt.select.2 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj2.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj1.txt.select.1, obj1.txt.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt1.select.2 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj2.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() outfile = obj2.outfile.bgen.noselect.1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, 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", obj2.outfile.bgen.select.1 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) method.optim = "AI") expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", select <- match(1:400, unique(obj2$id_include)) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, select[is.na(select)] <- 0 c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", obj2.outfile.bed.noselect.2 <- tempfile() "VAR", "PVAL")]) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.outfile.bed.select.2 <- tempfile() obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) } obj2.outfile.txt.select.1 <- tempfile() obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, 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.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.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(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, 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", obj2.outfile.gds.noselect.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.txt2.select.1 <- tempfile() obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, } header = TRUE, as.is = TRUE) obj2.outfile.txt.select.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt2.select.1) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, idx <- sample(nrow(pheno)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- pheno[idx, ] select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.txt.select.1, obj2.txt.select.2) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.txt1.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj1.outfile.bed.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.2 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj2.outfile.txt2.select.2 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, 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, expect_equal(obj1.bed.select.1, obj1.bed.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, outfile = obj1.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) kins <- kins[idx, idx] obj1.outfile.bgen.select.2 <- tempfile() obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, id = "id", family = binomial(link = "logit"), method = "REML", select = select, outfile = obj1.outfile.bgen.select.2) method.optim = "AI") obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select <- match(1:400, unique(obj1$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj1.outfile.bed.noselect.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) quietly = TRUE)) { obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, 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.bed.noselect.1, obj1.bed.noselect.3) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj1.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.select.1, obj1.gds.select.2) outfile = obj1.outfile.bgen.noselect.3) } obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) select = select, outfile = obj1.outfile.bgen.select.3) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj1.outfile.txt1.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) "Allele2")) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.gds.select.3 <- read.table(obj1.outfile.gds.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.gds.select.1, obj1.gds.select.3) "Allele2")) } obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, obj1.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select = select, infile.header.print = c("SNP", "Allele1", id = "id", family = binomial(link = "logit"), method = "REML", "Allele2")) method.optim = "AI") obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.outfile.bed.noselect.2 <- tempfile() obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bed.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj1.outfile.txt2.select.3 <- tempfile() obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, outfile = obj2.outfile.bgen.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.2 <- tempfile() expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select = select, outfile = obj2.outfile.bgen.select.2) id = "id", family = binomial(link = "logit"), method = "REML", obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, method.optim = "AI") header = TRUE, as.is = TRUE) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) select[is.na(select)] <- 0 if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.bed.noselect.3 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.outfile.gds.noselect.2 <- tempfile() obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.3 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.outfile.gds.select.2 <- tempfile() obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) header = TRUE, as.is = TRUE) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, expect_equal(obj2.bed.select.1, obj2.bed.select.3) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj2.outfile.bgen.noselect.3) obj2.outfile.txt.select.2 <- tempfile() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 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.bgen.noselect.1, obj2.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) expect_equal(obj2.txt.select.1, obj2.txt.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj2.outfile.txt1.select.2 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.outfile.gds.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.gds.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.outfile.txt2.select.2 <- tempfile() obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, 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, expect_equal(obj2.gds.select.1, obj2.gds.select.3) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj2.outfile.txt.select.3 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) idx <- sample(nrow(kins)) select = select, infile.header.print = c("SNP", "Allele1", kins <- kins[idx, idx] "Allele2")) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.txt.select.1, obj2.txt.select.3) select <- match(1:400, unique(obj1$id_include)) obj2.outfile.txt1.select.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj1.outfile.bed.noselect.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.3 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj2.outfile.txt2.select.3 <- tempfile() obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bgen.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3) obj2.outfile.txt2.select.1)) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2)) obj1.outfile.gds.noselect.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.2)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.gds.select.3 <- read.table(obj1.outfile.gds.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(obj1.gds.select.1, obj1.gds.select.3) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, } obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj1.outfile.txt.select.3 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) quietly = TRUE)) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) select = select, infile.header.print = c("SNP", "Allele1", }) "Allele2"))
obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, 33: header = TRUE, as.is = TRUE)eval(code, test_env) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)
obj1.outfile.txt2.select.3 <- tempfile()34: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, eval(code, test_env) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 35: "Allele2"))withCallingHandlers({ obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) if (snapshot_skipped) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip("On CRAN") id = "id", family = binomial(link = "logit"), method = "REML", } method.optim = "AI") else if (!new_expectations && skip_on_empty) { select <- match(1:400, unique(obj2$id_include)) skip_empty() select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) if (on_cran()) { obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)}, snapshot_on_cran = function(cnd) { obj2.outfile.bed.select.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) invokeRestart("muffle_cran_snapshot") obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt) expect_equal(obj2.bed.select.1, obj2.bed.select.3)
obj2.outfile.bgen.noselect.3 <- tempfile()36: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, doTryCatch(return(expr), name, parentenv, handler) outfile = obj2.outfile.bgen.noselect.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3,
header = TRUE, as.is = TRUE)37: expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.outfile.bgen.select.3 <- tempfile()
glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 38: select = select, outfile = obj2.outfile.bgen.select.3)tryCatchList(expr, classes, parentenv, handlers) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3,
header = TRUE, as.is = TRUE)39: expect_equal(obj2.bgen.select.1, obj2.bgen.select.3)tryCatch(withCallingHandlers({ if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", eval(code, test_env) quietly = TRUE)) { new_expectations <- the$test_expectations > starting_expectations obj2.outfile.gds.noselect.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) skip("On CRAN") obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) skip_empty() obj2.outfile.gds.select.3 <- tempfile() } glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj2.gds.select.1, obj2.gds.select.3) } }}, snapshot_on_cran = function(cnd) { obj2.outfile.txt.select.3 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, invokeRestart("muffle_cran_snapshot") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, skip = handle_skip, warning = handle_warning, message = handle_message, select = select, infile.header.print = c("SNP", "Allele1", error = handle_error, interrupt = handle_interrupt), error = handle_fatal) "Allele2"))
obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, 40: header = TRUE, as.is = TRUE)doWithOneRestart(return(expr), restart) expect_equal(obj2.txt.select.1, obj2.txt.select.3)
obj2.outfile.txt1.select.3 <- tempfile()41: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, withOneRestart(expr, restarts[[1L]]) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
42: select = select, infile.header.print = c("SNP", "Allele1", withRestarts(tryCatch(withCallingHandlers({ "Allele2")) eval(code, test_env) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) if (snapshot_skipped) { expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) skip("On CRAN") obj2.outfile.txt2.select.3 <- tempfile() } glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, else if (!new_expectations && skip_on_empty) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_empty() select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) } unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, }, snapshot_on_cran = function(cnd) { obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, snapshot_skipped <<- TRUE obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, invokeRestart("muffle_cran_snapshot") obj2.outfile.txt2.select.1))}, skip = handle_skip, warning = handle_warning, message = handle_message, unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, end_test = function() { 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, 43: obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, test_code(code = exprs, env = env, reporter = get_reporter() %||% obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, StopReporter$new()) obj2.outfile.txt2.select.2))
unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, 44: obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, error_call = error_call) obj1.outfile.txt2.select.3))
unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, 45: obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, FUN(X[[i]], ...) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3,
obj2.outfile.txt2.select.3))46: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, quietly = TRUE)) error_call = error_call) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1,
obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, 47: obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, doTryCatch(return(expr), name, parentenv, handler) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3,
obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))48: })tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: 33: tryCatchList(expr, classes, parentenv, handlers)eval(code, test_env)
50: 34: tryCatch(code, testthat_abort_reporter = function(cnd) {eval(code, test_env) cat(conditionMessage(cnd), "\n")
NULL35: })withCallingHandlers({
eval(code, test_env)51: new_expectations <- the$test_expectations > starting_expectationswith_reporter(reporters$multi, lapply(test_paths, test_one_file, if (snapshot_skipped) { env = env, desc = desc, shuffle = shuffle, error_call = error_call)) skip("On CRAN")
}52: else if (!new_expectations && skip_on_empty) {test_files_serial(test_dir = test_dir, test_package = test_package, skip_empty() } test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, }, expectation = handle_expectation, packageNotFoundError = function(e) { env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, if (on_cran()) { desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call) skip(paste0("{", e$package, "} is not installed."))
}53: }, snapshot_on_cran = function(cnd) {test_files(test_dir = path, test_paths = test_paths, test_package = package, snapshot_skipped <<- TRUE reporter = reporter, load_helpers = load_helpers, env = env, invokeRestart("muffle_cran_snapshot") stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, }, skip = handle_skip, warning = handle_warning, message = handle_message, load_package = load_package, parallel = parallel, shuffle = shuffle) error = handle_error, interrupt = handle_interrupt)
54: 36: test_dir("testthat", package = package, reporter = reporter, doTryCatch(return(expr), name, parentenv, handler) ..., load_package = "installed")
37: 55: tryCatchOne(expr, names, parentenv, handlers[[1L]])test_check("GMMAT")
38: An irrecoverable exception occurred. R is aborting now ...
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