Last updated on 2026-08-01 02:57:14 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | 45.26 | 237.89 | 283.15 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 43.26 | 223.45 | 266.71 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 59.00 | 320.34 | 379.34 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 43.00 | 162.29 | 205.29 | OK | |
| r-devel-windows-x86_64 | 1.5.0 | 70.00 | 316.00 | 386.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 53.40 | 237.31 | 290.71 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 51.78 | 232.71 | 284.49 | OK | |
| r-release-macos-arm64 | 1.5.0 | 12.00 | 90.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.5.0 | 38.00 | 405.00 | 443.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 68.00 | 318.00 | 386.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | 11.00 | 51.00 | 62.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.5.0 | 41.00 | 551.00 | 592.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 90.00 | 399.00 | 489.00 | OK |
Version: 1.5.0
Check: for new files in some other directories
Result: NOTE
Found the following files/directories:
‘~/tmp/scratch/Rtmp0SgXOF’ ‘~/tmp/scratch/Rtmp0rdUZT’
‘~/tmp/scratch/Rtmp163PI0’ ‘~/tmp/scratch/Rtmp20bqUe’
‘~/tmp/scratch/Rtmp2odWq7’ ‘~/tmp/scratch/Rtmp3Qy7Eu’
‘~/tmp/scratch/Rtmp3xEn0r’ ‘~/tmp/scratch/Rtmp4GPEUm’
‘~/tmp/scratch/Rtmp5Ztkok’ ‘~/tmp/scratch/Rtmp62Rk4k’
‘~/tmp/scratch/Rtmp6D72VG’ ‘~/tmp/scratch/Rtmp6JGcX2’
‘~/tmp/scratch/Rtmp6rdfzi’ ‘~/tmp/scratch/Rtmp7UN0Yh’
‘~/tmp/scratch/Rtmp7ld9Ri’ ‘~/tmp/scratch/Rtmp8HE7Vm’
‘~/tmp/scratch/Rtmp8HT0iN’ ‘~/tmp/scratch/Rtmp8svjG4’
‘~/tmp/scratch/Rtmp9GuJKa’ ‘~/tmp/scratch/RtmpAOSSlB’
‘~/tmp/scratch/RtmpAQCcnP’ ‘~/tmp/scratch/RtmpB4uOBP’
‘~/tmp/scratch/RtmpBokFdu’ ‘~/tmp/scratch/RtmpBqXbSU’
‘~/tmp/scratch/RtmpCYLhUE’ ‘~/tmp/scratch/RtmpCg0TbT’
‘~/tmp/scratch/RtmpCvT70a’ ‘~/tmp/scratch/RtmpDXRzUX’
‘~/tmp/scratch/RtmpDcE3RE’ ‘~/tmp/scratch/RtmpDmveMg’
‘~/tmp/scratch/RtmpDxcAxk’ ‘~/tmp/scratch/RtmpE3YEMd’
‘~/tmp/scratch/RtmpEiQiLO’ ‘~/tmp/scratch/RtmpEnaNWG’
‘~/tmp/scratch/RtmpEzT2KW’ ‘~/tmp/scratch/RtmpGKO4pK’
‘~/tmp/scratch/RtmpHeKbBM’ ‘~/tmp/scratch/RtmpHfVkjj’
‘~/tmp/scratch/RtmpHkRLkQ’ ‘~/tmp/scratch/RtmpIAjP1f’
‘~/tmp/scratch/RtmpIOLknt’ ‘~/tmp/scratch/RtmpJGKoGs’
‘~/tmp/scratch/RtmpJYSsl9’ ‘~/tmp/scratch/RtmpJbYRsM’
‘~/tmp/scratch/RtmpKRvufj’ ‘~/tmp/scratch/RtmpKcMOoe’
‘~/tmp/scratch/RtmpKdBVuP’ ‘~/tmp/scratch/RtmpM2PFPe’
‘~/tmp/scratch/RtmpM9nK6W’ ‘~/tmp/scratch/RtmpMLirKe’
‘~/tmp/scratch/RtmpNXbGHV’ ‘~/tmp/scratch/RtmpNpezmA’
‘~/tmp/scratch/RtmpODeR3i’ ‘~/tmp/scratch/RtmpOU4IMG’
‘~/tmp/scratch/RtmpP0jLUw’ ‘~/tmp/scratch/RtmpP2Z8ee’
‘~/tmp/scratch/RtmpPcLoSd’ ‘~/tmp/scratch/RtmpPcxzOX’
‘~/tmp/scratch/RtmpQr9jwV’ ‘~/tmp/scratch/RtmpR2pMpn’
‘~/tmp/scratch/RtmpR6JF3U’ ‘~/tmp/scratch/RtmpR9FX8l’
‘~/tmp/scratch/RtmpRBgpDh’ ‘~/tmp/scratch/RtmpS14h1f’
‘~/tmp/scratch/RtmpSAGPij’ ‘~/tmp/scratch/RtmpSPYHUQ’
‘~/tmp/scratch/RtmpSfLpKA’ ‘~/tmp/scratch/RtmpSvIiAX’
‘~/tmp/scratch/RtmpSzBFM2’ ‘~/tmp/scratch/RtmpUCa55v’
‘~/tmp/scratch/RtmpUclUUk’ ‘~/tmp/scratch/RtmpUxM68G’
‘~/tmp/scratch/RtmpV3YPtL’ ‘~/tmp/scratch/RtmpVHQnC0’
‘~/tmp/scratch/RtmpWWvQq1’ ‘~/tmp/scratch/RtmpX8t0kp’
‘~/tmp/scratch/RtmpXBKY38’ ‘~/tmp/scratch/RtmpXS1L8T’
‘~/tmp/scratch/RtmpXWLe2f’ ‘~/tmp/scratch/RtmpXhOnMe’
‘~/tmp/scratch/RtmpXlyere’ ‘~/tmp/scratch/RtmpXmOecZ’
‘~/tmp/scratch/RtmpYRYKqS’ ‘~/tmp/scratch/RtmpYTvWb8’
‘~/tmp/scratch/RtmpZD0MqV’ ‘~/tmp/scratch/RtmpZDe8af’
‘~/tmp/scratch/Rtmpa6rxQs’ ‘~/tmp/scratch/RtmpaOvTSs’
‘~/tmp/scratch/RtmpaasNAK’ ‘~/tmp/scratch/RtmpaoDS1t’
‘~/tmp/scratch/RtmpazDMjN’ ‘~/tmp/scratch/Rtmpb2fvlo’
‘~/tmp/scratch/RtmpbDEvbk’ ‘~/tmp/scratch/RtmpbMyyPW’
‘~/tmp/scratch/RtmpbXMjU9’ ‘~/tmp/scratch/RtmpbbK0WT’
‘~/tmp/scratch/RtmpcAmQ9d’ ‘~/tmp/scratch/RtmpcFFHdO’
‘~/tmp/scratch/RtmpcMo2b1’ ‘~/tmp/scratch/RtmpcMpNBO’
‘~/tmp/scratch/RtmpceLJJA’ ‘~/tmp/scratch/RtmpdH3GBj’
‘~/tmp/scratch/RtmpdH3yea’ ‘~/tmp/scratch/RtmpdJt69h’
‘~/tmp/scratch/RtmpeyQ6aM’ ‘~/tmp/scratch/RtmpgEsqR0’
‘~/tmp/scratch/RtmpgGLPI2’ ‘~/tmp/scratch/RtmpgJ6CDG’
‘~/tmp/scratch/RtmphkhIGq’ ‘~/tmp/scratch/RtmphkvzhE’
‘~/tmp/scratch/RtmpiIEsKn’ ‘~/tmp/scratch/RtmpiNscKd’
‘~/tmp/scratch/RtmpiOHFlE’ ‘~/tmp/scratch/RtmpjCNMfM’
‘~/tmp/scratch/RtmpjbXcQr’ ‘~/tmp/scratch/RtmpjlfUUu’
‘~/tmp/scratch/RtmpjtGPH2’ ‘~/tmp/scratch/Rtmpk6ofOh’
‘~/tmp/scratch/RtmpkAMWUd’ ‘~/tmp/scratch/RtmpkIVXvp’
‘~/tmp/scratch/RtmpkiluHy’ ‘~/tmp/scratch/Rtmpl7bUWh’
‘~/tmp/scratch/RtmplKKxdH’ ‘~/tmp/scratch/Rtmplg31Vj’
‘~/tmp/scratch/RtmplxeUIP’ ‘~/tmp/scratch/Rtmpm1uBDu’
‘~/tmp/scratch/Rtmpm3zkiF’ ‘~/tmp/scratch/RtmpmZDdo2’
‘~/tmp/scratch/RtmpmdCNcu’ ‘~/tmp/scratch/Rtmpn1ihf7’
‘~/tmp/scratch/RtmpnW2t5v’ ‘~/tmp/scratch/Rtmpo22diY’
‘~/tmp/scratch/Rtmpo80FWA’ ‘~/tmp/scratch/RtmpoXAaMk’
‘~/tmp/scratch/RtmpokYrFL’ ‘~/tmp/scratch/RtmponMRhC’
‘~/tmp/scratch/RtmppIpQBb’ ‘~/tmp/scratch/Rtmpq1szOP’
‘~/tmp/scratch/Rtmpq50zHJ’ ‘~/tmp/scratch/RtmpqBdFHf’
‘~/tmp/scratch/RtmpqMqcNc’ ‘~/tmp/scratch/Rtmpqf9U3y’
‘~/tmp/scratch/RtmpqgaC5i’ ‘~/tmp/scratch/RtmpqpmqHq’
‘~/tmp/scratch/RtmprDr7yZ’ ‘~/tmp/scratch/RtmprGx0bO’
‘~/tmp/scratch/Rtmpsbutif’ ‘~/tmp/scratch/Rtmpscb13K’
‘~/tmp/scratch/RtmpsnoY9t’ ‘~/tmp/scratch/Rtmpsw46fm’
‘~/tmp/scratch/Rtmpt2gfyF’ ‘~/tmp/scratch/RtmptmLl1V’
‘~/tmp/scratch/RtmptqqScV’ ‘~/tmp/scratch/RtmpuUhhci’
‘~/tmp/scratch/Rtmpv4NfPP’ ‘~/tmp/scratch/Rtmpvc6iS2’
‘~/tmp/scratch/Rtmpvc7Opk’ ‘~/tmp/scratch/RtmpveyyTz’
‘~/tmp/scratch/RtmpwpdUAD’ ‘~/tmp/scratch/RtmpxLRxuS’
‘~/tmp/scratch/RtmpxMvsy1’ ‘~/tmp/scratch/RtmpxSzSSx’
‘~/tmp/scratch/Rtmpxd6vGd’ ‘~/tmp/scratch/RtmpxvMMYT’
‘~/tmp/scratch/RtmpzJVSwt’ ‘~/tmp/scratch/RtmpzaPUck’
‘~/tmp/scratch/xvfb-run.0Gjbc9’ ‘~/tmp/scratch/xvfb-run.1OodgL’
‘~/tmp/scratch/xvfb-run.2ab8Zb’ ‘~/tmp/scratch/xvfb-run.4BuD4K’
‘~/tmp/scratch/xvfb-run.4FL8Dj’ ‘~/tmp/scratch/xvfb-run.5R8Kbe’
‘~/tmp/scratch/xvfb-run.5YLTyz’ ‘~/tmp/scratch/xvfb-run.60pPQR’
‘~/tmp/scratch/xvfb-run.8ncMDL’ ‘~/tmp/scratch/xvfb-run.AsLgR3’
‘~/tmp/scratch/xvfb-run.BIUPdi’ ‘~/tmp/scratch/xvfb-run.CYRWFO’
‘~/tmp/scratch/xvfb-run.CnvpQ8’ ‘~/tmp/scratch/xvfb-run.D5AvjM’
‘~/tmp/scratch/xvfb-run.FZDo6O’ ‘~/tmp/scratch/xvfb-run.GonTLd’
‘~/tmp/scratch/xvfb-run.H7pN3B’ ‘~/tmp/scratch/xvfb-run.Iw9s5q’
‘~/tmp/scratch/xvfb-run.IzwvjK’ ‘~/tmp/scratch/xvfb-run.JZ7SSZ’
‘~/tmp/scratch/xvfb-run.JqoBkW’ ‘~/tmp/scratch/xvfb-run.MitlyJ’
‘~/tmp/scratch/xvfb-run.NbzMS4’ ‘~/tmp/scratch/xvfb-run.O3URFX’
‘~/tmp/scratch/xvfb-run.OyBZuI’ ‘~/tmp/scratch/xvfb-run.PeLbYn’
‘~/tmp/scratch/xvfb-run.PrcDjt’ ‘~/tmp/scratch/xvfb-run.Q8YuDs’
‘~/tmp/scratch/xvfb-run.QcKdKc’ ‘~/tmp/scratch/xvfb-run.REpaUh’
‘~/tmp/scratch/xvfb-run.RHSrQu’ ‘~/tmp/scratch/xvfb-run.S7YCcl’
‘~/tmp/scratch/xvfb-run.SI9xOa’ ‘~/tmp/scratch/xvfb-run.T1j9dp’
‘~/tmp/scratch/xvfb-run.TMlROv’ ‘~/tmp/scratch/xvfb-run.TTIWgq’
‘~/tmp/scratch/xvfb-run.TeZ6km’ ‘~/tmp/scratch/xvfb-run.Teoehp’
‘~/tmp/scratch/xvfb-run.VWJjET’ ‘~/tmp/scratch/xvfb-run.Xdmizs’
‘~/tmp/scratch/xvfb-run.XfNJCq’ ‘~/tmp/scratch/xvfb-run.Y1K8N1’
‘~/tmp/scratch/xvfb-run.ZwwsZ0’ ‘~/tmp/scratch/xvfb-run.ZzSbgw’
‘~/tmp/scratch/xvfb-run.a5ng5u’ ‘~/tmp/scratch/xvfb-run.aJWqUk’
‘~/tmp/scratch/xvfb-run.aWcHDa’ ‘~/tmp/scratch/xvfb-run.bJRqlj’
‘~/tmp/scratch/xvfb-run.baJo76’ ‘~/tmp/scratch/xvfb-run.bpdf2g’
‘~/tmp/scratch/xvfb-run.chBeJS’ ‘~/tmp/scratch/xvfb-run.eDq0L3’
‘~/tmp/scratch/xvfb-run.eGfa6f’ ‘~/tmp/scratch/xvfb-run.eITA2N’
‘~/tmp/scratch/xvfb-run.esbpaA’ ‘~/tmp/scratch/xvfb-run.ex37VO’
‘~/tmp/scratch/xvfb-run.gMIuLU’ ‘~/tmp/scratch/xvfb-run.gX8Zyc’
‘~/tmp/scratch/xvfb-run.gZs3OB’ ‘~/tmp/scratch/xvfb-run.idG3ws’
‘~/tmp/scratch/xvfb-run.j1MXTd’ ‘~/tmp/scratch/xvfb-run.kNON8i’
‘~/tmp/scratch/xvfb-run.mXYJ1v’ ‘~/tmp/scratch/xvfb-run.mbvVYT’
‘~/tmp/scratch/xvfb-run.oNBf1E’ ‘~/tmp/scratch/xvfb-run.oUKYI9’
‘~/tmp/scratch/xvfb-run.pOT13w’ ‘~/tmp/scratch/xvfb-run.pZveoF’
‘~/tmp/scratch/xvfb-run.rLhlX9’ ‘~/tmp/scratch/xvfb-run.s4KXBR’
‘~/tmp/scratch/xvfb-run.spMD5H’ ‘~/tmp/scratch/xvfb-run.uImwwA’
‘~/tmp/scratch/xvfb-run.vKhYHG’ ‘~/tmp/scratch/xvfb-run.xBB4jW’
‘~/tmp/scratch/xvfb-run.xb5yvI’ ‘~/tmp/scratch/xvfb-run.xn1JBx’
‘~/tmp/scratch/xvfb-run.y3nTGB’ ‘~/tmp/scratch/xvfb-run.zPZ9dh’
Flavor: r-devel-linux-x86_64-debian-gcc
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)
7: FUN(X[[i]], ...)
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, ...)
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 ")
9: doTryCatch(return(expr), name, parentenv, handler)
10: } 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)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)
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 { 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()) 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$pos[i], } invisible(structure(msg, class = "try-error", condition = e))})
bgenInfo$N, bgenInfo$CompressionFlag, 1) } }}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)13: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
14:
21: eval(code, test_env)
22: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
15: FUN(X[[i]], ...)
eval(code, test_env)
23: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) {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: skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUEe$fun(obj, substitute(ex), parent.frame(), e$data)
19: foreach(i = 1:ncores) %dopar% { if (!is.null(obj$P)) { if (bgenInfo$LayoutFlag == 2) { invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
24: doTryCatch(return(expr), name, parentenv, handler)
25: .Call(C_glmm_score_bgen13, as.numeric(res), obj$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$CompressionFlag, 1) } else {tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
27: tryCatch(withCallingHandlers({ eval(code, test_env) .Call(C_glmm_score_bgen11, as.numeric(res), obj$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 { new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { .Call(C_glmm_score_bgen11_sp, as.numeric(res), obj$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$CompressionFlag, 1) } } if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}
20: glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1.tmp, ncores = 2)
}, 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: 21: eval(code, test_env)
22: eval(code, test_env)
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) {23: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
31: test_code(code, parent.frame())
32: test_that("cross-sectional id le 400 binomial", { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE 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") 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) data(example) suppressWarnings(RNGversion("3.5.0")) set.seed(123) pheno <- rbind(example$pheno, example$pheno[1:100, ]) pheno$id <- 1:500 pheno$disease[sample(1:500, 20)] <- NA pheno$age[sample(1:500, 20)] <- NA pheno$sex[sample(1:500, 20)] <- NA pheno <- pheno[sample(1:500, 450), ] pheno <- pheno[pheno$id <= 400, ] kins <- example$GRM obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1) obj1.bed.noselect.1 <- read.table(obj1.outfile.bed.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.bed.noselect.1.tmp <- tempfile()
25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
26: tryCatchList(expr, classes, parentenv, handlers)
27: expect_error(glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.1.tmp, ncores = 2), "Error: parallel computing currently not implemented for PLINK binary format genotypes.") unlink(obj1.outfile.bed.noselect.1.tmp) obj1.outfile.bed.select.1 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.1) obj1.bed.select.1 <- read.table(obj1.outfile.bed.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.select.1) obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.1) obj1.bgen.noselect.1 <- read.table(obj1.outfile.bgen.noselect.1, header = TRUE, as.is = TRUE)tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { 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) skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE expect_equal(obj1.bgen.noselect.1, obj1.bgen.noselect.1.tmp) unlink(obj1.outfile.bgen.noselect.1.tmp) obj1.outfile.bgen.select.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, select = select, outfile = obj1.outfile.bgen.select.1) obj1.bgen.select.1 <- read.table(obj1.outfile.bgen.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.bgen.noselect.1, obj1.bgen.select.1) expect_equal(obj1.bed.select.1[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")], obj1.bgen.select.1[, invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
28: doWithOneRestart(return(expr), restart)
29: withOneRestart(expr, restarts[[1L]])
30: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$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$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1) obj1.gds.noselect.1 <- read.table(obj1.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj1.outfile.gds.noselect.1.tmp <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.1.tmp, ncores = 2) obj1.gds.noselect.1.tmp <- read.table(obj1.outfile.gds.noselect.1.tmp, snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
31: test_code(code, parent.frame())
32: test_that("cross-sectional id le 400 binomial", { plinkfiles <- strsplit(system.file("extdata", "geno.bed", package = "GMMAT"), ".bed", fixed = TRUE)[[1]] header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.1.tmp) unlink(obj1.outfile.gds.noselect.1.tmp) obj1.outfile.gds.select.1 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.1) bgenfile <- system.file("extdata", "geno.bgen", package = "GMMAT") samplefile <- system.file("extdata", "geno.sample", package = "GMMAT") gdsfile <- system.file("extdata", "geno.gds", package = "GMMAT") txtfile <- system.file("extdata", "geno.txt", package = "GMMAT") txtfile1 <- system.file("extdata", "geno.txt.gz", package = "GMMAT") txtfile2 <- system.file("extdata", "geno.txt.bz2", package = "GMMAT") data(example) suppressWarnings(RNGversion("3.5.0")) obj1.gds.select.1 <- read.table(obj1.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.select.1) expect_equal(obj1.bed.select.1$PVAL, signif(obj1.gds.select.1$PVAL)) expect_equal(signif(range(obj1.gds.select.1$PVAL)), signif(c(0.003804942, 0.986534857))) 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 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, 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, 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$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.1 <- tempfile() header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1$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.") unlink(obj1.outfile.txt.select.1.tmp) 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, 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() 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) 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, 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) obj1.outfile.bgen.noselect.1 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, 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, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, 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.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)) 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) 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, 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, 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", quietly = TRUE)) { obj1.outfile.gds.noselect.1 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.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, 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) 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[, c("SNP", "CHR", "POS", "A1", "A2", "N", "AF", "SCORE", "VAR", "PVAL")]) if (requireNamespace("SeqArray", quietly = TRUE) && requireNamespace("SeqVarTools", 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.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$PVAL)), signif(c(0.003804942, 0.986534857))) unlink(c(obj1.outfile.gds.noselect.1, obj1.outfile.gds.select.1)) } quietly = TRUE)) { obj2.outfile.gds.noselect.1 <- tempfile() glmm.score(obj2, infile = gdsfile, outfile = obj2.outfile.gds.noselect.1) obj2.gds.noselect.1 <- read.table(obj2.outfile.gds.noselect.1, header = TRUE, as.is = TRUE) obj2.outfile.gds.select.1 <- tempfile() glmm.score(obj2, infile = gdsfile, select = select, outfile = obj2.outfile.gds.select.1) obj2.gds.select.1 <- read.table(obj2.outfile.gds.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.select.1) expect_equal(obj2.bed.select.1$PVAL, signif(obj2.gds.select.1$PVAL)) expect_equal(signif(range(obj2.gds.select.1$PVAL)), signif(c(0.003738918, 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) expect_equal(obj1.bed.select.1$PVAL, obj1.txt.select.1$PVAL) 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")) 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, 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.") unlink(obj1.outfile.txt.select.1.tmp) obj1.outfile.txt1.select.1 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.1, 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, 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) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) pheno <- pheno[idx, ] 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) obj1.outfile.txt2.select.1 <- tempfile() glmm.score(obj1, infile = txtfile2, outfile = obj1.outfile.txt2.select.1, 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$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.2) obj1.bed.noselect.2 <- read.table(obj1.outfile.bed.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.bed.noselect.1, obj1.bed.noselect.2) obj1.outfile.bed.select.2 <- tempfile() glmm.score(obj1, infile = plinkfiles, select = select, outfile = obj1.outfile.bed.select.2) obj1.bed.select.2 <- read.table(obj1.outfile.bed.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt2.select.1 <- read.table(obj1.outfile.txt2.select.1, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt2.select.1) unlink(c(obj1.outfile.bed.noselect.1, obj1.outfile.bed.select.1, obj1.outfile.bgen.noselect.1, obj1.outfile.bgen.select.1, obj1.outfile.txt.select.1, obj1.outfile.txt1.select.1, obj1.outfile.txt2.select.1)) skip_on_cran() obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, id = "id", family = binomial(link = "logit"), method = "REML", header = TRUE, as.is = TRUE) expect_equal(obj1.bed.select.1, obj1.bed.select.2) obj1.outfile.bgen.noselect.2 <- tempfile() glmm.score(obj1, infile = bgenfile, BGEN.samplefile = samplefile, outfile = obj1.outfile.bgen.noselect.2) obj1.bgen.noselect.2 <- read.table(obj1.outfile.bgen.noselect.2, method.optim = "AI") select <- match(1:400, unique(obj2$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, 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(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) obj1.bgen.select.2 <- read.table(obj1.outfile.bgen.select.2, 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, 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[, 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() 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) 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) 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$PVAL)), signif(c(0.003738918, 0.996996766))) } obj2.outfile.txt.select.1 <- tempfile() glmm.score(obj2, infile = txtfile, outfile = obj2.outfile.txt.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", obj1.gds.noselect.2 <- read.table(obj1.outfile.gds.noselect.2, header = TRUE, as.is = TRUE) expect_equal(obj1.gds.noselect.1, obj1.gds.noselect.2) obj1.outfile.gds.select.2 <- tempfile() glmm.score(obj1, infile = gdsfile, select = select, outfile = obj1.outfile.gds.select.2) obj1.gds.select.2 <- read.table(obj1.outfile.gds.select.2, header = TRUE, as.is = TRUE) "Allele2")) obj2.txt.select.1 <- read.table(obj2.outfile.txt.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1$PVAL, obj2.txt.select.1$PVAL) obj2.outfile.txt1.select.1 <- tempfile() glmm.score(obj2, infile = txtfile1, outfile = obj2.outfile.txt1.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) expect_equal(obj1.gds.select.1, obj1.gds.select.2) } obj1.outfile.txt.select.2 <- tempfile() glmm.score(obj1, infile = txtfile, outfile = obj1.outfile.txt.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, obj2.txt1.select.1 <- read.table(obj2.outfile.txt1.select.1, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt1.select.1) obj2.outfile.txt2.select.1 <- tempfile() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.1, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj2.txt2.select.1 <- read.table(obj2.outfile.txt2.select.1, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt.select.2 <- read.table(obj1.outfile.txt.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt.select.1, obj1.txt.select.2) obj1.outfile.txt1.select.2 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.2, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, header = TRUE, as.is = TRUE) expect_equal(obj2.txt.select.1, obj2.txt2.select.1) idx <- sample(nrow(pheno)) pheno <- pheno[idx, ] obj1 <- glmmkin(disease ~ age + sex, data = pheno, kins = kins, select = select, infile.header.print = c("SNP", "Allele1", "Allele2")) obj1.txt1.select.2 <- read.table(obj1.outfile.txt1.select.2, header = TRUE, as.is = TRUE) expect_equal(obj1.txt1.select.1, obj1.txt1.select.2) id = "id", family = binomial(link = "logit"), method = "REML", method.optim = "AI") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.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, 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) obj2 <- glmmkin(disease ~ age + sex, data = pheno, kins = NULL, header = TRUE, as.is = TRUE) 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) 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() 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, 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, header = TRUE, as.is = TRUE) expect_equal(obj2.bed.select.1, obj2.bed.select.2) obj2.outfile.bgen.noselect.2 <- tempfile() 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", quietly = TRUE)) { obj1.outfile.gds.noselect.2 <- tempfile() glmm.score(obj1, infile = gdsfile, outfile = obj1.outfile.gds.noselect.2) 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) 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() 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() 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(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, 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) 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, obj2.gds.select.2 <- read.table(obj2.outfile.gds.select.2, 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, 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, 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) 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, 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", method.optim = "AI") select <- match(1:400, unique(obj2$id_include)) select[is.na(select)] <- 0 obj2.outfile.bed.noselect.2 <- tempfile() 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() glmm.score(obj2, infile = txtfile2, outfile = obj2.outfile.txt2.select.2, 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, 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) 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] 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.3 <- tempfile() glmm.score(obj1, infile = plinkfiles, outfile = obj1.outfile.bed.noselect.3) 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, header = TRUE, as.is = TRUE) expect_equal(obj2.gds.noselect.1, obj2.gds.noselect.2) 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) 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) 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) 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.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(obj1.bgen.noselect.1, obj1.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")) 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) 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", 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, 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) "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) } 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", "Allele2")) obj1.txt.select.3 <- read.table(obj1.outfile.txt.select.3, header = TRUE, as.is = TRUE) 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") select <- match(1:400, unique(obj1$id_include)) select[is.na(select)] <- 0 obj1.outfile.bed.noselect.3 <- tempfile() expect_equal(obj1.txt.select.1, obj1.txt.select.3) obj1.outfile.txt1.select.3 <- tempfile() glmm.score(obj1, infile = txtfile1, outfile = obj1.outfile.txt1.select.3, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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) 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, 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) 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) obj1.outfile.bgen.noselect.3 <- 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.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)) outfile = obj1.outfile.bgen.noselect.3) 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, 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() 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, 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, 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() 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, infile.nrow.skip = 5, infile.ncol.skip = 3, infile.ncol.print = 1:3, 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) 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) expect_equal(obj1.txt1.select.1, obj1.txt1.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) 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", "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.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, 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) 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, 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) 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, 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", 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, 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) 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, 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() 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)) 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()})
33: eval(code, test_env) 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()
34: eval(code, test_env)
35: withCallingHandlers({ 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, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() } obj2.outfile.txt2.select.1)) unlink(c(obj1.outfile.bed.noselect.2, obj1.outfile.bed.select.2, obj1.outfile.bgen.noselect.2, obj1.outfile.bgen.select.2, obj1.outfile.txt.select.2, obj1.outfile.txt1.select.2, obj1.outfile.txt2.select.2))}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$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, 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", 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({ 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))})
eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }33: eval(code, test_env)
34: eval(code, test_env)
35: withCallingHandlers({}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$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, eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() } error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
40: doWithOneRestart(return(expr), restart)
41: withOneRestart(expr, restarts[[1L]])
}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$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)42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN")
36: doTryCatch(return(expr), name, parentenv, handler)
37: tryCatchOne(expr, names, parentenv, handlers[[1L]])
38: } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) {tryCatchList(expr, classes, parentenv, handlers)
39: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: 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]])
test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
44: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
45: FUN(X[[i]], ...)
42: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }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: }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
50: error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
43: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: 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)with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
52: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,
47: doTryCatch(return(expr), name, parentenv, handler)
48: tryCatchOne(expr, names, parentenv, handlers[[1L]])
49: tryCatchList(expr, classes, parentenv, handlers)
env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
53: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 50: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
51: with_reporter(reporters$multi, lapply(test_paths, test_one_file, load_package = load_package, parallel = parallel, shuffle = shuffle)
54: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
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)55: test_check("GMMAT")
An irrecoverable exception occurred. R is aborting now ...
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