Last updated on 2026-11-10 12:51:42 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.64 | 240.29 | 285.93 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 40.16 | 205.56 | 245.72 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 26.00 | 141.72 | 167.72 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 393.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | OK | ||||
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 69.00 | 322.00 | 391.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 14.00 | 77.00 | 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 | 85.00 | 389.00 | 474.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
21: eval(code, test_env)
22: eval(code, test_env)
23: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN")
Traceback:
} else if (!new_expectations && skip_on_empty) { 1: skip_empty()eval(c.expr, envir = args, enclos = envir) }
}, expectation = handle_expectation, packageNotFoundError = function(e) { 2: if (on_cran()) {eval(c.expr, envir = args, enclos = envir) skip(paste0("{", e$package, "} is not installed."))
} 3: }, snapshot_on_cran = function(cnd) {doTryCatch(return(expr), name, parentenv, handler) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot") 4: }, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatchOne(expr, names, parentenv, handlers[[1L]]) error = handle_error, interrupt = handle_interrupt)
5: 24: tryCatchList(expr, classes, parentenv, handlers)doTryCatch(return(expr), name, parentenv, handler)
6: 25: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)tryCatchOne(expr, names, parentenv, handlers[[1L]])
7:
FUN(X[[i]], ...)26:
tryCatchList(expr, classes, parentenv, handlers) 8:
lapply(X = S, FUN = FUN, ...)27:
tryCatch(withCallingHandlers({ 9: eval(code, test_env)doTryCatch(return(expr), name, parentenv, handler) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) {
skip("On CRAN")10: tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
else if (!new_expectations && skip_on_empty) {11: skip_empty()tryCatchList(expr, classes, parentenv, handlers) }
}, expectation = handle_expectation, packageNotFoundError = function(e) {12: if (on_cran()) {tryCatch(expr, error = function(e) { skip(paste0("{", e$package, "} is not installed.")) } call <- conditionCall(e)}, snapshot_on_cran = function(cnd) { if (!is.null(call)) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") if (identical(call[[1L]], quote(doTryCatch))) }, skip = handle_skip, warning = handle_warning, message = handle_message, call <- sys.call(-4L) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) dcall <- deparse(call, nlines = 1L)
prefix <- paste("Error in", dcall, ": ")28: LONG <- 75LdoWithOneRestart(return(expr), restart) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")29: if (is.na(w)) withOneRestart(expr, restarts[[1L]]) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")30: if (w > LONG) withRestarts(tryCatch(withCallingHandlers({ prefix <- paste0(prefix, "\n ") eval(code, test_env) } new_expectations <- the$test_expectations > starting_expectations else prefix <- "Error : " if (snapshot_skipped) { msg <- paste0(prefix, conditionMessage(e), "\n") skip("On CRAN") .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) { else if (!new_expectations && skip_on_empty) { cat(msg, file = outFile) skip_empty() .Internal(printDeferredWarnings()) } }}, expectation = handle_expectation, packageNotFoundError = function(e) { invisible(structure(msg, class = "try-error", condition = e)) if (on_cran()) {}) skip(paste0("{", e$package, "} is not installed."))
}13: }, snapshot_on_cran = function(cnd) {try(lapply(X = S, FUN = FUN, ...), silent = TRUE) snapshot_skipped <<- TRUE
invokeRestart("muffle_cran_snapshot")14: }, skip = handle_skip, warning = handle_warning, message = handle_message, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) error = handle_error, interrupt = handle_interrupt), error = handle_fatal),
end_test = function() {15: FUN(X[[i]], ...) })
16: 31: lapply(seq_len(cores), inner.do)test_code(code, parent.frame())
32: 17: test_that("cross-sectional id le 400 binomial", {mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, plinkfiles <- strsplit(system.file("extdata", "geno.bed", mc.silent = silent, mc.cores = cores) package = "GMMAT"), ".bed", fixed = TRUE)[[1]]
bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT")18: txtfile <- system.file("extdata", "geno.txt", package = "GMMAT")e$fun(obj, substitute(ex), parent.frame(), e$data)
txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT")19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") if (bgenInfo$LayoutFlag == 2) { data(example) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, suppressWarnings(RNGversion("3.5.0")) infile, paste0(outfile, "_tmp.", i), center2, set.seed(123) MAF.range[1], MAF.range[2], miss.cutoff, miss.method, pheno <- rbind(example$pheno, example$pheno[1:100, ]) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, pheno$id <- 1:500 1) } pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA else { pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, pheno <- pheno[pheno$id <= 400, ] infile, paste0(outfile, "_tmp.", i), center2, kins <- example$GRM MAF.range[1], MAF.range[2], miss.cutoff, miss.method, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", nperbatch, select, threadInfo$begin[i], threadInfo$end[i], method.optim = "AI") threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, select <- match(1:400, unique(obj1$id_include)) 1) select[is.na(select)] <- 0 } obj1.outfile.bed.noselect.1 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) else { obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, if (bgenInfo$LayoutFlag == 2) { header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, obj$Sigma_iX, obj$cov, infile, paste0(outfile, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") "_tmp.", i), center2, MAF.range[1], MAF.range[2], unlink(obj1.outfile.bed.noselect.1.tmp) miss.cutoff, miss.method, nperbatch, select, obj1.outfile.bed.select.1 <- tempfile() threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) bgenInfo$N, bgenInfo$CompressionFlag, 1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, } header = TRUE, as.is = TRUE) else { expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj1.outfile.bgen.noselect.1 <- tempfile() obj$Sigma_iX, obj$cov, infile, paste0(outfile, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], outfile = obj1.outfile.bgen.noselect.1) miss.cutoff, miss.method, nperbatch, select, obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], header = TRUE, as.is = TRUE) bgenInfo$N, bgenInfo$CompressionFlag, 1) obj1.outfile.bgen.noselect.1.tmp <- tempfile() } glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)} obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp,
header = TRUE, as.is = TRUE)20: expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(obj1.outfile.bgen.noselect.1.tmp) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.outfile.bgen.select.1 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 21: select = select, outfile = obj1.outfile.bgen.select.1)eval(code, test_env) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1,
header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1)22: expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", eval(code, test_env) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[,
c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", 23: "VAR", "PVAL")])withCallingHandlers({ if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", eval(code, test_env) quietly = TRUE)) { new_expectations <- the$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)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) skip(paste0("{", e$package, "} is not installed.")) unlink(obj1.outfile.gds.noselect.1.tmp) } obj1.outfile.gds.select.1 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) snapshot_skipped <<- TRUE obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) error = handle_error, interrupt = handle_interrupt) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL))
expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, 24: 0.986534857)))doTryCatch(return(expr), name, parentenv, handler) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))
}25: obj1.outfile.txt.select.1 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 26: select = select, infile.header.print = c("SNP", "Allele1", "Allele2"))tryCatchList(expr, classes, parentenv, handlers) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1,
header = TRUE, as.is = TRUE)27: expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)tryCatch(withCallingHandlers({ obj1.outfile.txt.select.1.tmp <- tempfile() eval(code, test_env) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, new_expectations <- the$test_expectations > starting_expectations infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (snapshot_skipped) { select = select, infile.header.print = c("SNP", "Allele1", skip("On CRAN") "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") } unlink(obj1.outfile.txt.select.1.tmp) else if (!new_expectations && skip_on_empty) { obj1.outfile.txt1.select.1 <- tempfile() skip_empty() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, infile.header.print = c("SNP", "Allele1", if (on_cran()) { "Allele2")) skip(paste0("{", e$package, "} is not installed.")) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj1.txt.select.1, obj1.txt1.select.1) snapshot_skipped <<- TRUE obj1.outfile.txt2.select.1 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, }, 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), error = handle_fatal) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))28: obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, doWithOneRestart(return(expr), restart) header = TRUE, as.is = TRUE)
29: expect_equal(obj1.txt.select.1, obj1.txt2.select.1)withOneRestart(expr, restarts[[1L]]) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1,
obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, 30: obj1.outfile.txt2.select.1))withRestarts(tryCatch(withCallingHandlers({ skip_on_cran() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, eval(code, test_env) id = "id", family = binomial(link = "logit"), method = "REML", new_expectations <- the$test_expectations > starting_expectations method.optim = "AI") if (snapshot_skipped) { select <- match(1:400, unique(obj2$id_include)) skip("On CRAN") select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.1 <- tempfile() else if (!new_expectations && skip_on_empty) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) skip_empty() obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) } obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) } obj2.outfile.bgen.noselect.1 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, snapshot_skipped <<- TRUE outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, invokeRestart("muffle_cran_snapshot") header = TRUE, as.is = TRUE)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.bgen.select.1 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, end_test = function() { select = select, outfile = obj2.outfile.bgen.select.1) }) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1,
header = TRUE, as.is = TRUE)31: expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)test_code(code, parent.frame()) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1",
"A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, 32: c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", test_that("cross-sectional id le 400 binomial", { "VAR", "PVAL")]) plinkfiles <- strsplit(system.file("extdata", "geno.bed", if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] quietly = TRUE)) { bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") obj2.outfile.gds.noselect.1 <- tempfile() samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, suppressWarnings(RNGversion("3.5.0")) header = TRUE, as.is = TRUE) set.seed(123) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) pheno <- rbind(example$pheno, example$pheno[1:100, ]) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) pheno$id <- 1:500 } pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA obj2.outfile.txt.select.1 <- tempfile() pheno$sex[sample(1:500, 20)] <- NA glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, pheno <- pheno[sample(1:500, 450), ] infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- pheno[pheno$id <= 400, ] select = select, infile.header.print = c("SNP", "Allele1", kins <- example$GRM "Allele2")) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) obj2.outfile.txt1.select.1 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, obj1.outfile.bed.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj1.outfile.bed.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj2.outfile.txt2.select.1 <- tempfile() unlink(obj1.outfile.bed.noselect.1.tmp) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) "Allele2")) obj1.outfile.bgen.noselect.1 <- tempfile() obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, idx <- sample(nrow(pheno)) pheno <- pheno[idx, ] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj1.outfile.bgen.noselect.1.tmp <- tempfile() 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)) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj1.outfile.bed.noselect.2 <- tempfile() unlink(obj1.outfile.bgen.noselect.1.tmp) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.outfile.bgen.select.1 <- tempfile() obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj1.outfile.bed.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, expect_equal(obj1.bed.select.1, obj1.bed.select.2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) obj1.outfile.bgen.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, quietly = TRUE)) { outfile = obj1.outfile.bgen.noselect.2) obj1.outfile.gds.noselect.1 <- tempfile() obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) header = TRUE, as.is = TRUE) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, select = select, outfile = obj1.outfile.bgen.select.2) ncores = 2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) unlink(obj1.outfile.gds.noselect.1.tmp) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.outfile.gds.select.1 <- tempfile() quietly = TRUE)) { glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.outfile.gds.noselect.2 <- tempfile() obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) header = TRUE, as.is = TRUE) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) 0.986534857))) obj1.outfile.gds.select.2 <- tempfile() unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) } obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.select.1, obj1.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", } "Allele2")) obj1.outfile.txt.select.2 <- tempfile() obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.1.tmp <- tempfile() "Allele2")) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, obj1.txt.select.2 <- read.table(obj1.outfile.txt.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.txt.select.1, obj1.txt.select.2) "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1.outfile.txt1.select.2 <- tempfile() unlink(obj1.outfile.txt.select.1.tmp) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.outfile.txt1.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.2 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt1.select.1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, obj1.outfile.txt2.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, "Allele2")) select = select, infile.header.print = c("SNP", "Allele1", obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) id = "id", family = binomial(link = "logit"), method = "REML", unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, method.optim = "AI") obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, select <- match(1:400, unique(obj2$id_include)) obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, select[is.na(select)] <- 0 obj1.outfile.txt2.select.1)) obj2.outfile.bed.noselect.2 <- tempfile() skip_on_cran() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) select <- match(1:400, unique(obj2$id_include)) obj2.outfile.bed.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.outfile.bed.noselect.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj2.bed.select.1, obj2.bed.select.2) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.1 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) obj2.outfile.bgen.select.2 <- tempfile() obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.1) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) obj2.outfile.gds.noselect.2 <- tempfile() expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.gds.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.outfile.gds.noselect.1 <- tempfile() obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) obj2.outfile.gds.select.1 <- tempfile() } glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.outfile.txt.select.2 <- tempfile() obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, select = select, infile.header.print = c("SNP", "Allele1", 0.996996766))) } "Allele2")) obj2.outfile.txt.select.1 <- tempfile() obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, 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.txt.select.1 <- read.table(obj2.outfile.txt.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.bed.select.1$PVAL, obj2.txt.select.1$PVAL) "Allele2")) obj2.outfile.txt1.select.1 <- tempfile() obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.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.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.txt2.select.2 <- read.table(obj2.outfile.txt2.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.txt2.select.1, obj2.txt2.select.2) select = select, infile.header.print = c("SNP", "Allele1", idx <- sample(nrow(kins)) "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, kins <- kins[idx, idx] header = TRUE, as.is = TRUE) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) id = "id", family = binomial(link = "logit"), method = "REML", pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, method.optim = "AI") id = "id", family = binomial(link = "logit"), method = "REML", select <- match(1:400, unique(obj1$id_include)) method.optim = "AI") select[is.na(select)] <- 0 select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(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, obj1.bed.select.3) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3 <- tempfile() expect_equal(obj1.bed.select.1, obj1.bed.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bgen.noselect.2 <- tempfile() outfile = obj1.outfile.bgen.noselect.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) select = select, outfile = obj1.outfile.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3 <- tempfile() expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.gds.select.3 <- tempfile() obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) header = TRUE, as.is = TRUE) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) expect_equal(obj1.gds.select.1, obj1.gds.select.3) obj1.outfile.gds.select.2 <- tempfile() } glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.txt.select.3 <- tempfile() obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.select.1, obj1.gds.select.2) select = select, infile.header.print = c("SNP", "Allele1", } obj1.outfile.txt.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt.select.1, obj1.txt.select.3) "Allele2")) obj1.outfile.txt1.select.3 <- tempfile() obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.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(obj1.txt.select.1, obj1.txt.select.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt1.select.2 <- tempfile() "Allele2")) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, obj1.txt1.select.3 <- read.table(obj1.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", "Allele2")) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.txt2.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, "Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) "Allele2")) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, 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.2) select <- match(1:400, unique(obj2$id_include)) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.3 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) select <- match(1:400, unique(obj2$id_include)) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj2.outfile.bed.noselect.2 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.outfile.bed.select.3 <- tempfile() obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.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.select.1, obj2.bed.select.3) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1, obj2.bed.select.2) outfile = obj2.outfile.bgen.noselect.3) obj2.outfile.bgen.noselect.2 <- tempfile() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) select = select, outfile = obj2.outfile.bgen.select.3) obj2.outfile.bgen.select.2 <- tempfile() obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj2.outfile.gds.noselect.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) quietly = TRUE)) { obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, obj2.outfile.gds.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, obj2.outfile.gds.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) expect_equal(obj2.gds.select.1, obj2.gds.select.3) 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, } obj2.outfile.txt.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.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.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.3 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.2) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj2.outfile.txt1.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile1, outfile = obj2.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", obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.3 <- tempfile() obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) "Allele2")) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) idx <- sample(nrow(kins)) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, kins <- kins[idx, idx] obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.txt2.select.1)) method.optim = "AI") unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, select <- match(1:400, unique(obj1$id_include)) obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, select[is.na(select)] <- 0 obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.bed.noselect.3 <- tempfile() obj1.outfile.txt2.select.2)) 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, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj2.outfile.txt2.select.2)) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.bed.select.3 <- tempfile() obj1.outfile.txt2.select.3)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.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, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj2.outfile.txt2.select.3)) obj1.outfile.bgen.noselect.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, outfile = obj1.outfile.bgen.noselect.3) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) 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, 33: eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)34: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", eval(code, test_env) quietly = TRUE)) {
obj1.outfile.gds.noselect.3 <- tempfile()35: glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)withCallingHandlers({ obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) if (snapshot_skipped) { obj1.outfile.gds.select.3 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) } obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, else if (!new_expectations && skip_on_empty) { skip_empty() header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.select.1, obj1.gds.select.3)}, expectation = handle_expectation, packageNotFoundError = function(e) { } if (on_cran()) { obj1.outfile.txt.select.3 <- tempfile() skip(paste0("{", e$package, "} is not installed.")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, snapshot_on_cran = function(cnd) { select = select, infile.header.print = c("SNP", "Allele1", snapshot_skipped <<- TRUE "Allele2")) invokeRestart("muffle_cran_snapshot") obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt) expect_equal(obj1.txt.select.1, obj1.txt.select.3)
obj1.outfile.txt1.select.3 <- tempfile()36: glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, doTryCatch(return(expr), name, parentenv, handler) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))37: tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3,
header = TRUE, as.is = TRUE)38: tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)
obj1.outfile.txt2.select.3 <- tempfile()39: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, tryCatch(withCallingHandlers({ infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations select = select, infile.header.print = c("SNP", "Allele1", if (snapshot_skipped) { "Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) else if (!new_expectations && skip_on_empty) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, skip_empty() id = "id", family = binomial(link = "logit"), method = "REML", }}, expectation = handle_expectation, packageNotFoundError = function(e) { method.optim = "AI") if (on_cran()) { select <- match(1:400, unique(obj2$id_include)) skip(paste0("{", e$package, "} is not installed.")) select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.3 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) snapshot_skipped <<- TRUE obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) invokeRestart("muffle_cran_snapshot") expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3)}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.bed.select.3 <- tempfile() error = handle_error, interrupt = handle_interrupt), error = handle_fatal) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3,
header = TRUE, as.is = TRUE)40: expect_equal(obj2.bed.select.1, obj2.bed.select.3)doWithOneRestart(return(expr), restart)
obj2.outfile.bgen.noselect.3 <- tempfile()41: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, withOneRestart(expr, restarts[[1L]]) outfile = obj2.outfile.bgen.noselect.3)
obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 42: header = TRUE, as.is = TRUE)withRestarts(tryCatch(withCallingHandlers({ expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) eval(code, test_env) obj2.outfile.bgen.select.3 <- tempfile() new_expectations <- the$test_expectations > starting_expectations glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, if (snapshot_skipped) { select = select, outfile = obj2.outfile.bgen.select.3) skip("On CRAN") obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, } header = TRUE, as.is = TRUE) else if (!new_expectations && skip_on_empty) { expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) skip_empty() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", } quietly = TRUE)) {}, expectation = handle_expectation, packageNotFoundError = function(e) { obj2.outfile.gds.noselect.3 <- tempfile() if (on_cran()) { glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) skip(paste0("{", e$package, "} is not installed.")) obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) snapshot_skipped <<- TRUE obj2.outfile.gds.select.3 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) invokeRestart("muffle_cran_snapshot") obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, }, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal), expect_equal(obj2.gds.select.1, obj2.gds.select.3) end_test = function() { } }) obj2.outfile.txt.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 43: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, test_code(code = exprs, env = env, reporter = get_reporter() %||% select = select, infile.header.print = c("SNP", "Allele1", StopReporter$new()) "Allele2"))
obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, 44: header = TRUE, as.is = TRUE)source_file(path, env = env(env), desc = desc, shuffle = shuffle, expect_equal(obj2.txt.select.1, obj2.txt.select.3) error_call = error_call) obj2.outfile.txt1.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, 45: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, FUN(X[[i]], ...) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))46: obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) error_call = error_call) obj2.outfile.txt2.select.3 <- tempfile()
glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, 47: doTryCatch(return(expr), name, parentenv, handler) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 48: "Allele2"))tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3,
header = TRUE, as.is = TRUE)49: expect_equal(obj2.txt2.select.1, obj2.txt2.select.3)tryCatchList(expr, classes, parentenv, handlers) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1,
obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, 50: obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, tryCatch(code, testthat_abort_reporter = function(cnd) { obj2.outfile.txt2.select.1)) cat(conditionMessage(cnd), "\n") unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, NULL obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, }) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2,
obj1.outfile.txt2.select.2))51: unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, with_reporter(reporters$multi, lapply(test_paths, test_one_file, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2,
obj2.outfile.txt2.select.2))52: unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, test_files_serial(test_dir = test_dir, test_package = test_package, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj1.outfile.txt2.select.3)) desc = desc, load_package = load_package, shuffle = shuffle, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, error_call = error_call) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3,
obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.3))53: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", test_files(test_dir = path, test_paths = test_paths, test_package = package, quietly = TRUE)) reporter = reporter, load_helpers = load_helpers, env = env, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, load_package = load_package, parallel = parallel, shuffle = shuffle) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2,
obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, 54: obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))test_dir("testthat", package = package, reporter = reporter, }) ..., load_package = "installed")
33: 55: eval(code, test_env)test_check("GMMAT")
34: eval(code, test_env)An irrecoverable exception occurred. R is aborting now ...
35: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64