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|
#!/bin/sh
set -o errexit
set -o nounset
set -o noclobber
out=/tmp/irt-regression.$$.log
opt="-q --vanilla --no-save"
tests="
inst/models/passing/xxm-1.R
inst/models/passing/xxm-2.R
inst/models/passing/xxm-3.R
inst/models/passing/xxm-4.R
inst/models/passing/lmer-1.R
inst/models/passing/lmer-2.R
inst/models/passing/Rampart1.R
inst/models/passing/Autoregressive_Tree_Matrix.R
inst/models/passing/Autoregressive_Tree_Path.R
inst/models/passing/multilevelLatentRegression.R
inst/models/passing/MultilevelUniRandomSlopeInt.R
tests/testthat/test-gendata-multilevel.R
inst/models/nightly/univACErSEM.R
inst/models/nightly/mplus-ex9.1.R
inst/models/nightly/mplus-ex9.6.R
inst/models/nightly/mplus-ex9.11.R
inst/models/nightly/mplus-ex9.12.R
inst/models/nightly/xxm-cfars.R
inst/models/nightly/xxm-hcfa.R
inst/models/nightly/xxm-lgc.R
inst/models/nightly/xxm-mlcfa.R
inst/models/nightly/xxm-faces.R
inst/models/nightly/mplus-ex9.23.R
inst/models/nightly/multilevelLatentRegression2.R
"
for t in $tests; do
echo -n "$t\t"
if false; then
R $opt -f $t
else
if ! /usr/bin/time --format "%E %F %R %c" bash -c "R $opt -f $t > $out 2>&1"; then
cat $out
exit
else
rm -f $out
fi
fi
done
exit 0
# -----------------------------------
# !! check psensor for overheating !!
# -----------------------------------
passing/xxm-1.R 0:01.88
passing/lmer-1.R 0:02.77
passing/lmer-2.R 0:02.92
passing/Rampart1.R 0:02.16
passing/Autoregressive_Tree_Matrix.R 0:02.00
passing/Autoregressive_Tree_Path.R 0:02.08
passing/multilevelLatentRegression.R 0:02.21
nightly/univACErSEM.R 0:02.78
nightly/multilevelLatentRegression2.R 0:16.80
nightly/mplus-ex9.6.R 0:40.32
29 Feb 2016:
passing/xxm-1.R 0:01.94
passing/lmer-1.R 0:02.94
passing/lmer-2.R 0:03.54
passing/Rampart1.R 0:02.46
passing/Autoregressive_Tree_Matrix.R 0:02.00
passing/Autoregressive_Tree_Path.R 0:02.19
passing/multilevelLatentRegression.R 0:02.24
nightly/univACErSEM.R 0:03.18
nightly/multilevelLatentRegression2.R 0:19.39
nightly/mplus-ex9.6.R 0:27.54
14 Mar 2016:
passing/xxm-1.R 0:01.87
passing/lmer-1.R 0:02.69
passing/lmer-2.R 0:03.19
passing/Rampart1.R 0:02.46
passing/Autoregressive_Tree_Matrix.R 0:01.93
passing/Autoregressive_Tree_Path.R 0:02.04
passing/multilevelLatentRegression.R 0:02.09
passing/MultilevelUniRandomSlopeInt.R 0:09.50
nightly/univACErSEM.R 0:02.85
nightly/multilevelLatentRegression2.R 0:16.71
nightly/mplus-ex9.6.R 0:09.97
19 Apr 2016:
passing/xxm-1.R 0:01.89
passing/lmer-1.R 0:02.69
passing/lmer-2.R 0:02.78
passing/Rampart1.R 0:02.29
passing/Autoregressive_Tree_Matrix.R 0:01.82
passing/Autoregressive_Tree_Path.R 0:01.87
passing/multilevelLatentRegression.R 0:02.02
passing/MultilevelUniRandomSlopeInt.R 0:06.51
nightly/univACErSEM.R 0:02.46
nightly/multilevelLatentRegression2.R 0:09.06
nightly/mplus-ex9.6.R 0:04.63
23 Dec 2016:
passing/xxm-1.R 0:01.71 0 53089 99
passing/xxm-2.R 0:01.99 0 53750 25
passing/xxm-3.R 0:02.09 0 53770 17
passing/xxm-4.R 0:02.28 0 55210 54
passing/lmer-1.R 0:02.32 0 53888 341
passing/lmer-2.R 0:02.61 0 55027 453
passing/Rampart1.R 0:02.14 0 69622 276
passing/Autoregressive_Tree_Matrix.R 0:01.68 0 53298 38
passing/Autoregressive_Tree_Path.R 0:01.74 0 53500 53
passing/multilevelLatentRegression.R 0:01.83 0 53100 25
passing/MultilevelUniRandomSlopeInt.R 0:05.71 0 61312 483
nightly/univACErSEM.R 0:02.36 0 53218 325
nightly/multilevelLatentRegression2.R 0:15.54 0 138388 7375
nightly/mplus-ex9.1.R 0:02.01 0 53578 301
nightly/mplus-ex9.6.R 0:05.17 0 53957 2170
nightly/mplus-ex9.11.R 0:17.67 0 53514 12350
nightly/mplus-ex9.12.R 0:03.71 0 54115 1055
nightly/mplus-ex9.23.R 1:44.95 0 69156 73255
nightly/xxm-cfars.R 0:03.10 0 54381 2232
nightly/xxm-faces.R 3:07.47 0 195999 155208
nightly/xxm-hcfa.R 0:02.91 0 53151 1417
nightly/xxm-lgc.R 0:02.08 0 53255 152
nightly/xxm-mlcfa.R 0:04.21 0 53388 2634
17 Jan 2017:
passing/xxm-1.R 0:01.65 0 53164 26
passing/xxm-2.R 0:01.93 0 53811 20
passing/xxm-3.R 0:01.98 0 53835 24
passing/xxm-4.R 0:02.33 0 54981 34
passing/lmer-1.R 0:02.37 0 53993 72
passing/lmer-2.R 0:02.57 0 54810 83
passing/Rampart1.R 0:02.10 0 69575 67
passing/Autoregressive_Tree_Matrix.R 0:01.63 0 53306 23
passing/Autoregressive_Tree_Path.R 0:01.69 0 53498 33
passing/multilevelLatentRegression.R 0:01.81 0 53181 20
passing/MultilevelUniRandomSlopeInt.R 0:05.29 0 57457 203
nightly/univACErSEM.R 0:02.33 0 53286 34
nightly/multilevelLatentRegression2.R 5:44.97 0 225734 114450
nightly/mplus-ex9.1.R 0:02.14 0 53612 71
nightly/mplus-ex9.6.R 0:06.09 0 53938 1536
nightly/mplus-ex9.11.R 0:23.50 0 53613 8825
nightly/mplus-ex9.12.R 0:04.20 0 54104 708
nightly/mplus-ex9.23.R 1:58.69 0 67263 43122
nightly/xxm-cfars.R 0:03.28 0 53994 595
nightly/xxm-faces.R 3:20.77 0 102663 61266
nightly/xxm-hcfa.R 0:02.67 0 53234 374
nightly/xxm-lgc.R 0:01.98 0 53322 42
nightly/xxm-mlcfa.R 0:03.61 0 53444 734
08 Oct 2018:
passing/xxm-1.R 0:04.29 0 80480 93
passing/xxm-2.R 0:03.14 0 83820 21
passing/xxm-3.R 0:03.09 0 84833 17
passing/xxm-4.R 0:03.64 0 85079 63
passing/lmer-1.R 0:03.94 0 84850 36
passing/lmer-2.R 0:04.24 0 87484 78
passing/Rampart1.R 0:03.74 0 95992 127
passing/Autoregressive_Tree_Matrix.R 0:02.49 0 64393 23
passing/Autoregressive_Tree_Path.R 0:02.64 0 64644 45
passing/multilevelLatentRegression.R 0:03.80 0 79878 39
passing/MultilevelUniRandomSlopeInt.R 0:07.41 0 89378 259
passing/Multilevel-GenData.R 0:04.75 0 80517 330
nightly/univACErSEM.R 0:03.89 0 82490 32
nightly/multilevelLatentRegression2.R 2:49.68 0 255047 94913
nightly/mplus-ex9.1.R 0:03.29 0 62924 73
nightly/mplus-ex9.6.R 0:06.38 0 80932 2399
nightly/mplus-ex9.11.R 0:04.82 0 81340 417
nightly/mplus-ex9.12.R 0:04.54 0 63443 896
nightly/mplus-ex9.23.R 3:07.69 0 95947 92962
nightly/xxm-cfars.R 0:04.02 0 63552 400
nightly/xxm-faces.R 3:01.82 0 113517 103467
nightly/xxm-hcfa.R 0:03.07 0 62592 49
nightly/xxm-lgc.R 0:03.85 0 73264 177
nightly/xxm-mlcfa.R 0:04.07 0 81109 88
v2.14.11-36-gaeb19eec7 (30 Oct 2019):
inst/models/passing/xxm-1.R 0:03.35 0 51501 20
inst/models/passing/xxm-2.R 0:02.80 0 70349 25
inst/models/passing/xxm-3.R 0:03.33 0 71438 18
inst/models/passing/xxm-4.R 0:03.49 0 71839 127
inst/models/passing/lmer-1.R 0:03.36 0 70485 292
inst/models/passing/lmer-2.R 0:03.68 0 71725 81
inst/models/passing/Rampart1.R 0:03.18 0 52393 130
inst/models/passing/Autoregressive_Tree_Matrix.R 0:02.38 0 54712 15
inst/models/passing/Autoregressive_Tree_Path.R 0:02.60 0 54261 93
inst/models/passing/multilevelLatentRegression.R 0:04.03 0 51521 215
inst/models/passing/MultilevelUniRandomSlopeInt.R 0:07.49 0 76525 199
tests/testthat/test-gendata-multilevel.R 0:03.24 0 51516 44
inst/models/nightly/univACErSEM.R 0:03.18 0 52084 27
inst/models/nightly/multilevelLatentRegression2.R 6:43.28 0 632283 74204
inst/models/nightly/mplus-ex9.1.R 0:02.70 0 51908 17
inst/models/nightly/mplus-ex9.6.R 0:05.37 0 52233 325
inst/models/nightly/mplus-ex9.11.R 0:04.30 0 70233 124
inst/models/nightly/mplus-ex9.12.R 0:03.90 0 52418 287
inst/models/nightly/mplus-ex9.23.R 2:59.58 0 97625 19158
inst/models/nightly/xxm-cfars.R 0:03.43 0 52509 388
inst/models/nightly/xxm-faces.R 2:56.96 0 8166393 15910
inst/models/nightly/xxm-hcfa.R 0:02.61 0 51567 13
inst/models/nightly/xxm-lgc.R 0:02.97 0 51733 31
inst/models/nightly/xxm-mlcfa.R 0:03.46 0 51797 71
v2.14.11-58-g2fa710390 (06 Nov 2019):
inst/models/passing/xxm-1.R 0:03.56 0 51497 12
inst/models/passing/xxm-2.R 0:02.78 0 70214 24
inst/models/passing/xxm-3.R 0:03.05 0 71441 19
inst/models/passing/xxm-4.R 0:03.23 0 71554 35
inst/models/passing/lmer-1.R 0:03.25 0 70483 37
inst/models/passing/lmer-2.R 0:03.52 0 71690 55
inst/models/passing/Rampart1.R 0:02.99 0 52265 40
inst/models/passing/Autoregressive_Tree_Matrix.R 0:02.12 0 54734 34
inst/models/passing/Autoregressive_Tree_Path.R 0:02.25 0 54205 14
inst/models/passing/multilevelLatentRegression.R 0:03.08 0 51546 25
inst/models/passing/MultilevelUniRandomSlopeInt.R 0:06.06 0 73656 99
tests/testthat/test-gendata-multilevel.R 0:03.22 0 51524 41
inst/models/nightly/univACErSEM.R 0:03.17 0 51781 23
inst/models/nightly/mplus-ex9.1.R 0:02.60 0 52015 19
inst/models/nightly/mplus-ex9.6.R 0:03.97 0 52477 369
inst/models/nightly/mplus-ex9.11.R 0:04.21 0 66253 108
inst/models/nightly/mplus-ex9.12.R 0:03.09 0 52817 223
inst/models/nightly/xxm-cfars.R 0:03.75 0 54591 459
inst/models/nightly/xxm-hcfa.R 0:02.47 0 51598 32
inst/models/nightly/xxm-lgc.R 0:02.94 0 51731 31
inst/models/nightly/xxm-mlcfa.R 0:03.23 0 51781 48
inst/models/nightly/xxm-faces.R 3:06.97 0 5606833 74981
inst/models/nightly/mplus-ex9.23.R 2:25.05 0 79302 35910
inst/models/nightly/multilevelLatentRegression2.R 6:10.13 0 336006 165208
|
