Last updated on 2026-10-14 03:10:52 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.06 | 240.59 | 283.65 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 38.79 | 212.40 | 251.19 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 156.48 | 186.48 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 42.00 | 168.79 | 210.79 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 388.00 | 458.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 51.86 | 231.73 | 283.59 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 50.71 | 234.78 | 285.49 | 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 | 313.00 | 383.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 17.00 | 78.00 | 95.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 90.00 | 396.00 | 486.00 | 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") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
27: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
28: doWithOneRestart(return(expr), restart)
29: withOneRestart(expr, restarts[[1L]])
30: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
31: test_code(code, parent.frame())
32: test_that("cross-sectional id le 400 binomial", { plinkfiles <- strsplit(system.file("extdata", "geno.bed", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) suppressWarnings(RNGversion("3.5.0")) set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE)
Traceback:
1: obj1.outfile.bed.noselect.1.tmp <- tempfile()eval(c.expr, envir = args, enclos = envir)
expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, 2: ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")eval(c.expr, envir = args, enclos = envir) unlink(obj1.outfile.bed.noselect.1.tmp)
obj1.outfile.bed.select.1 <- tempfile() 3: glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)doTryCatch(return(expr), name, parentenv, handler) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) 4: obj1.outfile.bgen.noselect.1 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
5: outfile = obj1.outfile.bgen.noselect.1)tryCatchList(expr, classes, parentenv, handlers) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1,
6: header = TRUE, as.is = TRUE)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
obj1.outfile.bgen.noselect.1.tmp <- tempfile() 7: FUN(X[[i]], ...) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) 8: obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, lapply(X = S, FUN = FUN, ...) header = TRUE, as.is = TRUE)
expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) 9: unlink(obj1.outfile.bgen.noselect.1.tmp)doTryCatch(return(expr), name, parentenv, handler) obj1.outfile.bgen.select.1 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 10: select = select, outfile = obj1.outfile.bgen.select.1)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1,
header = TRUE, as.is = TRUE)11: expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", tryCatchList(expr, classes, parentenv, handlers)
"A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, 12: c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", tryCatch(expr, error = function(e) { "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", call <- conditionCall(e) quietly = TRUE)) { if (!is.null(call)) { obj1.outfile.gds.noselect.1 <- tempfile() if (identical(call[[1L]], quote(doTryCatch))) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) call <- sys.call(-4L) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, dcall <- deparse(call, nlines = 1L) header = TRUE, as.is = TRUE) prefix <- paste("Error in", dcall, ": ") obj1.outfile.gds.noselect.1.tmp <- tempfile() LONG <- 75L glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") ncores = 2) if (is.na(w)) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") header = TRUE, as.is = TRUE) if (w > LONG) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) prefix <- paste0(prefix, "\n ") unlink(obj1.outfile.gds.noselect.1.tmp) } obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") header = TRUE, as.is = TRUE) .Internal(seterrmessage(msg[1L])) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) if (!silent && isTRUE(getOption("show.error.messages"))) { expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) cat(msg, file = outFile) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, .Internal(printDeferredWarnings()) } 0.986534857))) invisible(structure(msg, class = "try-error", condition = e)) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))})
}13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE) obj1.outfile.txt.select.1 <- tempfile()
14: glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 15: select = select, infile.header.print = c("SNP", "Allele1", FUN(X[[i]], ...)
"Allele2"))16: obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, lapply(seq_len(cores), inner.do) header = TRUE, as.is = TRUE)
17: expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL)mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, obj1.outfile.txt.select.1.tmp <- tempfile() mc.silent = silent, mc.cores = cores) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 18: "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")e$fun(obj, substitute(ex), parent.frame(), e$data)
unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile()19: glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, if (bgenInfo$LayoutFlag == 2) { select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, expect_equal(obj1.txt.select.1, obj1.txt1.select.1) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, obj1.outfile.txt2.select.1 <- tempfile() 1) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", else { "Allele2")) .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, 1) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, } obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, } obj1.outfile.txt2.select.1)) else { skip_on_cran() if (bgenInfo$LayoutFlag == 2) { obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, method.optim = "AI") "_tmp.", i), center2, MAF.range[1], MAF.range[2], select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 miss.cutoff, miss.method, nperbatch, select, obj2.outfile.bed.noselect.1 <- tempfile() threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, bgenInfo$N, bgenInfo$CompressionFlag, 1) } header = TRUE, as.is = TRUE) else { obj2.outfile.bed.select.1 <- tempfile() .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, header = TRUE, as.is = TRUE) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) bgenInfo$N, bgenInfo$CompressionFlag, 1) obj2.outfile.bgen.noselect.1 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } outfile = obj2.outfile.bgen.noselect.1)}
obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, 20: header = TRUE, as.is = TRUE)glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.1 <- tempfile() outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile,
select = select, outfile = obj2.outfile.bgen.select.1) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, 21: eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)22: eval(code, test_env) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1",
"A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, 23: c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", withCallingHandlers({ "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", eval(code, test_env) quietly = TRUE)) { new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { obj2.outfile.gds.noselect.1 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) } obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, else if (!new_expectations && skip_on_empty) { skip_empty() header = TRUE, as.is = TRUE) } obj2.outfile.gds.select.1 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) if (on_cran()) { obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL))}, snapshot_on_cran = function(cnd) { expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, snapshot_skipped <<- TRUE 0.996996766))) } invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, error = handle_error, interrupt = handle_interrupt) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 24: "Allele2"))doTryCatch(return(expr), name, parentenv, handler) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1,
header = TRUE, as.is = TRUE)25: expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL)tryCatchOne(expr, names, parentenv, handlers[[1L]]) obj2.outfile.txt1.select.1 <- tempfile()
glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, 26: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, tryCatchList(expr, classes, parentenv, handlers) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))27: obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, tryCatch(withCallingHandlers({ header = TRUE, as.is = TRUE) eval(code, test_env) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { obj2.outfile.txt2.select.1 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, else if (!new_expectations && skip_on_empty) { select = select, infile.header.print = c("SNP", "Allele1", skip_empty() "Allele2")) } obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.txt.select.1, obj2.txt2.select.1) skip(paste0("{", e$package, "} is not installed.")) } idx <- sample(nrow(pheno))}, snapshot_on_cran = function(cnd) { pheno <- pheno[idx, ] snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI")
select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 028: doWithOneRestart(return(expr), restart) obj1.outfile.bed.noselect.2 <- tempfile()
glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2)29: obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, withOneRestart(expr, restarts[[1L]])
header = TRUE, as.is = TRUE)30: expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2)withRestarts(tryCatch(withCallingHandlers({ obj1.outfile.bed.select.2 <- tempfile() eval(code, test_env) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj1.bed.select.1, obj1.bed.select.2) else if (!new_expectations && skip_on_empty) { skip_empty() obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, }}, expectation = handle_expectation, packageNotFoundError = function(e) { outfile = obj1.outfile.bgen.noselect.2) if (on_cran()) { obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, skip(paste0("{", e$package, "} is not installed.")) } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) invokeRestart("muffle_cran_snapshot") obj1.outfile.bgen.select.2 <- tempfile()}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, end_test = function() { select = select, outfile = obj1.outfile.bgen.select.2) }) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2,
header = TRUE, as.is = TRUE)31: expect_equal(obj1.bgen.select.1, obj1.bgen.select.2)test_code(code, parent.frame()) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) {
obj1.outfile.gds.noselect.2 <- tempfile()32: glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2)test_that("cross-sectional id le 400 binomial", { plinkfiles <- strsplit(system.file("extdata", "geno.bed", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") obj1.outfile.gds.select.2 <- tempfile() gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") header = TRUE, as.is = TRUE) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") expect_equal(obj1.gds.select.1, obj1.gds.select.2) data(example) } obj1.outfile.txt.select.2 <- tempfile() suppressWarnings(RNGversion("3.5.0")) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, set.seed(123) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, pheno <- rbind(example$pheno, example$pheno[1:100, ]) select = select, infile.header.print = c("SNP", "Allele1", pheno$id <- 1:500 "Allele2")) pheno$disease[sample(1:500, 20)] <- NA obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, pheno$age[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$sex[sample(1:500, 20)] <- NA expect_equal(obj1.txt.select.1, obj1.txt.select.2) pheno <- pheno[sample(1:500, 450), ] obj1.outfile.txt1.select.2 <- tempfile() pheno <- pheno[pheno$id <= 400, ] glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, kins <- example$GRM infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, "Allele2")) id = "id", family = binomial(link = "logit"), method = "REML", obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) select <- match(1:400, unique(obj1$id_include)) obj1.outfile.txt2.select.2 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, 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) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, 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(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") id = "id", family = binomial(link = "logit"), method = "REML", unlink(obj1.outfile.bed.noselect.1.tmp) method.optim = "AI") obj1.outfile.bed.select.1 <- tempfile() select <- match(1:400, unique(obj2$id_include)) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) select[is.na(select)] <- 0 obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, obj2.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, obj1.outfile.bgen.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) outfile = obj1.outfile.bgen.noselect.1) obj2.outfile.bed.select.2 <- tempfile() obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) header = TRUE, as.is = TRUE) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj1.outfile.bgen.noselect.1.tmp <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.bed.select.1, obj2.bed.select.2) outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj2.outfile.bgen.noselect.2 <- tempfile() obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj2.outfile.bgen.noselect.2) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, unlink(obj1.outfile.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.1 <- tempfile() expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.bgen.select.2 <- tempfile() select = select, outfile = obj1.outfile.bgen.select.1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, select = select, outfile = obj2.outfile.bgen.select.2) header = TRUE, as.is = TRUE) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", quietly = TRUE)) { "VAR", "PVAL")]) obj2.outfile.gds.noselect.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) quietly = TRUE)) { obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, obj1.outfile.gds.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) obj2.outfile.gds.select.2 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, } ncores = 2) obj2.outfile.txt.select.2 <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = txtfile, outfile = obj2.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.gds.noselect.1, obj1.gds.noselect.1.tmp) "Allele2")) unlink(obj1.outfile.gds.noselect.1.tmp) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, obj1.outfile.gds.select.1 <- tempfile() 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(obj2.txt.select.1, obj2.txt.select.2) header = TRUE, as.is = TRUE) obj2.outfile.txt1.select.2 <- tempfile() expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, select = select, infile.header.print = c("SNP", "Allele1", 0.986534857))) "Allele2")) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, } header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1 <- tempfile() expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, obj2.outfile.txt2.select.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.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.txt.select.1 <- read.table(obj1.outfile.txt.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) header = TRUE, as.is = TRUE) obj1.outfile.txt.select.1.tmp <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, idx <- sample(nrow(kins)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, kins <- kins[idx, idx] select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, unlink(obj1.outfile.txt.select.1.tmp) id = "id", family = binomial(link = "logit"), method = "REML", obj1.outfile.txt1.select.1 <- tempfile() method.optim = "AI") glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, select <- match(1:400, unique(obj1$id_include)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select[is.na(select)] <- 0 select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.bed.noselect.3 <- tempfile() "Allele2")) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.txt2.select.1 <- tempfile() obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, expect_equal(obj1.txt.select.1, obj1.txt2.select.1) header = TRUE, as.is = TRUE) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.bgen.select.3 <- tempfile() obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt2.select.1)) select = select, outfile = obj1.outfile.bgen.select.3) skip_on_cran() obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) method.optim = "AI") if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { select <- match(1:400, unique(obj2$id_include)) obj1.outfile.gds.noselect.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj2.outfile.bed.noselect.1 <- tempfile() obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) header = TRUE, as.is = TRUE) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) header = TRUE, as.is = TRUE) obj1.outfile.gds.select.3 <- tempfile() obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) } obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, outfile = obj2.outfile.bgen.noselect.1) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, 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, obj2.outfile.bgen.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt.select.1, obj1.txt.select.3) select = select, outfile = obj2.outfile.bgen.select.1) obj1.outfile.txt1.select.3 <- tempfile() obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, header = TRUE, as.is = TRUE) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) "VAR", "PVAL")]) obj1.outfile.txt2.select.3 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, quietly = TRUE)) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, "Allele2")) header = TRUE, as.is = TRUE) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, obj2.outfile.gds.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) select <- match(1:400, unique(obj2$id_include)) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.txt.select.1 <- tempfile() obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, 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$PVAL, obj2.txt.select.1$PVAL) outfile = obj2.outfile.bgen.noselect.3) obj2.outfile.txt1.select.1 <- tempfile() obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, 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.bgen.noselect.1, obj2.bgen.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.bgen.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, select = select, outfile = obj2.outfile.bgen.select.3) header = TRUE, as.is = TRUE) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, expect_equal(obj2.txt.select.1, obj2.txt1.select.1) header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, quietly = TRUE)) { select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.noselect.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.3) idx <- sample(nrow(pheno)) obj2.outfile.gds.select.3 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) method.optim = "AI") expect_equal(obj2.gds.select.1, obj2.gds.select.3) select <- match(1:400, unique(obj1$id_include)) } obj2.outfile.txt.select.3 <- tempfile() select[is.na(select)] <- 0 glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, obj1.outfile.bed.noselect.2 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) select = select, infile.header.print = c("SNP", "Allele1", obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) header = TRUE, as.is = TRUE) obj1.outfile.bed.select.2 <- tempfile() expect_equal(obj2.txt.select.1, obj2.txt.select.3) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, obj2.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2")) outfile = obj1.outfile.bgen.noselect.2) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj2.outfile.txt2.select.3 <- tempfile() obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) obj1.outfile.gds.noselect.2 <- tempfile() unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj2.outfile.txt2.select.1)) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.gds.select.2 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, obj1.outfile.txt2.select.2)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, } obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj1.outfile.txt.select.2 <- tempfile() obj2.outfile.txt2.select.2)) glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, "Allele2")) obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, 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.txt.select.1, obj1.txt.select.2) obj2.outfile.txt2.select.3)) obj1.outfile.txt1.select.2 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, quietly = TRUE)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, select = select, infile.header.print = c("SNP", "Allele1", obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, "Allele2")) obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) header = TRUE, as.is = TRUE)}) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2)
obj1.outfile.txt2.select.2 <- tempfile()33: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, eval(code, test_env) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 34: "Allele2"))eval(code, test_env) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2,
header = TRUE, as.is = TRUE)35: expect_equal(obj1.txt2.select.1, obj1.txt2.select.2)withCallingHandlers({ obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations id = "id", family = binomial(link = "logit"), method = "REML", if (snapshot_skipped) { method.optim = "AI") skip("On CRAN") select <- match(1:400, unique(obj2$id_include)) } select[is.na(select)] <- 0 else if (!new_expectations && skip_on_empty) { obj2.outfile.bed.noselect.2 <- tempfile() skip_empty() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) } obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) skip(paste0("{", e$package, "} is not installed.")) } obj2.outfile.bed.select.2 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) snapshot_skipped <<- TRUE obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt) expect_equal(obj2.bed.select.1, obj2.bed.select.2)
obj2.outfile.bgen.noselect.2 <- tempfile()36: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, doTryCatch(return(expr), name, parentenv, handler) outfile = obj2.outfile.bgen.noselect.2)
obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, 37: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2)
obj2.outfile.bgen.select.2 <- tempfile()38: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, tryCatchList(expr, classes, parentenv, handlers) select = select, outfile = obj2.outfile.bgen.select.2)
obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, 39: header = TRUE, as.is = TRUE)tryCatch(withCallingHandlers({ expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) eval(code, test_env) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", new_expectations <- the$test_expectations > starting_expectations quietly = TRUE)) { if (snapshot_skipped) { skip("On CRAN") obj2.outfile.gds.noselect.2 <- tempfile() } glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, else if (!new_expectations && skip_on_empty) { header = TRUE, as.is = TRUE) skip_empty() expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) } obj2.outfile.gds.select.2 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) if (on_cran()) { obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.gds.select.1, obj2.gds.select.2) }}, snapshot_on_cran = function(cnd) { obj2.outfile.txt.select.2 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, invokeRestart("muffle_cran_snapshot")}, 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", 40: "Allele2"))doWithOneRestart(return(expr), restart) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2,
header = TRUE, as.is = TRUE)41: withOneRestart(expr, restarts[[1L]]) expect_equal(obj2.txt.select.1, obj2.txt.select.2)
obj2.outfile.txt1.select.2 <- tempfile()42: glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, withRestarts(tryCatch(withCallingHandlers({ infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, eval(code, test_env) select = select, infile.header.print = c("SNP", "Allele1", new_expectations <- the$test_expectations > starting_expectations "Allele2")) if (snapshot_skipped) { obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, skip("On CRAN") header = TRUE, as.is = TRUE) } expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) else if (!new_expectations && skip_on_empty) { obj2.outfile.txt2.select.2 <- tempfile() skip_empty() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }, expectation = handle_expectation, packageNotFoundError = function(e) { select = select, infile.header.print = c("SNP", "Allele1", if (on_cran()) { "Allele2")) skip(paste0("{", e$package, "} is not installed.")) obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) } expect_equal(obj2.txt2.select.1, obj2.txt2.select.2)}, snapshot_on_cran = function(cnd) { idx <- sample(nrow(kins)) snapshot_skipped <<- TRUE kins <- kins[idx, idx] invokeRestart("muffle_cran_snapshot") obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, }, skip = handle_skip, warning = handle_warning, message = handle_message, id = "id", family = binomial(link = "logit"), method = "REML", error = handle_error, interrupt = handle_interrupt), error = handle_fatal), method.optim = "AI") end_test = function() { select <- match(1:400, unique(obj1$id_include)) }) select[is.na(select)] <- 0
obj1.outfile.bed.noselect.3 <- tempfile()43: glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3)test_code(code = exprs, env = env, reporter = get_reporter() %||% obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, StopReporter$new()) header = TRUE, as.is = TRUE)
expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3)44: obj1.outfile.bed.select.3 <- tempfile()source_file(path, env = env(env), desc = desc, shuffle = shuffle, glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) error_call = error_call) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3,
header = TRUE, as.is = TRUE)45: expect_equal(obj1.bed.select.1, obj1.bed.select.3)FUN(X[[i]], ...) obj1.outfile.bgen.noselect.3 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 46: outfile = obj1.outfile.bgen.noselect.3)lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) error_call = error_call) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)
obj1.outfile.bgen.select.3 <- tempfile()47: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, doTryCatch(return(expr), name, parentenv, handler) select = select, outfile = obj1.outfile.bgen.select.3)
obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, 48: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3)
49: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", tryCatchList(expr, classes, parentenv, handlers) quietly = TRUE)) {
obj1.outfile.gds.noselect.3 <- tempfile()50: glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3)tryCatch(code, testthat_abort_reporter = function(cnd) { obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, cat(conditionMessage(cnd), "\n") header = TRUE, as.is = TRUE) NULL expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)}) obj1.outfile.gds.select.3 <- tempfile()
glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3)51: obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, with_reporter(reporters$multi, lapply(test_paths, test_one_file, header = TRUE, as.is = TRUE) env = env, desc = desc, shuffle = shuffle, error_call = error_call)) expect_equal(obj1.gds.select.1, obj1.gds.select.3)
52: }test_files_serial(test_dir = test_dir, test_package = test_package, obj1.outfile.txt.select.3 <- tempfile() test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, desc = desc, load_package = load_package, shuffle = shuffle, select = select, infile.header.print = c("SNP", "Allele1", error_call = error_call) "Allele2"))
obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, 53: header = TRUE, as.is = TRUE)test_files(test_dir = path, test_paths = test_paths, test_package = package, expect_equal(obj1.txt.select.1, obj1.txt.select.3) reporter = reporter, load_helpers = load_helpers, env = env, obj1.outfile.txt1.select.3 <- tempfile() stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 54: select = select, infile.header.print = c("SNP", "Allele1", test_dir("testthat", package = package, reporter = reporter, "Allele2")) ..., load_package = "installed") obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3,
55: header = TRUE, as.is = TRUE)test_check("GMMAT") expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)
obj1.outfile.txt2.select.3 <- tempfile()An irrecoverable exception occurred. R is aborting now ...
glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.3) obj2.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, 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) obj2.outfile.bgen.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.3) obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj2.outfile.gds.noselect.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) 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() 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.select.1, obj2.gds.select.3) } obj2.outfile.txt.select.3 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = 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", "Allele2")) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) obj2.outfile.txt1.select.3 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj2.outfile.txt2.select.3 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj2.outfile.txt.select.1, obj2.outfile.txt1.select.1, obj2.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2)) unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.3)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
33: eval(code, test_env)
34: eval(code, test_env)
35: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test_glmm.score-37.R
The following SNPs have been removed due to inconsistent alleles across studies:
[1] "L10" "L12" "L15"
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
══ Skipped tests (30) ══════════════════════════════════════════════════════════
• On CRAN (28): 'test_SMMAT.R:56:2', 'test_SMMAT.R:103:2',
'test_SMMAT.R:149:2', 'test_SMMAT.R:196:2', 'test_SMMAT.R:236:2',
'test_SMMAT.R:276:2', 'test_SMMAT.meta.R:45:2', 'test_SMMAT.meta.R:77:2',
'test_SMMAT.meta.R:108:2', 'test_SMMAT.meta.R:140:2',
'test_SMMAT.meta.R:165:2', 'test_glmm.score.R:317:2',
'test_glmm.score.R:616:2', 'test_glmm.score.R:914:2',
'test_glmm.score.R:1213:2', 'test_glmm.score.R:1505:2',
'test_glmm.score.R:1797:2', 'test_glmm.wald.R:2:2', 'test_glmm.wald.R:805:2',
'test_glmm.wald.R:1609:2', 'test_glmm.wald.R:1761:2', 'test_glmmkin.R:2:2',
'test_glmmkin.R:82:2', 'test_glmmkin.R:163:2', 'test_glmmkin.R:245:2',
'test_glmmkin.R:328:2', 'test_glmmkin.R:362:2', 'test_glmmkin.R:396:2'
• {SeqArray} is not installed (2): 'test_SMMAT.R:2:9', 'test_SMMAT.meta.R:2:2'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_glmm.score.R:37:2'): cross-sectional id le 400 binomial ────────
Error in `file(outfile, "w")`: cannot open the connection
Backtrace:
▆
1. └─GMMAT::glmm.score(...) at test_glmm.score.R:37:9
2. └─base::file(outfile, "w")
[ FAIL 1 | WARN 2 | SKIP 30 | PASS 3 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64