Last updated on 2024-08-17 16:48:17 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.7 | 8.96 | 169.78 | 178.74 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.1.7 | 7.38 | 120.25 | 127.63 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.1.7 | 309.21 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1.7 | 295.04 | OK | |||
| r-devel-windows-x86_64 | 0.1.7 | 13.00 | 187.00 | 200.00 | OK | |
| r-patched-linux-x86_64 | 0.1.7 | 11.76 | 160.74 | 172.50 | OK | |
| r-release-linux-x86_64 | 0.1.7 | 9.66 | 163.03 | 172.69 | OK | |
| r-release-macos-arm64 | 0.1.7 | 85.00 | OK | |||
| r-release-macos-x86_64 | 0.1.7 | 140.00 | OK | |||
| r-release-windows-x86_64 | 0.1.7 | 12.00 | 188.00 | 200.00 | OK | |
| r-oldrel-macos-arm64 | 0.1.7 | 94.00 | OK | |||
| r-oldrel-macos-x86_64 | 0.1.7 | 161.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.1.7 | 17.00 | 220.00 | 237.00 | OK |
Version: 0.1.7
Check: tests
Result: ERROR
Running ‘Rsctest.R’ [6s/21s]
Running ‘testthat.R’ [18s/60s]
Running the tests in ‘tests/Rsctest.R’ failed.
Complete output:
> set.seed(7485)
> library(mstDIF)
>
> # function to simulate 3PL data
> P3pl <- function(theta, a = rep(1, length(b)), b, c = rep(0, length(b)),
+ generateResponses = FALSE, seed = 1){
+ nItem <- length(b)
+ nPerson <- length(theta)
+ A <- rep(1, nPerson) %o% a
+ B <- rep(1, nPerson) %o% b
+ C <- rep(1, nPerson) %o% c
+ Theta <- theta %o% rep(1, nItem)
+
+ Theta_B <- Theta - B
+ P <- C + (1 - C) * (1 + exp(- A * Theta_B))^(-1)
+ if(generateResponses){
+ set.seed(seed)
+ resp <- (P > matrix(runif(nPerson * nItem),
+ ncol = nItem, nrow = nPerson)) * 1
+ return(resp)
+ } else (return(P))
+ }
>
> # item parameters
> b <- seq(-2, 1.8, length.out = 3)
> a <- runif(length(b), 1, 1.5)
> c <- rep(0, length(b))
>
> # person parameters
> nPerson <- 20
> theta <- rnorm(nPerson)
>
> # generate responses
> resp <- P3pl(theta, a, b, generateResponses = TRUE)
>
> # create orders
> metric <- rnorm(nPerson)
> factor <- factor(sample(1:3, size = nPerson, replace = TRUE))
> order <- ordered(sample(1:3, size = nPerson, replace = TRUE))
>
> # compute scores
> scores <- mstDIF:::get_scores(resp, a, b, c, theta,
+ slope_intercept = FALSE, return_terms = TRUE)
> scores
$scores
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.18468225 -0.29352519 0.17191034 -0.08138442 0.7041845 0.16688286
[2,] 0.24976935 -0.13752642 0.10360703 -0.15718775 -0.6362792 0.06725971
[3,] 0.25808191 0.24282391 0.05379964 -0.27972165 0.2904038 0.02636932
[4,] -1.75302701 0.03643510 0.13375030 0.92545002 -0.5366830 0.10198830
[5,] 0.26231754 -0.62929507 0.05773734 -0.26498604 -0.8135188 0.02901723
[6,] 0.18569685 0.17124920 0.17098626 -0.08218499 -0.4199869 0.16481453
[7,] 0.08519532 0.22398270 -0.14471676 -0.02501704 -0.1519493 -0.81380608
[8,] 0.11580700 0.24759733 0.19010357 -0.03870471 -0.2272828 0.38960229
[9,] 0.21991317 -0.05828303 0.13709574 -0.11414900 0.5950554 0.10650713
[10,] 0.19486646 0.24290672 0.03447349 -0.37956746 0.1996025 0.01468697
[11,] 0.18122482 0.18219312 0.17500655 -0.07870552 -0.4068408 0.17411170
[12,] 0.22215194 -0.04513158 0.13474084 -0.11668733 0.5876285 0.10330967
[13,] 0.11464363 0.24729999 0.18873158 -0.03813587 -0.2243038 0.39537570
[14,] 0.25852876 -0.68865553 0.05414770 -0.27836213 -0.8292328 0.02659968
[15,] -0.86864569 0.22788034 0.06387389 0.79869143 0.3338023 0.03332827
[16,] 0.26407992 0.18398173 0.08156276 -0.19817396 0.4045920 0.04707138
[17,] 0.23633298 0.03423168 0.11938525 -0.13482229 0.5381899 0.08422634
[18,] -0.40513722 -1.03611565 0.03762819 0.68411508 -0.9059990 0.01644519
[19,] 0.22423371 -0.03306617 0.13253647 -0.11911593 0.5806450 0.10038748
[20,] 0.19966544 0.13066538 0.15769286 -0.09394865 -0.4619674 0.13855327
$terms
$terms$P
[,1] [,2] [,3]
[1,] 0.9219875 0.6280759 0.11999384
[2,] 0.8493248 0.4324902 0.04836178
[3,] 0.7318676 0.2590168 0.01896034
[4,] 0.8871073 0.5213220 0.07333268
[5,] 0.7459927 0.2744067 0.02086427
[6,] 0.9212201 0.6254055 0.11850665
[7,] 0.9760195 0.8644734 0.41484873
[8,] 0.9628989 0.7972820 0.28013587
[9,] 0.8905804 0.5307415 0.07658186
[10,] 0.6361586 0.1780294 0.01056038
[11,] 0.9245554 0.6371307 0.12519160
[12,] 0.8881472 0.5241173 0.07428279
[13,] 0.9634442 0.7999391 0.28428713
[14,] 0.7331708 0.2603911 0.01912598
[15,] 0.7656005 0.2977248 0.02396404
[16,] 0.8100367 0.3608635 0.03384575
[17,] 0.8707636 0.4800221 0.06056130
[18,] 0.6557712 0.1919218 0.01182459
[19,] 0.8858192 0.5178886 0.07218165
[20,] 0.9099438 0.5879622 0.09962401
$terms$a
[,1] [,2] [,3]
[1,] 2.3673403 0.46734029 -1.4326597
[2,] 1.6576669 -0.24233311 -2.1423331
[3,] 0.9625167 -0.93748326 -2.8374833
[4,] 1.9761161 0.07611608 -1.8238839
[5,] 1.0327166 -0.86728342 -2.7672834
[6,] 2.3571589 0.45715890 -1.4428411
[7,] 3.5526849 1.65268487 -0.2473151
[8,] 3.1213880 1.22138797 -0.6786120
[9,] 2.0098143 0.10981435 -1.7901857
[10,] 0.5355807 -1.36441929 -3.2644193
[11,] 2.4020903 0.50209026 -1.3979097
[12,] 1.9861097 0.08610969 -1.8138903
[13,] 3.1361234 1.23612336 -0.6638766
[14,] 0.9688923 -0.93110767 -2.8311077
[15,] 1.1345939 -0.76540606 -2.6654061
[16,] 1.3901626 -0.50983742 -2.4098374
[17,] 1.8286873 -0.07131272 -1.9713127
[18,] 0.6178027 -1.28219731 -3.1821973
[19,] 1.9638480 0.06384804 -1.8361520
[20,] 2.2171199 0.31711990 -1.5828801
$terms$b
[,1] [,2] [,3]
[1,] -1.043222 -1.121177 -1.390762
[2,] -1.043222 -1.121177 -1.390762
[3,] -1.043222 -1.121177 -1.390762
[4,] -1.043222 -1.121177 -1.390762
[5,] -1.043222 -1.121177 -1.390762
[6,] -1.043222 -1.121177 -1.390762
[7,] -1.043222 -1.121177 -1.390762
[8,] -1.043222 -1.121177 -1.390762
[9,] -1.043222 -1.121177 -1.390762
[10,] -1.043222 -1.121177 -1.390762
[11,] -1.043222 -1.121177 -1.390762
[12,] -1.043222 -1.121177 -1.390762
[13,] -1.043222 -1.121177 -1.390762
[14,] -1.043222 -1.121177 -1.390762
[15,] -1.043222 -1.121177 -1.390762
[16,] -1.043222 -1.121177 -1.390762
[17,] -1.043222 -1.121177 -1.390762
[18,] -1.043222 -1.121177 -1.390762
[19,] -1.043222 -1.121177 -1.390762
[20,] -1.043222 -1.121177 -1.390762
$terms$c
[,1] [,2] [,3]
[1,] 1.084613 1.592164 8.333761
[2,] 1.177406 2.312191 20.677487
[3,] 1.366367 3.860753 52.741674
[4,] 1.127259 1.918200 13.636484
[5,] 1.340496 3.644226 47.928833
[6,] 1.085517 1.598963 8.438345
[7,] 1.024570 1.156774 2.410517
[8,] 1.038531 1.254261 3.569696
[9,] 1.122863 1.884156 13.057923
[10,] 1.571935 5.617050 94.693600
[11,] 1.081601 1.569537 7.987757
[12,] 1.125939 1.907970 13.462068
[13,] 1.037943 1.250095 3.517570
[14,] 1.363939 3.840377 52.284912
[15,] 1.306164 3.358807 41.729193
[16,] 1.234512 2.771131 29.545805
[17,] 1.148417 2.083237 16.512196
[18,] 1.524922 5.210455 84.569506
[19,] 1.128899 1.930917 13.853937
[20,] 1.098969 1.700790 10.037741
>
> # compute process
> process <- apply(mstDIF:::scale_scores(scores$scores,
+ decorrelate = TRUE)[order(metric),],
+ 2, cumsum)
> process
[,1] [,2] [,3] [,4] [,5]
[1,] 2.420033e-04 -3.334786e-01 -1.006781e-01 -2.818289e-01 -2.725019e-01
[2,] 7.404527e-02 -2.365103e-01 -2.790570e-01 -3.512988e-01 -9.589193e-02
[3,] -1.210368e-01 -7.134302e-02 -5.975643e-01 1.935734e-01 9.981391e-02
[4,] 1.275834e-02 1.254864e-01 -3.028179e-01 2.360873e-01 -1.017536e-01
[5,] 9.917085e-02 3.384026e-01 -4.148226e-01 3.013782e-01 -1.746471e-01
[6,] 1.558282e-01 2.450313e-01 -2.763645e-01 2.776883e-01 1.168400e-01
[7,] 2.884038e-01 4.476681e-01 2.711118e-02 3.231950e-01 -8.003818e-02
[8,] 3.471061e-01 3.641271e-01 1.529694e-01 2.996919e-01 2.065794e-01
[9,] 4.640467e-01 3.709941e-01 2.077436e-01 2.405129e-01 -5.336269e-02
[10,] 5.661012e-01 6.017797e-01 1.599709e-01 3.079788e-01 -1.717531e-01
[11,] -3.424868e-01 6.527641e-01 3.862716e-01 3.117870e-01 -3.015950e-01
[12,] -2.733318e-01 6.244143e-01 4.354578e-01 2.866215e-01 -4.371310e-02
[13,] -2.539443e-01 7.702933e-01 9.482611e-02 1.029992e-01 7.482810e-02
[14,] -2.736185e-01 3.940135e-01 -1.394281e-02 -2.107656e-01 -1.963198e-01
[15,] -1.721636e-01 6.246434e-01 -8.422372e-02 -1.430374e-01 -3.131174e-01
[16,] -2.850898e-01 7.629785e-01 -5.225636e-01 -5.245715e-01 -2.305197e-01
[17,] -2.012700e-01 2.218021e-01 -7.402078e-01 3.678912e-02 -4.604837e-01
[18,] -6.493485e-02 3.961034e-01 -4.827298e-01 6.781724e-02 -6.761841e-01
[19,] -1.054789e-02 2.920380e-01 -3.309072e-01 4.379308e-02 -3.794727e-01
[20,] 4.059253e-16 -9.540979e-18 -1.249001e-16 2.524023e-16 4.163336e-17
[,6]
[1,] -5.781780e-03
[2,] 1.578973e-02
[3,] 6.572980e-02
[4,] 8.266642e-02
[5,] -7.856663e-01
[6,] -7.919143e-01
[7,] -7.680210e-01
[8,] -7.738898e-01
[9,] -7.878639e-01
[10,] -4.507996e-01
[11,] -4.672783e-01
[12,] -4.691381e-01
[13,] -4.241853e-01
[14,] -4.296219e-01
[15,] -8.059560e-02
[16,] -1.974634e-02
[17,] -1.952819e-03
[18,] -1.154427e-03
[19,] -7.702091e-03
[20,] -1.578598e-16
>
> # Compare with strucchange
> library(strucchange)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
> gefp <- gefp(x = scale(scores$scores, scale = FALSE), fit = NULL,
+ scores = function(x) {x}, order.by = metric)
>
> # Is the cumsum score process the same?
> stopifnot(all((gefp$process[-1,] - process) < 1e-14))
Error: all((gefp$process[-1, ] - process) < 1e-14) is not TRUE
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang