Last updated on 2026-09-26 09:51:26 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 46.20 | 244.93 | 291.13 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.73 | 206.56 | 245.29 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 29.00 | 153.51 | 182.51 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 162.06 | 204.06 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.93 | 241.79 | 293.72 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 51.95 | 232.79 | 284.74 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 70.00 | 310.00 | 380.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 15.00 | 76.00 | 91.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 89.00 | 394.00 | 483.00 | 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],
Traceback:
miss.cutoff, miss.method, nperbatch, select, 1: threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], eval(c.expr, envir = args, enclos = envir) bgenInfo$N, bgenInfo$CompressionFlag, 1)
} 2: }eval(c.expr, envir = args, enclos = envir)}
3: 20: doTryCatch(return(expr), name, parentenv, handler)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) 4:
tryCatchOne(expr, names, parentenv, handlers[[1L]])21:
eval(code, test_env) 5:
tryCatchList(expr, classes, parentenv, handlers)22:
eval(code, test_env) 6:
tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)23:
withCallingHandlers({ 7: eval(code, test_env)FUN(X[[i]], ...)
8: new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) {lapply(X = S, FUN = FUN, ...) skip("On CRAN")
} 9: else if (!new_expectations && skip_on_empty) {doTryCatch(return(expr), name, parentenv, handler) skip_empty()
}10: }, expectation = handle_expectation, packageNotFoundError = function(e) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (on_cran()) {
skip(paste0("{", e$package, "} is not installed.")) }11: }, snapshot_on_cran = function(cnd) {tryCatchList(expr, classes, parentenv, handlers) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)12:
tryCatch(expr, error = function(e) {24: call <- conditionCall(e)doTryCatch(return(expr), name, parentenv, handler) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L)25: dcall <- deparse(call, nlines = 1L)tryCatchOne(expr, names, parentenv, handlers[[1L]]) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]]26: w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")tryCatchList(expr, classes, parentenv, handlers) if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], 27: type = "b")tryCatch(withCallingHandlers({ if (w > LONG) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations prefix <- paste0(prefix, "\n ") if (snapshot_skipped) { } else prefix <- "Error : " skip("On CRAN") msg <- paste0(prefix, conditionMessage(e), "\n") } .Internal(seterrmessage(msg[1L])) else if (!new_expectations && skip_on_empty) { if (!silent && isTRUE(getOption("show.error.messages"))) { skip_empty() cat(msg, file = outFile) } .Internal(printDeferredWarnings()) }}, expectation = handle_expectation, packageNotFoundError = function(e) { invisible(structure(msg, class = "try-error", condition = e))}) if (on_cran()) { skip(paste0("{", e$package, "} is not installed."))
}13: }, snapshot_on_cran = function(cnd) {try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, 14: error = handle_error, interrupt = handle_interrupt), error = handle_fatal)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
28: 15: doWithOneRestart(return(expr), restart)FUN(X[[i]], ...)
29: 16: lapply(seq_len(cores), inner.do)withOneRestart(expr, restarts[[1L]])
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, 30: mc.silent = silent, mc.cores = cores)withRestarts(tryCatch(withCallingHandlers({
eval(code, test_env)18: new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) {e$fun(obj, substitute(ex), parent.frame(), e$data)
19: skip("On CRAN")foreach(i = 1:ncores) %dopar% { } if (!is.null(obj$P)) { else if (!new_expectations && skip_on_empty) { if (bgenInfo$LayoutFlag == 2) { skip_empty() .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, }}, expectation = handle_expectation, packageNotFoundError = function(e) { infile, paste0(outfile, "_tmp.", i), center2, if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, } nperbatch, select, threadInfo$begin[i], threadInfo$end[i], }, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, invokeRestart("muffle_cran_snapshot") 1)}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), } end_test = function() { else { }) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P,
infile, paste0(outfile, "_tmp.", i), center2, 31: MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], test_code(code, parent.frame()) threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag,
1)32: }test_that("cross-sectional id le 400 binomial", { } plinkfiles <- strsplit(system.file("extdata", "geno.bed", else { package = "GMMAT"), ".bed", fixed = TRUE)[[1]] if (bgenInfo$LayoutFlag == 2) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") bgenInfo$N, bgenInfo$CompressionFlag, 1) } txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") else { data(example) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, suppressWarnings(RNGversion("3.5.0")) obj$Sigma_iX, obj$cov, infile, paste0(outfile, set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) "_tmp.", i), center2, MAF.range[1], MAF.range[2], pheno$id <- 1:500 miss.cutoff, miss.method, nperbatch, select, pheno$disease[sample(1:500, 20)] <- NA threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], pheno$age[sample(1:500, 20)] <- NA bgenInfo$N, bgenInfo$CompressionFlag, 1) pheno$sex[sample(1:500, 20)] <- NA } pheno <- pheno[sample(1:500, 450), ] } pheno <- pheno[pheno$id <= 400, ]} kins <- example$GRM
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 20: id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, method.optim = "AI") outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) select <- match(1:400, unique(obj1$id_include))
select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile()21: glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, eval(code, test_env) header = TRUE, as.is = TRUE)
obj1.outfile.bed.noselect.1.tmp <- tempfile()22: expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")eval(code, test_env) unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile()
23: glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, if (snapshot_skipped) { outfile = obj1.outfile.bgen.noselect.1) skip("On CRAN") obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) else if (!new_expectations && skip_on_empty) { skip_empty() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, } header = TRUE, as.is = TRUE)}, expectation = handle_expectation, packageNotFoundError = function(e) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) if (on_cran()) { obj1.outfile.bgen.select.1 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1)}, snapshot_on_cran = function(cnd) { obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, snapshot_skipped <<- TRUE header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", error = handle_error, interrupt = handle_interrupt) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[,
c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 24: "VAR", "PVAL")])doTryCatch(return(expr), name, parentenv, handler) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {25: obj1.outfile.gds.noselect.1 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1)
obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, 26: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) obj1.outfile.gds.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, 27: ncores = 2)tryCatch(withCallingHandlers({ obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, eval(code, test_env) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.gds.select.1 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) } else if (!new_expectations && skip_on_empty) { obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) skip_empty() } expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, 0.986534857))) skip(paste0("{", e$package, "} is not installed.")) } unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) }}, snapshot_on_cran = function(cnd) { obj1.outfile.txt.select.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, 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", "Allele2")) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1,
header = TRUE, as.is = TRUE)28: expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)doWithOneRestart(return(expr), restart) 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, select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")29: unlink(obj1.outfile.txt.select.1.tmp)withOneRestart(expr, restarts[[1L]]) obj1.outfile.txt1.select.1 <- tempfile()
glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, 30: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", withRestarts(tryCatch(withCallingHandlers({ "Allele2")) eval(code, test_env) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, new_expectations <- the$test_expectations > starting_expectations header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() if (snapshot_skipped) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, skip("On CRAN") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) else if (!new_expectations && skip_on_empty) { obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) skip_empty() } unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, skip(paste0("{", e$package, "} is not installed.")) obj1.outfile.txt2.select.1)) skip_on_cran() } obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, }, snapshot_on_cran = function(cnd) { id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") select <- match(1:400, unique(obj2$id_include))}, skip = handle_skip, warning = handle_warning, message = handle_message, select[is.na(select)] <- 0 error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) }) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1,
header = TRUE, as.is = TRUE)31: obj2.outfile.bed.select.1 <- tempfile()test_code(code, parent.frame()) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)
obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, 32: test_that("cross-sectional id le 400 binomial", { header = TRUE, as.is = TRUE) plinkfiles <- strsplit(system.file("extdata", "geno.bed", expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] obj2.outfile.bgen.noselect.1 <- tempfile() bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") outfile = obj2.outfile.bgen.noselect.1) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj2.outfile.bgen.select.1 <- tempfile() data(example) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, suppressWarnings(RNGversion("3.5.0")) select = select, outfile = obj2.outfile.bgen.select.1) set.seed(123) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, pheno <- rbind(example$pheno, example$pheno[1:100, ]) header = TRUE, as.is = TRUE) pheno$id <- 1:500 expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) pheno$disease[sample(1:500, 20)] <- NA expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", pheno$age[sample(1:500, 20)] <- NA "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, pheno$sex[sample(1:500, 20)] <- NA c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] "VAR", "PVAL")]) kins <- example$GRM if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, quietly = TRUE)) { id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.gds.noselect.1 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) select <- match(1:400, unique(obj1$id_include)) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1 <- tempfile() obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, unlink(obj1.outfile.bed.noselect.1.tmp) 0.996996766))) obj1.outfile.bed.select.1 <- tempfile() } glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) "Allele2")) obj1.outfile.bgen.noselect.1 <- tempfile() obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) header = TRUE, as.is = TRUE) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.bgen.noselect.1.tmp <- 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.1.tmp, ncores = 2) "Allele2")) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) unlink(obj1.outfile.bgen.noselect.1.tmp) obj2.outfile.txt2.select.1 <- tempfile() obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, outfile = obj1.outfile.bgen.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", idx <- sample(nrow(pheno)) "VAR", "PVAL")]) pheno <- pheno[idx, ] if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, quietly = TRUE)) { id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.gds.noselect.1 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) select <- match(1:400, unique(obj1$id_include)) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.2 <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.outfile.bed.select.2 <- tempfile() unlink(obj1.outfile.gds.noselect.1.tmp) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.outfile.gds.select.1 <- tempfile() obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, expect_equal(obj1.bed.select.1, obj1.bed.select.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) outfile = obj1.outfile.bgen.noselect.2) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, header = TRUE, as.is = TRUE) 0.986534857))) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj1.outfile.bgen.select.2) obj1.outfile.txt.select.1 <- tempfile() obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", "Allele2")) quietly = TRUE)) { obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj1.outfile.txt.select.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") header = TRUE, as.is = TRUE) unlink(obj1.outfile.txt.select.1.tmp) expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.outfile.txt1.select.1 <- tempfile() } glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, obj1.outfile.txt.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.2) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 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")) 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) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.2 <- tempfile() obj1.outfile.txt2.select.1)) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, skip_on_cran() 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.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) select[is.na(select)] <- 0 expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.outfile.bed.noselect.1 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) method.optim = "AI") obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, select <- match(1:400, unique(obj2$id_include)) header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() select[is.na(select)] <- 0 obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.noselect.2 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, outfile = obj2.outfile.bgen.noselect.2) header = TRUE, as.is = TRUE) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.outfile.bgen.select.2 <- tempfile() c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "VAR", "PVAL")]) select = select, outfile = obj2.outfile.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.outfile.gds.noselect.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) quietly = TRUE)) { obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, obj2.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.outfile.gds.select.1 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) header = TRUE, as.is = TRUE) obj2.outfile.gds.select.2 <- tempfile() expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.outfile.txt.select.1 <- tempfile() } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.outfile.txt.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", 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, obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj2.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, 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")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.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", expect_equal(obj2.txt.select.1, obj2.txt1.select.1) "Allele2")) obj2.outfile.txt2.select.1 <- tempfile() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) 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, obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.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", expect_equal(obj2.txt.select.1, obj2.txt2.select.1) "Allele2")) idx <- sample(nrow(pheno)) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) pheno <- pheno[idx, ] expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, idx <- sample(nrow(kins)) id = "id", family = binomial(link = "logit"), method = "REML", kins <- kins[idx, idx] method.optim = "AI") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select <- match(1:400, unique(obj1$id_include)) id = "id", family = binomial(link = "logit"), method = "REML", select[is.na(select)] <- 0 method.optim = "AI") obj1.outfile.bed.noselect.2 <- tempfile() select <- match(1:400, unique(obj1$id_include)) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) select[is.na(select)] <- 0 obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj1.outfile.bed.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.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.bed.select.1, obj1.bed.select.2) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj1.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) outfile = obj1.outfile.bgen.noselect.3) obj1.outfile.bgen.select.2 <- tempfile() obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) select = select, outfile = obj1.outfile.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) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.outfile.gds.select.2 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 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(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() expect_equal(obj1.gds.select.1, obj1.gds.select.2) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) } obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.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.gds.select.1, obj1.gds.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } obj1.outfile.txt.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.txt1.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, expect_equal(obj1.txt.select.1, obj1.txt.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt1.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) select <- match(1:400, unique(obj2$id_include)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select <- match(1:400, unique(obj2$id_include)) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.3 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, obj2.outfile.bgen.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1, obj2.bed.select.3) outfile = obj2.outfile.bgen.noselect.2) obj2.outfile.bgen.noselect.3 <- tempfile() obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.3) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.outfile.bgen.select.3 <- tempfile() obj2.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { quietly = TRUE)) { obj2.outfile.gds.noselect.3 <- tempfile() obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.outfile.gds.select.3 <- tempfile() obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) expect_equal(obj2.gds.select.1, obj2.gds.select.3) obj2.outfile.gds.select.2 <- tempfile() 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) obj2.outfile.txt.select.3 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt1.select.3 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt.select.1, obj2.txt.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.txt1.select.2 <- tempfile() "Allele2")) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) "Allele2")) obj2.outfile.txt2.select.3 <- tempfile() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, idx <- sample(nrow(kins)) obj2.outfile.txt2.select.1)) kins <- kins[idx, idx] unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) obj1.outfile.txt2.select.2)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj1.bed.noselect.3 <- read.table(obj1.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(obj1.bed.noselect.1, obj1.bed.noselect.3) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, outfile = obj1.outfile.bgen.noselect.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.outfile.txt2.select.3)) header = TRUE, as.is = TRUE) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, quietly = TRUE)) { obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) obj1.outfile.gds.noselect.3 <- tempfile()}) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)
obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, 33: header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)eval(code, test_env) obj1.outfile.gds.select.3 <- tempfile()
glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)34: obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.select.1, obj1.gds.select.3)35: }withCallingHandlers({ obj1.outfile.txt.select.3 <- tempfile() eval(code, test_env) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, new_expectations <- the$test_expectations > starting_expectations infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (snapshot_skipped) { select = select, infile.header.print = c("SNP", "Allele1", skip("On CRAN") "Allele2")) } obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj1.txt.select.1, obj1.txt.select.3) } obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, infile.header.print = c("SNP", "Allele1", if (on_cran()) { "Allele2")) skip(paste0("{", e$package, "} is not installed.")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) snapshot_skipped <<- TRUE obj1.outfile.txt2.select.3 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, error = handle_error, interrupt = handle_interrupt) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, 36: doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt2.select.1, obj1.txt2.select.3)37: obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, tryCatchOne(expr, names, parentenv, handlers[[1L]]) id = "id", family = binomial(link = "logit"), method = "REML",
method.optim = "AI")38: select <- match(1:400, unique(obj2$id_include))tryCatchList(expr, classes, parentenv, handlers) select[is.na(select)] <- 0
obj2.outfile.bed.noselect.3 <- tempfile()39: glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3)tryCatch(withCallingHandlers({ obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile() if (snapshot_skipped) { glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) skip("On CRAN") obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, } else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj2.bed.select.1, obj2.bed.select.3) } obj2.outfile.bgen.noselect.3 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (on_cran()) { outfile = obj2.outfile.bgen.noselect.3) skip(paste0("{", e$package, "} is not installed.")) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, }, snapshot_on_cran = function(cnd) { select = select, outfile = obj2.outfile.bgen.select.3) snapshot_skipped <<- TRUE obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools",
quietly = TRUE)) {40: obj2.outfile.gds.noselect.3 <- tempfile()doWithOneRestart(return(expr), restart) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3)
obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, 41: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3)
obj2.outfile.gds.select.3 <- tempfile()42: glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3)withRestarts(tryCatch(withCallingHandlers({ obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj2.gds.select.1, obj2.gds.select.3) if (snapshot_skipped) { } skip("On CRAN") obj2.outfile.txt.select.3 <- tempfile() } glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, else if (!new_expectations && skip_on_empty) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, skip_empty() select = select, infile.header.print = c("SNP", "Allele1", } "Allele2"))}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.txt.select.1, obj2.txt.select.3)}, snapshot_on_cran = function(cnd) { obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, snapshot_skipped <<- TRUE infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, invokeRestart("muffle_cran_snapshot") select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), header = TRUE, as.is = TRUE) end_test = function() { }) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3)
43: obj2.outfile.txt2.select.3 <- tempfile()test_code(code = exprs, env = env, reporter = get_reporter() %||% glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, StopReporter$new()) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))44: obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, source_file(path, env = env(env), desc = desc, shuffle = shuffle, header = TRUE, as.is = TRUE) error_call = error_call) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)
unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, 45: obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, FUN(X[[i]], ...) obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1,
obj2.outfile.txt2.select.1))46: unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, error_call = error_call) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2,
obj1.outfile.txt2.select.2))47: unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2,
obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, 48: obj2.outfile.txt2.select.2))tryCatchOne(expr, names, parentenv, handlers[[1L]]) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3,
obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, 49: obj1.outfile.txt2.select.3))tryCatchList(expr, classes, parentenv, handlers) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3,
obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, 50: obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") obj2.outfile.txt2.select.3)) NULL if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }) quietly = TRUE))
unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, 51: obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, with_reporter(reporters$multi, lapply(test_paths, test_one_file, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
52: 33: test_files_serial(test_dir = test_dir, test_package = test_package, eval(code, test_env) test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 34: desc = desc, load_package = load_package, shuffle = shuffle, eval(code, test_env) error_call = error_call)
35: 53: withCallingHandlers({test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, eval(code, test_env) stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, new_expectations <- the$test_expectations > starting_expectations load_package = load_package, parallel = parallel, shuffle = shuffle)
if (snapshot_skipped) { skip("On CRAN")54: test_dir("testthat", package = package, reporter = reporter, } ..., load_package = "installed")
else if (!new_expectations && skip_on_empty) {55: skip_empty()test_check("GMMAT") }
}, expectation = handle_expectation, packageNotFoundError = function(e) {An irrecoverable exception occurred. R is aborting now ...
if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64