Last updated on 2026-11-13 17:51:48 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 43.58 | 240.25 | 283.83 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 37.72 | 233.26 | 270.98 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 30.00 | 158.84 | 188.84 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 165.65 | 208.65 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 72.00 | 391.00 | 463.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 53.75 | 246.68 | 300.43 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 49.06 | 229.50 | 278.56 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 68.00 | 314.00 | 382.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | ERROR | ||||
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 86.00 | 391.00 | 477.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [2s/2s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(GMMAT)
> Sys.setenv(MKL_NUM_THREADS = 1)
>
> test_check("GMMAT")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: e$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$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$GRM
Traceback:
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 1: id = "id", family = binomial(link = "logit"), method = "REML", eval(c.expr, envir = args, enclos = envir) method.optim = "AI")
select <- match(1:400, unique(obj1$id_include)) 2: select[is.na(select)] <- 0eval(c.expr, envir = args, enclos = envir) obj1.outfile.bed.noselect.1 <- tempfile()
glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) 3: obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
obj1.outfile.bed.noselect.1.tmp <- tempfile() 4: expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, tryCatchOne(expr, names, parentenv, handlers[[1L]]) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.")
unlink(obj1.outfile.bed.noselect.1.tmp) 5: obj1.outfile.bed.select.1 <- tempfile()tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1)
obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, 6: header = TRUE, as.is = TRUE)tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1)
obj1.outfile.bgen.noselect.1 <- tempfile() 7: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, FUN(X[[i]], ...) outfile = obj1.outfile.bgen.noselect.1)
obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, 8: header = TRUE, as.is = TRUE)lapply(X = S, FUN = FUN, ...) obj1.outfile.bgen.noselect.1.tmp <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 9: outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)doTryCatch(return(expr), name, parentenv, handler) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp,
header = TRUE, as.is = TRUE)10: expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp)tryCatchOne(expr, names, parentenv, handlers[[1L]]) unlink(obj1.outfile.bgen.noselect.1.tmp)
obj1.outfile.bgen.select.1 <- tempfile()11: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, tryCatchList(expr, classes, parentenv, handlers) select = select, outfile = obj1.outfile.bgen.select.1)
obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, 12: header = TRUE, as.is = TRUE)tryCatch(expr, error = function(e) { expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) call <- conditionCall(e) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, if (!is.null(call)) { c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (identical(call[[1L]], quote(doTryCatch))) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", call <- sys.call(-4L) quietly = TRUE)) { dcall <- deparse(call, nlines = 1L) obj1.outfile.gds.noselect.1 <- tempfile() prefix <- paste("Error in", dcall, ": ") glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) LONG <- 75L obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) sm <- strsplit(conditionMessage(e), "\n")[[1L]] obj1.outfile.gds.noselect.1.tmp <- tempfile() w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, if (is.na(w)) ncores = 2) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, type = "b") if (w > LONG) header = TRUE, as.is = TRUE) prefix <- paste0(prefix, "\n ") expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) } unlink(obj1.outfile.gds.noselect.1.tmp) else prefix <- "Error : " obj1.outfile.gds.select.1 <- tempfile() msg <- paste0(prefix, conditionMessage(e), "\n") glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) .Internal(seterrmessage(msg[1L])) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, if (!silent && isTRUE(getOption("show.error.messages"))) { header = TRUE, as.is = TRUE) cat(msg, file = outFile) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) .Internal(printDeferredWarnings()) } expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) invisible(structure(msg, class = "try-error", condition = e)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, }) 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1))
}13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE) obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 14: select = select, infile.header.print = c("SNP", "Allele1", sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) "Allele2"))
obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE)15: FUN(X[[i]], ...) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.outfile.txt.select.1.tmp <- tempfile()
expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, 16: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, lapply(seq_len(cores), inner.do) select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.")
17: unlink(obj1.outfile.txt.select.1.tmp)mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, obj1.outfile.txt1.select.1 <- tempfile() mc.silent = silent, mc.cores = cores) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 18: select = select, infile.header.print = c("SNP", "Allele1", e$fun(obj, substitute(ex), parent.frame(), e$data) "Allele2"))
obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, 19: header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1)foreach(i = 1:ncores) %dopar% { obj1.outfile.txt2.select.1 <- tempfile() if (!is.null(obj$P)) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13, as.numeric(res), obj$P, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, infile, paste0(outfile, "_tmp.", i), center2, select = select, infile.header.print = c("SNP", "Allele1", MAF.range[1], MAF.range[2], miss.cutoff, miss.method, "Allele2")) nperbatch, select, threadInfo$begin[i], threadInfo$end[i], obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, header = TRUE, as.is = TRUE) 1) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) } unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, else { obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, .Call(C_glmm_score_bgen11, as.numeric(res), obj$P, obj1.outfile.txt2.select.1)) infile, paste0(outfile, "_tmp.", i), center2, skip_on_cran() MAF.range[1], MAF.range[2], miss.cutoff, miss.method, obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], id = "id", family = binomial(link = "logit"), method = "REML", threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, method.optim = "AI") 1) select <- match(1:400, unique(obj2$id_include)) } select[is.na(select)] <- 0 } obj2.outfile.bed.noselect.1 <- tempfile() else { if (bgenInfo$LayoutFlag == 2) { glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj$Sigma_iX, obj$cov, infile, paste0(outfile, header = TRUE, as.is = TRUE) "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj2.outfile.bed.select.1 <- tempfile() miss.cutoff, miss.method, nperbatch, select, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, bgenInfo$N, bgenInfo$CompressionFlag, 1) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) else { obj2.outfile.bgen.noselect.1 <- tempfile() .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj$Sigma_iX, obj$cov, infile, paste0(outfile, outfile = obj2.outfile.bgen.noselect.1) "_tmp.", i), center2, MAF.range[1], MAF.range[2], obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, miss.cutoff, miss.method, nperbatch, select, header = TRUE, as.is = TRUE) threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], obj2.outfile.bgen.select.1 <- tempfile() bgenInfo$N, bgenInfo$CompressionFlag, 1) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj2.outfile.bgen.select.1) } obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, } header = TRUE, as.is = TRUE)
expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1)20: expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE",
"VAR", "PVAL")])21: if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) {eval(code, test_env) obj2.outfile.gds.noselect.1 <- tempfile()
glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1)22: obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE)eval(code, test_env) obj2.outfile.gds.select.1 <- tempfile()
glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1)23: obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE)withCallingHandlers({ expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) eval(code, test_env) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, skip("On CRAN") 0.996996766))) } } else if (!new_expectations && skip_on_empty) { obj2.outfile.txt.select.1 <- tempfile() skip_empty() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", }, expectation = handle_expectation, packageNotFoundError = function(e) { "Allele2")) if (on_cran()) { obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL)}, snapshot_on_cran = function(cnd) { obj2.outfile.txt1.select.1 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, invokeRestart("muffle_cran_snapshot") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", }, skip = handle_skip, warning = handle_warning, message = handle_message, "Allele2")) error = handle_error, interrupt = handle_interrupt) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1,
header = TRUE, as.is = TRUE)24: expect_equal(obj2.txt.select.1, obj2.txt1.select.1)doTryCatch(return(expr), name, parentenv, handler) obj2.outfile.txt2.select.1 <- tempfile()
glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, 25: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, tryCatchOne(expr, names, parentenv, handlers[[1L]]) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))26: obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, tryCatchList(expr, classes, parentenv, handlers) header = TRUE, as.is = TRUE)
27: expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno))tryCatch(withCallingHandlers({ pheno <- pheno[idx, ] eval(code, test_env) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, new_expectations <- the$test_expectations > starting_expectations id = "id", family = binomial(link = "logit"), method = "REML", if (snapshot_skipped) { method.optim = "AI") skip("On CRAN") select <- match(1:400, unique(obj1$id_include)) } else if (!new_expectations && skip_on_empty) { select[is.na(select)] <- 0 skip_empty() obj1.outfile.bed.noselect.2 <- tempfile() } glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2)}, expectation = handle_expectation, packageNotFoundError = function(e) { obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, if (on_cran()) { header = TRUE, as.is = TRUE) skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) } obj1.outfile.bed.select.2 <- tempfile()}, snapshot_on_cran = function(cnd) { glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) snapshot_skipped <<- TRUE obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, header = TRUE, as.is = TRUE) error = handle_error, interrupt = handle_interrupt), error = handle_fatal) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile()
glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 28: outfile = obj1.outfile.bgen.noselect.2)doWithOneRestart(return(expr), restart)
obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, 29: header = TRUE, as.is = TRUE)withOneRestart(expr, restarts[[1L]]) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2)
obj1.outfile.bgen.select.2 <- tempfile()30: withRestarts(tryCatch(withCallingHandlers({ glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, eval(code, test_env) select = select, outfile = obj1.outfile.bgen.select.2) new_expectations <- the$test_expectations > starting_expectations obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) } if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", else if (!new_expectations && skip_on_empty) { quietly = TRUE)) { skip_empty() } obj1.outfile.gds.noselect.2 <- tempfile()}, expectation = handle_expectation, packageNotFoundError = function(e) { glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) if (on_cran()) { obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, skip(paste0("{", e$package, "} is not installed.")) header = TRUE, as.is = TRUE) } expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2)}, snapshot_on_cran = function(cnd) { obj1.outfile.gds.select.2 <- tempfile() snapshot_skipped <<- TRUE glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) invokeRestart("muffle_cran_snapshot") obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, }, 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(obj1.gds.select.1, obj1.gds.select.2) end_test = function() { } }) obj1.outfile.txt.select.2 <- tempfile()
glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, 31: infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, test_code(code, parent.frame()) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))32: obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, test_that("cross-sectional id le 400 binomial", { header = TRUE, as.is = TRUE) plinkfiles <- strsplit(system.file("extdata", "geno.bed", expect_equal(obj1.txt.select.1, obj1.txt.select.2) package = "GMMAT"), ".bed", fixed = TRUE)[[1]] obj1.outfile.txt1.select.2 <- tempfile() bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") obj1.outfile.txt2.select.2 <- tempfile() data(example) glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, suppressWarnings(RNGversion("3.5.0")) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, set.seed(123) select = select, infile.header.print = c("SNP", "Allele1", pheno <- rbind(example$pheno, example$pheno[1:100, ]) "Allele2")) pheno$id <- 1:500 obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, pheno$disease[sample(1:500, 20)] <- NA header = TRUE, as.is = TRUE) pheno$age[sample(1:500, 20)] <- NA expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) pheno$sex[sample(1:500, 20)] <- NA obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, pheno <- pheno[sample(1:500, 450), ] id = "id", family = binomial(link = "logit"), method = "REML", pheno <- pheno[pheno$id <= 400, ] method.optim = "AI") kins <- example$GRM select <- match(1:400, unique(obj2$id_include)) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select[is.na(select)] <- 0 id = "id", family = binomial(link = "logit"), method = "REML", obj2.outfile.bed.noselect.2 <- tempfile() method.optim = "AI") glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) select <- match(1:400, unique(obj1$id_include)) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, select[is.na(select)] <- 0 header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj2.outfile.bed.select.2 <- tempfile() obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.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.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.bed.select.1, obj2.bed.select.2) ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") obj2.outfile.bgen.noselect.2 <- tempfile() unlink(obj1.outfile.bed.noselect.1.tmp) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.bed.select.1 <- tempfile() outfile = obj2.outfile.bgen.noselect.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, obj2.outfile.bgen.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) select = select, outfile = obj2.outfile.bgen.select.2) obj1.outfile.bgen.noselect.1 <- tempfile() obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, header = TRUE, as.is = TRUE) outfile = obj1.outfile.bgen.noselect.1) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", header = TRUE, as.is = TRUE) quietly = TRUE)) { obj2.outfile.gds.noselect.2 <- tempfile() obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) header = TRUE, as.is = TRUE) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) 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(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, unlink(obj1.outfile.bgen.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.bgen.select.1 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.2) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) } obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, obj2.outfile.txt.select.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "Allele2")) "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", header = TRUE, as.is = TRUE) "VAR", "PVAL")]) expect_equal(obj2.txt.select.1, obj2.txt.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt1.select.2 <- tempfile() quietly = TRUE)) { glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, obj1.outfile.gds.noselect.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) "Allele2")) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) ncores = 2) obj2.outfile.txt2.select.2 <- tempfile() obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, header = TRUE, as.is = TRUE) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) select = select, infile.header.print = c("SNP", "Allele1", unlink(obj1.outfile.gds.noselect.1.tmp) "Allele2")) obj1.outfile.gds.select.1 <- tempfile() obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) header = TRUE, as.is = TRUE) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, expect_equal(obj2.txt2.select.1, obj2.txt2.select.2) header = TRUE, as.is = TRUE) idx <- sample(nrow(kins)) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) kins <- kins[idx, idx] expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, id = "id", family = binomial(link = "logit"), method = "REML", 0.986534857))) method.optim = "AI") unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) select <- match(1:400, unique(obj1$id_include)) } select[is.na(select)] <- 0 obj1.outfile.txt.select.1 <- tempfile() obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, select = select, infile.header.print = c("SNP", "Allele1", header = TRUE, as.is = TRUE) "Allele2")) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, obj1.outfile.bed.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, obj1.outfile.txt.select.1.tmp <- tempfile() header = TRUE, as.is = TRUE) expect_error(glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1.tmp, expect_equal(obj1.bed.select.1, obj1.bed.select.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") outfile = obj1.outfile.bgen.noselect.3) unlink(obj1.outfile.txt.select.1.tmp) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, obj1.outfile.txt1.select.1 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, infile.header.print = c("SNP", "Allele1", select = select, outfile = obj1.outfile.bgen.select.3) "Allele2")) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) obj1.outfile.txt2.select.1 <- tempfile() if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.gds.noselect.3 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) "Allele2")) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.gds.select.3 <- tempfile() obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1)) expect_equal(obj1.gds.select.1, obj1.gds.select.3) skip_on_cran() } obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, obj1.outfile.txt.select.3 <- tempfile() id = "id", family = binomial(link = "logit"), method = "REML", glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, method.optim = "AI") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select <- match(1:400, unique(obj2$id_include)) select = select, infile.header.print = c("SNP", "Allele1", select[is.na(select)] <- 0 "Allele2")) obj2.outfile.bed.noselect.1 <- tempfile() obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, obj1.outfile.txt1.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, obj2.outfile.bed.select.1 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) select = select, infile.header.print = c("SNP", "Allele1", obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, "Allele2")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj2.outfile.bgen.noselect.1 <- tempfile() obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.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")) obj2.outfile.bgen.select.1 <- tempfile() obj1.txt2.select.3 <- read.table(obj1.outfile.txt2.select.3, glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj2.outfile.bgen.select.1) header = TRUE, as.is = TRUE) obj2.bgen.select.1 <- read.table(obj2.outfile.bgen.select.1, expect_equal(obj1.txt2.select.1, obj1.txt2.select.3) header = TRUE, as.is = TRUE) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj2.bgen.noselect.1, obj2.bgen.select.1) id = "id", family = binomial(link = "logit"), method = "REML", expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", method.optim = "AI") "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", select <- match(1:400, unique(obj2$id_include)) "VAR", "PVAL")]) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.3 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.bed.noselect.3 <- read.table(obj2.outfile.bed.noselect.3, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.1 <- tempfile() expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.3) obj2.outfile.bed.select.3 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.3) header = TRUE, as.is = TRUE) obj2.bed.select.3 <- read.table(obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() expect_equal(obj2.bed.select.1, obj2.bed.select.3) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, obj2.outfile.bgen.noselect.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) outfile = obj2.outfile.bgen.noselect.3) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) obj2.bgen.noselect.3 <- read.table(obj2.outfile.bgen.noselect.3, expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, header = TRUE, as.is = TRUE) 0.996996766))) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.3) obj2.outfile.bgen.select.3 <- tempfile() } glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, obj2.outfile.txt.select.1 <- tempfile() select = select, outfile = obj2.outfile.bgen.select.3) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, obj2.bgen.select.3 <- read.table(obj2.outfile.bgen.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) select = select, infile.header.print = c("SNP", "Allele1", expect_equal(obj2.bgen.select.1, obj2.bgen.select.3) "Allele2")) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, quietly = TRUE)) { header = TRUE, as.is = TRUE) obj2.outfile.gds.noselect.3 <- tempfile() expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.3) obj2.outfile.txt1.select.1 <- tempfile() obj2.gds.noselect.3 <- read.table(obj2.outfile.gds.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.gds.noselect.1, obj2.gds.noselect.3) select = select, infile.header.print = c("SNP", "Allele1", obj2.outfile.gds.select.3 <- tempfile() "Allele2")) glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.3) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, obj2.gds.select.3 <- read.table(obj2.outfile.gds.select.3, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() expect_equal(obj2.gds.select.1, obj2.gds.select.3) } glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, obj2.outfile.txt.select.3 <- tempfile() infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.3, 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.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt.select.3 <- read.table(obj2.outfile.txt.select.3, expect_equal(obj2.txt.select.1, obj2.txt2.select.1) header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.3) idx <- sample(nrow(pheno)) obj2.outfile.txt1.select.3 <- tempfile() pheno <- pheno[idx, ] glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.3, obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select = select, infile.header.print = c("SNP", "Allele1", select <- match(1:400, unique(obj1$id_include)) "Allele2")) select[is.na(select)] <- 0 obj2.txt1.select.3 <- read.table(obj2.outfile.txt1.select.3, obj1.outfile.bed.noselect.2 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj2.txt1.select.1, obj2.txt1.select.3) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, obj2.outfile.txt2.select.3 <- tempfile() header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.3, expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.outfile.bed.select.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, "Allele2")) header = TRUE, as.is = TRUE) obj2.txt2.select.3 <- read.table(obj2.outfile.txt2.select.3, expect_equal(obj1.bed.select.1, obj1.bed.select.2) header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.2 <- tempfile() expect_equal(obj2.txt2.select.1, obj2.txt2.select.3) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, unlink(c(obj2.outfile.bed.noselect.1, obj2.outfile.bed.select.1, outfile = obj1.outfile.bgen.noselect.2) obj2.outfile.bgen.noselect.1, obj2.outfile.bgen.select.1, obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.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.bgen.noselect.1, obj1.bgen.noselect.2) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.bgen.select.2 <- tempfile() obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, select = select, outfile = obj1.outfile.bgen.select.2) obj1.outfile.txt2.select.2)) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj2.outfile.txt2.select.2)) quietly = TRUE)) { unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.gds.noselect.2 <- tempfile() obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.outfile.txt2.select.3)) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, header = TRUE, as.is = TRUE) obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj1.outfile.gds.select.2 <- tempfile() obj2.outfile.txt2.select.3)) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, quietly = TRUE)) header = TRUE, as.is = TRUE) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, expect_equal(obj1.gds.select.1, obj1.gds.select.2) obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, } obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.txt.select.2 <- tempfile() obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3)) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, }) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))33: obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, eval(code, test_env) header = TRUE, as.is = TRUE)
expect_equal(obj1.txt.select.1, obj1.txt.select.2)34: obj1.outfile.txt1.select.2 <- tempfile()eval(code, test_env) glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2,
infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", 35: "Allele2"))withCallingHandlers({ obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) if (snapshot_skipped) { obj1.outfile.txt2.select.2 <- tempfile() skip("On CRAN") glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, } infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { skip_empty() "Allele2")) } obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) } obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, }, snapshot_on_cran = function(cnd) { id = "id", family = binomial(link = "logit"), method = "REML", snapshot_skipped <<- TRUE method.optim = "AI") invokeRestart("muffle_cran_snapshot") select <- match(1:400, unique(obj2$id_include))}, skip = handle_skip, warning = handle_warning, message = handle_message, select[is.na(select)] <- 0 error = handle_error, interrupt = handle_interrupt) obj2.outfile.bed.noselect.2 <- tempfile()
glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2)36: obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, doTryCatch(return(expr), name, parentenv, handler) header = TRUE, as.is = TRUE)
expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2)37: obj2.outfile.bed.select.2 <- tempfile()tryCatchOne(expr, names, parentenv, handlers[[1L]]) glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2)
obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, 38: header = TRUE, as.is = TRUE)tryCatchList(expr, classes, parentenv, handlers) expect_equal(obj2.bed.select.1, obj2.bed.select.2)
obj2.outfile.bgen.noselect.2 <- tempfile()39: glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, tryCatch(withCallingHandlers({ eval(code, test_env) outfile = obj2.outfile.bgen.noselect.2) new_expectations <- the$test_expectations > starting_expectations obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, if (snapshot_skipped) { header = TRUE, as.is = TRUE) skip("On CRAN") expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) } obj2.outfile.bgen.select.2 <- tempfile() else if (!new_expectations && skip_on_empty) { skip_empty() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, } select = select, outfile = obj2.outfile.bgen.select.2) obj2.bgen.select.2 <- read.table(obj2.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.select.1, obj2.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }, expectation = handle_expectation, packageNotFoundError = function(e) { quietly = TRUE)) { if (on_cran()) { obj2.outfile.gds.noselect.2 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.2) skip(paste0("{", e$package, "} is not installed.")) obj2.gds.noselect.2 <- read.table(obj2.outfile.gds.noselect.2, } header = TRUE, as.is = TRUE)}, snapshot_on_cran = function(cnd) { expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) snapshot_skipped <<- TRUE obj2.outfile.gds.select.2 <- tempfile() invokeRestart("muffle_cran_snapshot") glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.2) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal) header = TRUE, as.is = TRUE)
expect_equal(obj2.gds.select.1, obj2.gds.select.2)40: }doWithOneRestart(return(expr), restart) obj2.outfile.txt.select.2 <- tempfile()
glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, 41: withOneRestart(expr, restarts[[1L]]) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", 42: "Allele2"))withRestarts(tryCatch(withCallingHandlers({ obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, eval(code, test_env) header = TRUE, as.is = TRUE) new_expectations <- the$test_expectations > starting_expectations expect_equal(obj2.txt.select.1, obj2.txt.select.2) if (snapshot_skipped) { obj2.outfile.txt1.select.2 <- tempfile() skip("On CRAN") glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, } select = select, infile.header.print = c("SNP", "Allele1", else if (!new_expectations && skip_on_empty) { "Allele2")) skip_empty() } obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, }, expectation = handle_expectation, packageNotFoundError = function(e) { header = TRUE, as.is = TRUE) if (on_cran()) { expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) skip(paste0("{", e$package, "} is not installed.")) obj2.outfile.txt2.select.2 <- tempfile() } glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, }, snapshot_on_cran = function(cnd) { infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, snapshot_skipped <<- TRUE select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) invokeRestart("muffle_cran_snapshot") obj2.txt2.select.2 <- read.table(obj2.outfile.txt2.select.2, }, 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.txt2.select.1, obj2.txt2.select.2) end_test = function() { idx <- sample(nrow(kins)) }) kins <- kins[idx, idx]
obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, 43: test_code(code = exprs, env = env, reporter = get_reporter() %||% id = "id", family = binomial(link = "logit"), method = "REML", StopReporter$new()) method.optim = "AI")
select <- match(1:400, unique(obj1$id_include))44: select[is.na(select)] <- 0source_file(path, env = env(env), desc = desc, shuffle = shuffle, obj1.outfile.bed.noselect.3 <- tempfile() error_call = error_call) glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3,
header = TRUE, as.is = TRUE)45: expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3)FUN(X[[i]], ...) obj1.outfile.bed.select.3 <- tempfile()
glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3)46: obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, header = TRUE, as.is = TRUE) error_call = error_call) expect_equal(obj1.bed.select.1, obj1.bed.select.3)
47: obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, doTryCatch(return(expr), name, parentenv, handler) outfile = obj1.outfile.bgen.noselect.3)
obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, 48: header = TRUE, as.is = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3)
obj1.outfile.bgen.select.3 <- tempfile()49: tryCatchList(expr, classes, parentenv, handlers) glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile,
select = select, outfile = obj1.outfile.bgen.select.3)50: obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, tryCatch(code, testthat_abort_reporter = function(cnd) { header = TRUE, as.is = TRUE) cat(conditionMessage(cnd), "\n") expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) NULL if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", }) quietly = TRUE)) {
obj1.outfile.gds.noselect.3 <- tempfile()51: glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)) header = TRUE, as.is = TRUE)
expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.3)52: obj1.outfile.gds.select.3 <- tempfile()test_files_serial(test_dir = test_dir, test_package = test_package, glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.3) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, header = TRUE, as.is = TRUE) desc = desc, load_package = load_package, shuffle = shuffle, expect_equal(obj1.gds.select.1, obj1.gds.select.3) error_call = error_call) }
obj1.outfile.txt.select.3 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", 53: "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, 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() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, 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, load_package = load_package, parallel = parallel, shuffle = shuffle) select = select, infile.header.print = c("SNP", "Allele1",
"Allele2"))54: obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, test_dir("testthat", package = package, reporter = reporter, header = TRUE, as.is = TRUE) ..., load_package = "installed") expect_equal(obj1.txt1.select.1, obj1.txt1.select.3)
obj1.outfile.txt2.select.3 <- tempfile()55: glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.3, test_check("GMMAT") infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3,
select = select, infile.header.print = c("SNP", "Allele1", An irrecoverable exception occurred. R is aborting now ...
"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