Last updated on 2025-12-04 07:51:40 CET.
| Package | ERROR | NOTE | OK |
|---|---|---|---|
| GMMAT | 1 | 3 | 9 |
| MAGEE | 3 | 10 |
Current CRAN status: ERROR: 1, NOTE: 3, OK: 9
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [1s/1s]
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)
Traceback:
1: eval(c.expr, envir = args, enclos = envir)
2: eval(c.expr, envir = args, enclos = envir)
3: 7: FUN(X[[i]], ...)
8: lapply(X = S, FUN = FUN, ...)doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 6: tryCatch(eval(c.expr, envir = args, enclos = envir), error = function(e) e)
7: FUN(X[[i]], ...)
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, ": ") 8: lapply(X = S, FUN = FUN, ...)
9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
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], 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))) type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) 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)) 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: 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"))) {sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14: 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$P, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
16: lapply(seq_len(cores), inner.do)
infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2], miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 17: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, mc.cores = cores)
18: 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$end[i], e$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$CompressionFlag, threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, 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) 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], } } else { if (bgenInfo$LayoutFlag == 2) { .Call(C_glmm_score_bgen13_sp, as.numeric(res), obj$Sigma_i, miss.cutoff, miss.method, nperbatch, select, threadInfo$begin[i], threadInfo$end[i], threadInfo$pos[i], bgenInfo$N, bgenInfo$CompressionFlag, 1) } } obj$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) } else { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$Sigma_i, obj$Sigma_iX, obj$cov, infile, paste0(outfile, "_tmp.", i), center2, MAF.range[1], MAF.range[2],
21: eval(code, test_env)
22: miss.cutoff, miss.method, nperbatch, select, threadInfo$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)eval(code, test_env)
23: withCallingHandlers({ eval(code, test_env)
22: eval(code, test_env)
23: new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) {withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$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) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message,
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
tryCatchList(expr, classes, parentenv, handlers)
27: tryCatch(withCallingHandlers({ eval(code, test_env)25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) {
27: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) } error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
28: doWithOneRestart(return(expr), restart)
}, 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)29: withOneRestart(expr, restarts[[1L]])
30: withRestarts(tryCatch(withCallingHandlers({
28: doWithOneRestart(return(expr), restart)
29: eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) {withOneRestart(expr, restarts[[1L]])
30: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$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) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE31: test_code(code, parent.frame())
32: test_that("cross-sectional id le 400 binomial", { 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() { }) 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")
31: test_code(code, parent.frame())
32: 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"))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") 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, 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) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, suppressWarnings(RNGversion("3.5.0")) set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, pheno$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$GRM header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj1$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, if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile() expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) 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.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, obj1.outfile.bgen.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2) obj1.bgen.noselect.1.tmp <- read.table(obj1.outfile.bgen.noselect.1.tmp, header = TRUE, as.is = TRUE) 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$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, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") select = select, outfile = obj1.outfile.bgen.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) obj1.outfile.txt2.select.1 <- tempfile() 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", "Allele2")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, 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, header = TRUE, as.is = TRUE) 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)) skip_on_cran() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.1 <- tempfile() } obj1.outfile.txt.select.1 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt.select.1 <- read.table(obj1.outfile.txt.select.1, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.bed.select.1$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, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, "Allele2"), ncores = 2), "Error: parallel computing currently not implemented for plain text format genotypes.") unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.1 <- read.table(obj1.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt1.select.1) quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.txt2.select.1 <- tempfile() 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", "Allele2")) obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) skip_on_cran() 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.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.1) obj2.bed.noselect.1 <- read.table(obj2.outfile.bed.noselect.1, expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 0.996996766))) } obj2.outfile.txt.select.1 <- tempfile() header = TRUE, as.is = TRUE) obj2.outfile.bed.select.1 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.1) obj2.bed.select.1 <- read.table(obj2.outfile.bed.select.1, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.select.1) obj2.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.1) obj2.bgen.noselect.1 <- read.table(obj2.outfile.bgen.noselect.1, expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) header = TRUE, as.is = TRUE) obj2.outfile.bgen.select.1 <- tempfile() 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) expect_equal(obj2.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj2.bgen.select.1[, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj2.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", "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, 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$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 0.996996766))) } obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, header = TRUE, as.is = TRUE) select = select, outfile = obj1.outfile.bgen.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.2) obj1.outfile.bgen.select.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.2) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() 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", "Allele2")) obj1.txt2.select.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.2) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() 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.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) obj2.outfile.bgen.select.2 <- tempfile() 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", header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.txt1.select.2 <- tempfile() 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", "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, quietly = TRUE)) { 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, header = TRUE, as.is = TRUE) 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) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, header = TRUE, as.is = TRUE) header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) obj1.outfile.txt2.select.2 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, expect_equal(obj2.gds.select.1, obj2.gds.select.2) } obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, 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.2 <- read.table(obj1.outfile.txt2.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt2.select.1, obj1.txt2.select.2) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt.select.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, outfile = obj2.outfile.bed.noselect.2) obj2.bed.noselect.2 <- read.table(obj2.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.noselect.1, obj2.bed.noselect.2) obj2.outfile.bed.select.2 <- tempfile() glmm.score(obj2, infile = plinkfiles, select = select, outfile = obj2.outfile.bed.select.2) obj2.bed.select.2 <- read.table(obj2.outfile.bed.select.2, obj2.outfile.txt1.select.2 <- tempfile() 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", "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj2, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj2.outfile.bgen.noselect.2) obj2.bgen.noselect.2 <- read.table(obj2.outfile.bgen.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj2.bgen.noselect.1, obj2.bgen.noselect.2) glmm.score(obj2, infile = txtfile2, outfile = obj2.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", "Allele2")) 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) idx <- sample(nrow(kins)) kins <- kins[idx, idx] obj2.outfile.bgen.select.2 <- tempfile() 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", quietly = TRUE)) { 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, 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 header = TRUE, as.is = TRUE) 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) obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, obj1.outfile.bed.noselect.3 <- tempfile() 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) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) header = TRUE, as.is = TRUE) expect_equal(obj2.gds.select.1, obj2.gds.select.2) } obj2.outfile.txt.select.2 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.2, obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.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.2 <- read.table(obj2.outfile.txt.select.2, header = TRUE, as.is = TRUE) obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt.select.2) obj2.outfile.txt1.select.2 <- tempfile() 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", expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, "Allele2")) obj2.txt1.select.2 <- read.table(obj2.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj2.txt1.select.1, obj2.txt1.select.2) obj2.outfile.txt2.select.2 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.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", "Allele2")) 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) header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) idx <- sample(nrow(kins)) kins <- kins[idx, idx] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) 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) select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) obj1.bed.noselect.3 <- read.table(obj1.outfile.bed.noselect.3, obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) } obj1.outfile.txt.select.3 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.3) obj1.outfile.bed.select.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.3) obj1.bed.select.3 <- read.table(obj1.outfile.bed.select.3, glmm.score(obj1, infile = 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", "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.3) obj1.outfile.bgen.noselect.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.3) infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) obj1.bgen.noselect.3 <- read.table(obj1.outfile.bgen.noselect.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.3) obj1.outfile.bgen.select.3 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt2.select.3 <- tempfile() 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", select = select, outfile = obj1.outfile.bgen.select.3) obj1.bgen.select.3 <- read.table(obj1.outfile.bgen.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.select.1, obj1.bgen.select.3) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.3 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.3) obj1.gds.noselect.3 <- read.table(obj1.outfile.gds.noselect.3, header = TRUE, as.is = TRUE) 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) obj1.gds.select.3 <- read.table(obj1.outfile.gds.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.select.1, obj1.gds.select.3) } 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, "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) select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.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, obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) 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) obj1.txt1.select.3 <- read.table(obj1.outfile.txt1.select.3, header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.3) obj1.outfile.txt2.select.3 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.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, 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) 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 <- 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() 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, 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) 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, 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)) { 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, 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) 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.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) 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, 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")) 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)) 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, 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)) 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, })
33: eval(code, test_env)
34: eval(code, test_env)
35: withCallingHandlers({ 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)) eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } unlink(c(obj2.outfile.bed.noselect.2, obj2.outfile.bed.select.2, obj2.outfile.bgen.noselect.2, obj2.outfile.bgen.select.2, obj2.outfile.txt.select.2, obj2.outfile.txt1.select.2, obj2.outfile.txt2.select.2)) unlink(c(obj1.outfile.bed.noselect.3, obj1.outfile.bed.select.3, else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { obj1.outfile.bgen.noselect.3, obj1.outfile.bgen.select.3, obj1.outfile.txt.select.3, obj1.outfile.txt1.select.3, obj1.outfile.txt2.select.3)) unlink(c(obj2.outfile.bed.noselect.3, obj2.outfile.bed.select.3, obj2.outfile.bgen.noselect.3, obj2.outfile.bgen.select.3, obj2.outfile.txt.select.3, obj2.outfile.txt1.select.3, obj2.outfile.txt2.select.3)) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) unlink(c(obj2.outfile.gds.noselect.1, obj2.outfile.gds.select.1, obj1.outfile.gds.noselect.2, obj1.outfile.gds.select.2, obj2.outfile.gds.noselect.2, obj2.outfile.gds.select.2, obj1.outfile.gds.noselect.3, obj1.outfile.gds.select.3, skip(paste0("{", e$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)
obj2.outfile.gds.noselect.3, obj2.outfile.gds.select.3))})
33: 36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)eval(code, test_env)
34: eval(code, test_env)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) {35: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) } error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
}, 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)41: withOneRestart(expr, restarts[[1L]])
42: withRestarts(tryCatch(withCallingHandlers({
36: doTryCatch(return(expr), name, parentenv, handler)
37: eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") }tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { }) else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$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$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty()
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
}}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() {44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
46: })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)45: FUN(X[[i]], ...)
46: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle,
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: error_call = error_call)
47: doTryCatch(return(expr), name, parentenv, handler)
tryCatchList(expr, classes, parentenv, handlers)
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n")48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers) NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,
50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: })
51: with_reporter(reporters$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, 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") 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)
An irrecoverable exception occurred. R is aborting now ...
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-release-macos-arm64
Version: 1.5.0
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'SeqArray', 'SeqVarTools'
Flavor: r-oldrel-macos-arm64
Version: 1.5.0
Check: installed package size
Result: NOTE
installed size is 7.6Mb
sub-directories of 1Mb or more:
libs 5.7Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Version: 1.5.0
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: 'doMC'
Flavor: r-oldrel-windows-x86_64
Current CRAN status: NOTE: 3, OK: 10
Version: 1.4.4
Check: installed package size
Result: NOTE
installed size is 6.6Mb
sub-directories of 1Mb or more:
libs 4.9Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Version: 1.4.4
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: 'doMC'
Flavor: r-oldrel-windows-x86_64
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.