Last updated on 2025-12-04 07:51:19 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 6.2.6 | 26.52 | 201.96 | 228.48 | OK | |
| r-devel-linux-x86_64-debian-gcc | 6.2.6 | 17.91 | 141.05 | 158.96 | OK | |
| r-devel-linux-x86_64-fedora-clang | 6.2.6 | 135.00 | 235.05 | 370.05 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 6.2.6 | 148.00 | 230.26 | 378.26 | ERROR | |
| r-devel-windows-x86_64 | 6.2.6 | 33.00 | 209.00 | 242.00 | OK | |
| r-patched-linux-x86_64 | 6.2.6 | 28.65 | 186.29 | 214.94 | OK | |
| r-release-linux-x86_64 | 6.2.6 | 26.49 | 187.45 | 213.94 | OK | |
| r-release-macos-arm64 | 6.2.6 | OK | ||||
| r-release-macos-x86_64 | 6.2.6 | 25.00 | 154.00 | 179.00 | OK | |
| r-release-windows-x86_64 | 6.2.6 | 34.00 | 250.00 | 284.00 | OK | |
| r-oldrel-macos-arm64 | 6.2.6 | NOTE | ||||
| r-oldrel-macos-x86_64 | 6.2.6 | 22.00 | 185.00 | 207.00 | NOTE | |
| r-oldrel-windows-x86_64 | 6.2.6 | 41.00 | 407.00 | 448.00 | NOTE |
Version: 6.2.6
Check: examples
Result: ERROR
Running examples in ‘VIM-Ex.R’ failed
The error most likely occurred in:
> ### Name: xgboostImpute
> ### Title: Xgboost Imputation
> ### Aliases: xgboostImpute
>
> ### ** Examples
>
> data(sleep)
> xgboostImpute(Dream~BodyWgt+BrainWgt,data=sleep)
Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), :
Passed unrecognized parameters: verbose. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger Dream_imp
1 6654.000 5712.00 NA 1.7996682 3.3 38.6 645.0 3 5 3 TRUE
2 1.000 6.60 6.3 2.0000000 8.3 4.5 42.0 3 1 3 FALSE
3 3.385 44.50 NA 3.5961127 12.5 14.0 60.0 1 1 1 TRUE
4 0.920 5.70 NA 0.8757253 16.5 NA 25.0 5 2 3 TRUE
5 2547.000 4603.00 2.1 1.8000000 3.9 69.0 624.0 3 5 4 FALSE
6 10.550 179.50 9.1 0.7000000 9.8 27.0 180.0 4 4 4 FALSE
7 0.023 0.30 15.8 3.9000000 19.7 19.0 35.0 1 1 1 FALSE
8 160.000 169.00 5.2 1.0000000 6.2 30.4 392.0 4 5 4 FALSE
9 3.300 25.60 10.9 3.6000000 14.5 28.0 63.0 1 2 1 FALSE
10 52.160 440.00 8.3 1.4000000 9.7 50.0 230.0 1 1 1 FALSE
11 0.425 6.40 11.0 1.5000000 12.5 7.0 112.0 5 4 4 FALSE
12 465.000 423.00 3.2 0.7000000 3.9 30.0 281.0 5 5 5 FALSE
13 0.550 2.40 7.6 2.7000000 10.3 NA NA 2 1 2 FALSE
14 187.100 419.00 NA 1.7214588 3.1 40.0 365.0 5 5 5 TRUE
15 0.075 1.20 6.3 2.1000000 8.4 3.5 42.0 1 1 1 FALSE
16 3.000 25.00 8.6 0.0000000 8.6 50.0 28.0 2 2 2 FALSE
17 0.785 3.50 6.6 4.1000000 10.7 6.0 42.0 2 2 2 FALSE
18 0.200 5.00 9.5 1.2000000 10.7 10.4 120.0 2 2 2 FALSE
19 1.410 17.50 4.8 1.3000000 6.1 34.0 NA 1 2 1 FALSE
20 60.000 81.00 12.0 6.1000000 18.1 7.0 NA 1 1 1 FALSE
21 529.000 680.00 NA 0.3000000 NA 28.0 400.0 5 5 5 FALSE
22 27.660 115.00 3.3 0.5000000 3.8 20.0 148.0 5 5 5 FALSE
23 0.120 1.00 11.0 3.4000000 14.4 3.9 16.0 3 1 2 FALSE
24 207.000 406.00 NA 1.8088160 12.0 39.3 252.0 1 4 1 TRUE
25 85.000 325.00 4.7 1.5000000 6.2 41.0 310.0 1 3 1 FALSE
26 36.330 119.50 NA 0.5030808 13.0 16.2 63.0 1 1 1 TRUE
27 0.101 4.00 10.4 3.4000000 13.8 9.0 28.0 5 1 3 FALSE
28 1.040 5.50 7.4 0.8000000 8.2 7.6 68.0 5 3 4 FALSE
29 521.000 655.00 2.1 0.8000000 2.9 46.0 336.0 5 5 5 FALSE
30 100.000 157.00 NA 1.0328215 10.8 22.4 100.0 1 1 1 TRUE
31 35.000 56.00 NA 4.6171999 NA 16.3 33.0 3 5 4 TRUE
32 0.005 0.14 7.7 1.4000000 9.1 2.6 21.5 5 2 4 FALSE
33 0.010 0.25 17.9 2.0000000 19.9 24.0 50.0 1 1 1 FALSE
34 62.000 1320.00 6.1 1.9000000 8.0 100.0 267.0 1 1 1 FALSE
35 0.122 3.00 8.2 2.4000000 10.6 NA 30.0 2 1 1 FALSE
36 1.350 8.10 8.4 2.8000000 11.2 NA 45.0 3 1 3 FALSE
37 0.023 0.40 11.9 1.3000000 13.2 3.2 19.0 4 1 3 FALSE
38 0.048 0.33 10.8 2.0000000 12.8 2.0 30.0 4 1 3 FALSE
39 1.700 6.30 13.8 5.6000000 19.4 5.0 12.0 2 1 1 FALSE
40 3.500 10.80 14.3 3.1000000 17.4 6.5 120.0 2 1 1 FALSE
41 250.000 490.00 NA 1.0000000 NA 23.6 440.0 5 5 5 FALSE
42 0.480 15.50 15.2 1.8000000 17.0 12.0 140.0 2 2 2 FALSE
43 10.000 115.00 10.0 0.9000000 10.9 20.2 170.0 4 4 4 FALSE
44 1.620 11.40 11.9 1.8000000 13.7 13.0 17.0 2 1 2 FALSE
45 192.000 180.00 6.5 1.9000000 8.4 27.0 115.0 4 4 4 FALSE
46 2.500 12.10 7.5 0.9000000 8.4 18.0 31.0 5 5 5 FALSE
47 4.288 39.20 NA 2.4006271 12.5 13.7 63.0 2 2 2 TRUE
48 0.280 1.90 10.6 2.6000000 13.2 4.7 21.0 3 1 3 FALSE
49 4.235 50.40 7.4 2.4000000 9.8 9.8 52.0 1 1 1 FALSE
50 6.800 179.00 8.4 1.2000000 9.6 29.0 164.0 2 3 2 FALSE
51 0.750 12.30 5.7 0.9000000 6.6 7.0 225.0 2 2 2 FALSE
52 3.600 21.00 4.9 0.5000000 5.4 6.0 225.0 3 2 3 FALSE
53 14.830 98.20 NA 3.7860343 2.6 17.0 150.0 5 5 5 TRUE
54 55.500 175.00 3.2 0.6000000 3.8 20.0 151.0 5 5 5 FALSE
55 1.400 12.50 NA 1.0766708 11.0 12.7 90.0 2 2 2 TRUE
56 0.060 1.00 8.1 2.2000000 10.3 3.5 NA 3 1 2 FALSE
57 0.900 2.60 11.0 2.3000000 13.3 4.5 60.0 2 1 2 FALSE
58 2.000 12.30 4.9 0.5000000 5.4 7.5 200.0 3 1 3 FALSE
59 0.104 2.50 13.2 2.6000000 15.8 2.3 46.0 3 2 2 FALSE
60 4.190 58.00 9.7 0.6000000 10.3 24.0 210.0 4 3 4 FALSE
61 3.500 3.90 12.8 6.6000000 19.4 3.0 14.0 2 1 1 FALSE
62 4.050 17.00 NA 0.4989381 NA 13.0 38.0 3 1 1 TRUE
> xgboostImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)
Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), :
Passed unrecognized parameters: verbose. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), :
Passed unrecognized parameters: verbose. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
1 6654.000 5712.00 2.100814 1.7996682 3.3 38.6 645.0 3 5 3
2 1.000 6.60 6.300000 2.0000000 8.3 4.5 42.0 3 1 3
3 3.385 44.50 10.897091 3.5961127 12.5 14.0 60.0 1 1 1
4 0.920 5.70 7.186167 0.8757253 16.5 NA 25.0 5 2 3
5 2547.000 4603.00 2.100000 1.8000000 3.9 69.0 624.0 3 5 4
6 10.550 179.50 9.100000 0.7000000 9.8 27.0 180.0 4 4 4
7 0.023 0.30 15.800000 3.9000000 19.7 19.0 35.0 1 1 1
8 160.000 169.00 5.200000 1.0000000 6.2 30.4 392.0 4 5 4
9 3.300 25.60 10.900000 3.6000000 14.5 28.0 63.0 1 2 1
10 52.160 440.00 8.300000 1.4000000 9.7 50.0 230.0 1 1 1
11 0.425 6.40 11.000000 1.5000000 12.5 7.0 112.0 5 4 4
12 465.000 423.00 3.200000 0.7000000 3.9 30.0 281.0 5 5 5
13 0.550 2.40 7.600000 2.7000000 10.3 NA NA 2 1 2
14 187.100 419.00 5.123134 1.7214588 3.1 40.0 365.0 5 5 5
15 0.075 1.20 6.300000 2.1000000 8.4 3.5 42.0 1 1 1
16 3.000 25.00 8.600000 0.0000000 8.6 50.0 28.0 2 2 2
17 0.785 3.50 6.600000 4.1000000 10.7 6.0 42.0 2 2 2
18 0.200 5.00 9.500000 1.2000000 10.7 10.4 120.0 2 2 2
19 1.410 17.50 4.800000 1.3000000 6.1 34.0 NA 1 2 1
20 60.000 81.00 12.000000 6.1000000 18.1 7.0 NA 1 1 1
21 529.000 680.00 2.100814 0.3000000 NA 28.0 400.0 5 5 5
22 27.660 115.00 3.300000 0.5000000 3.8 20.0 148.0 5 5 5
23 0.120 1.00 11.000000 3.4000000 14.4 3.9 16.0 3 1 2
24 207.000 406.00 6.285658 1.8088160 12.0 39.3 252.0 1 4 1
25 85.000 325.00 4.700000 1.5000000 6.2 41.0 310.0 1 3 1
26 36.330 119.50 3.301962 0.5030808 13.0 16.2 63.0 1 1 1
27 0.101 4.00 10.400000 3.4000000 13.8 9.0 28.0 5 1 3
28 1.040 5.50 7.400000 0.8000000 8.2 7.6 68.0 5 3 4
29 521.000 655.00 2.100000 0.8000000 2.9 46.0 336.0 5 5 5
30 100.000 157.00 4.835842 1.0328215 10.8 22.4 100.0 1 1 1
31 35.000 56.00 9.414713 4.6171999 NA 16.3 33.0 3 5 4
32 0.005 0.14 7.700000 1.4000000 9.1 2.6 21.5 5 2 4
33 0.010 0.25 17.900000 2.0000000 19.9 24.0 50.0 1 1 1
34 62.000 1320.00 6.100000 1.9000000 8.0 100.0 267.0 1 1 1
35 0.122 3.00 8.200000 2.4000000 10.6 NA 30.0 2 1 1
36 1.350 8.10 8.400000 2.8000000 11.2 NA 45.0 3 1 3
37 0.023 0.40 11.900000 1.3000000 13.2 3.2 19.0 4 1 3
38 0.048 0.33 10.800000 2.0000000 12.8 2.0 30.0 4 1 3
39 1.700 6.30 13.800000 5.6000000 19.4 5.0 12.0 2 1 1
40 3.500 10.80 14.300000 3.1000000 17.4 6.5 120.0 2 1 1
41 250.000 490.00 6.742025 1.0000000 NA 23.6 440.0 5 5 5
42 0.480 15.50 15.200000 1.8000000 17.0 12.0 140.0 2 2 2
43 10.000 115.00 10.000000 0.9000000 10.9 20.2 170.0 4 4 4
44 1.620 11.40 11.900000 1.8000000 13.7 13.0 17.0 2 1 2
45 192.000 180.00 6.500000 1.9000000 8.4 27.0 115.0 4 4 4
46 2.500 12.10 7.500000 0.9000000 8.4 18.0 31.0 5 5 5
47 4.288 39.20 7.402267 2.4006271 12.5 13.7 63.0 2 2 2
48 0.280 1.90 10.600000 2.6000000 13.2 4.7 21.0 3 1 3
49 4.235 50.40 7.400000 2.4000000 9.8 9.8 52.0 1 1 1
50 6.800 179.00 8.400000 1.2000000 9.6 29.0 164.0 2 3 2
51 0.750 12.30 5.700000 0.9000000 6.6 7.0 225.0 2 2 2
52 3.600 21.00 4.900000 0.5000000 5.4 6.0 225.0 3 2 3
53 14.830 98.20 10.641701 3.7860343 2.6 17.0 150.0 5 5 5
54 55.500 175.00 3.200000 0.6000000 3.8 20.0 151.0 5 5 5
55 1.400 12.50 5.010159 1.0766708 11.0 12.7 90.0 2 2 2
56 0.060 1.00 8.100000 2.2000000 10.3 3.5 NA 3 1 2
57 0.900 2.60 11.000000 2.3000000 13.3 4.5 60.0 2 1 2
58 2.000 12.30 4.900000 0.5000000 5.4 7.5 200.0 3 1 3
59 0.104 2.50 13.200000 2.6000000 15.8 2.3 46.0 3 2 2
60 4.190 58.00 9.700000 0.6000000 10.3 24.0 210.0 4 3 4
61 3.500 3.90 12.800000 6.6000000 19.4 3.0 14.0 2 1 1
62 4.050 17.00 5.988167 0.4989381 NA 13.0 38.0 3 1 1
Dream_imp NonD_imp
1 TRUE TRUE
2 FALSE FALSE
3 TRUE TRUE
4 TRUE TRUE
5 FALSE FALSE
6 FALSE FALSE
7 FALSE FALSE
8 FALSE FALSE
9 FALSE FALSE
10 FALSE FALSE
11 FALSE FALSE
12 FALSE FALSE
13 FALSE FALSE
14 TRUE TRUE
15 FALSE FALSE
16 FALSE FALSE
17 FALSE FALSE
18 FALSE FALSE
19 FALSE FALSE
20 FALSE FALSE
21 FALSE TRUE
22 FALSE FALSE
23 FALSE FALSE
24 TRUE TRUE
25 FALSE FALSE
26 TRUE TRUE
27 FALSE FALSE
28 FALSE FALSE
29 FALSE FALSE
30 TRUE TRUE
31 TRUE TRUE
32 FALSE FALSE
33 FALSE FALSE
34 FALSE FALSE
35 FALSE FALSE
36 FALSE FALSE
37 FALSE FALSE
38 FALSE FALSE
39 FALSE FALSE
40 FALSE FALSE
41 FALSE TRUE
42 FALSE FALSE
43 FALSE FALSE
44 FALSE FALSE
45 FALSE FALSE
46 FALSE FALSE
47 TRUE TRUE
48 FALSE FALSE
49 FALSE FALSE
50 FALSE FALSE
51 FALSE FALSE
52 FALSE FALSE
53 TRUE TRUE
54 FALSE FALSE
55 TRUE TRUE
56 FALSE FALSE
57 FALSE FALSE
58 FALSE FALSE
59 FALSE FALSE
60 FALSE FALSE
61 FALSE FALSE
62 TRUE TRUE
> xgboostImpute(Dream+NonD+Gest~BodyWgt+BrainWgt,data=sleep)
Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), :
Passed unrecognized parameters: verbose. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), :
Passed unrecognized parameters: verbose. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), :
Passed unrecognized parameters: verbose. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
1 6654.000 5712.00 2.100814 1.7996682 3.3 38.6 645.00000 3 5 3
2 1.000 6.60 6.300000 2.0000000 8.3 4.5 42.00000 3 1 3
3 3.385 44.50 10.897091 3.5961127 12.5 14.0 60.00000 1 1 1
4 0.920 5.70 7.186167 0.8757253 16.5 NA 25.00000 5 2 3
5 2547.000 4603.00 2.100000 1.8000000 3.9 69.0 624.00000 3 5 4
6 10.550 179.50 9.100000 0.7000000 9.8 27.0 180.00000 4 4 4
7 0.023 0.30 15.800000 3.9000000 19.7 19.0 35.00000 1 1 1
8 160.000 169.00 5.200000 1.0000000 6.2 30.4 392.00000 4 5 4
9 3.300 25.60 10.900000 3.6000000 14.5 28.0 63.00000 1 2 1
10 52.160 440.00 8.300000 1.4000000 9.7 50.0 230.00000 1 1 1
11 0.425 6.40 11.000000 1.5000000 12.5 7.0 112.00000 5 4 4
12 465.000 423.00 3.200000 0.7000000 3.9 30.0 281.00000 5 5 5
13 0.550 2.40 7.600000 2.7000000 10.3 NA 28.07713 2 1 2
14 187.100 419.00 5.123134 1.7214588 3.1 40.0 365.00000 5 5 5
15 0.075 1.20 6.300000 2.1000000 8.4 3.5 42.00000 1 1 1
16 3.000 25.00 8.600000 0.0000000 8.6 50.0 28.00000 2 2 2
17 0.785 3.50 6.600000 4.1000000 10.7 6.0 42.00000 2 2 2
18 0.200 5.00 9.500000 1.2000000 10.7 10.4 120.00000 2 2 2
19 1.410 17.50 4.800000 1.3000000 6.1 34.0 80.31452 1 2 1
20 60.000 81.00 12.000000 6.1000000 18.1 7.0 101.52599 1 1 1
21 529.000 680.00 2.100814 0.3000000 NA 28.0 400.00000 5 5 5
22 27.660 115.00 3.300000 0.5000000 3.8 20.0 148.00000 5 5 5
23 0.120 1.00 11.000000 3.4000000 14.4 3.9 16.00000 3 1 2
24 207.000 406.00 6.285658 1.8088160 12.0 39.3 252.00000 1 4 1
25 85.000 325.00 4.700000 1.5000000 6.2 41.0 310.00000 1 3 1
26 36.330 119.50 3.301962 0.5030808 13.0 16.2 63.00000 1 1 1
27 0.101 4.00 10.400000 3.4000000 13.8 9.0 28.00000 5 1 3
28 1.040 5.50 7.400000 0.8000000 8.2 7.6 68.00000 5 3 4
29 521.000 655.00 2.100000 0.8000000 2.9 46.0 336.00000 5 5 5
30 100.000 157.00 4.835842 1.0328215 10.8 22.4 100.00000 1 1 1
31 35.000 56.00 9.414713 4.6171999 NA 16.3 33.00000 3 5 4
32 0.005 0.14 7.700000 1.4000000 9.1 2.6 21.50000 5 2 4
33 0.010 0.25 17.900000 2.0000000 19.9 24.0 50.00000 1 1 1
34 62.000 1320.00 6.100000 1.9000000 8.0 100.0 267.00000 1 1 1
35 0.122 3.00 8.200000 2.4000000 10.6 NA 30.00000 2 1 1
36 1.350 8.10 8.400000 2.8000000 11.2 NA 45.00000 3 1 3
37 0.023 0.40 11.900000 1.3000000 13.2 3.2 19.00000 4 1 3
38 0.048 0.33 10.800000 2.0000000 12.8 2.0 30.00000 4 1 3
39 1.700 6.30 13.800000 5.6000000 19.4 5.0 12.00000 2 1 1
40 3.500 10.80 14.300000 3.1000000 17.4 6.5 120.00000 2 1 1
41 250.000 490.00 6.742025 1.0000000 NA 23.6 440.00000 5 5 5
42 0.480 15.50 15.200000 1.8000000 17.0 12.0 140.00000 2 2 2
43 10.000 115.00 10.000000 0.9000000 10.9 20.2 170.00000 4 4 4
44 1.620 11.40 11.900000 1.8000000 13.7 13.0 17.00000 2 1 2
45 192.000 180.00 6.500000 1.9000000 8.4 27.0 115.00000 4 4 4
46 2.500 12.10 7.500000 0.9000000 8.4 18.0 31.00000 5 5 5
47 4.288 39.20 7.402267 2.4006271 12.5 13.7 63.00000 2 2 2
48 0.280 1.90 10.600000 2.6000000 13.2 4.7 21.00000 3 1 3
49 4.235 50.40 7.400000 2.4000000 9.8 9.8 52.00000 1 1 1
50 6.800 179.00 8.400000 1.2000000 9.6 29.0 164.00000 2 3 2
51 0.750 12.30 5.700000 0.9000000 6.6 7.0 225.00000 2 2 2
52 3.600 21.00 4.900000 0.5000000 5.4 6.0 225.00000 3 2 3
53 14.830 98.20 10.641701 3.7860343 2.6 17.0 150.00000 5 5 5
54 55.500 175.00 3.200000 0.6000000 3.8 20.0 151.00000 5 5 5
55 1.400 12.50 5.010159 1.0766708 11.0 12.7 90.00000 2 2 2
56 0.060 1.00 8.100000 2.2000000 10.3 3.5 22.96904 3 1 2
57 0.900 2.60 11.000000 2.3000000 13.3 4.5 60.00000 2 1 2
58 2.000 12.30 4.900000 0.5000000 5.4 7.5 200.00000 3 1 3
59 0.104 2.50 13.200000 2.6000000 15.8 2.3 46.00000 3 2 2
60 4.190 58.00 9.700000 0.6000000 10.3 24.0 210.00000 4 3 4
61 3.500 3.90 12.800000 6.6000000 19.4 3.0 14.00000 2 1 1
62 4.050 17.00 5.988167 0.4989381 NA 13.0 38.00000 3 1 1
Dream_imp NonD_imp Gest_imp
1 TRUE TRUE FALSE
2 FALSE FALSE FALSE
3 TRUE TRUE FALSE
4 TRUE TRUE FALSE
5 FALSE FALSE FALSE
6 FALSE FALSE FALSE
7 FALSE FALSE FALSE
8 FALSE FALSE FALSE
9 FALSE FALSE FALSE
10 FALSE FALSE FALSE
11 FALSE FALSE FALSE
12 FALSE FALSE FALSE
13 FALSE FALSE TRUE
14 TRUE TRUE FALSE
15 FALSE FALSE FALSE
16 FALSE FALSE FALSE
17 FALSE FALSE FALSE
18 FALSE FALSE FALSE
19 FALSE FALSE TRUE
20 FALSE FALSE TRUE
21 FALSE TRUE FALSE
22 FALSE FALSE FALSE
23 FALSE FALSE FALSE
24 TRUE TRUE FALSE
25 FALSE FALSE FALSE
26 TRUE TRUE FALSE
27 FALSE FALSE FALSE
28 FALSE FALSE FALSE
29 FALSE FALSE FALSE
30 TRUE TRUE FALSE
31 TRUE TRUE FALSE
32 FALSE FALSE FALSE
33 FALSE FALSE FALSE
34 FALSE FALSE FALSE
35 FALSE FALSE FALSE
36 FALSE FALSE FALSE
37 FALSE FALSE FALSE
38 FALSE FALSE FALSE
39 FALSE FALSE FALSE
40 FALSE FALSE FALSE
41 FALSE TRUE FALSE
42 FALSE FALSE FALSE
43 FALSE FALSE FALSE
44 FALSE FALSE FALSE
45 FALSE FALSE FALSE
46 FALSE FALSE FALSE
47 TRUE TRUE FALSE
48 FALSE FALSE FALSE
49 FALSE FALSE FALSE
50 FALSE FALSE FALSE
51 FALSE FALSE FALSE
52 FALSE FALSE FALSE
53 TRUE TRUE FALSE
54 FALSE FALSE FALSE
55 TRUE TRUE FALSE
56 FALSE FALSE TRUE
57 FALSE FALSE FALSE
58 FALSE FALSE FALSE
59 FALSE FALSE FALSE
60 FALSE FALSE FALSE
61 FALSE FALSE FALSE
62 TRUE TRUE FALSE
>
> sleepx <- sleep
> sleepx$Pred <- as.factor(LETTERS[sleepx$Pred])
> sleepx$Pred[1] <- NA
> xgboostImpute(Pred~BodyWgt+BrainWgt,data=sleepx)
Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), :
Passed unrecognized parameters: num_class, verbose. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", :
Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
Error in prescreen.objective(objective) :
Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
Calls: xgboostImpute -> <Anonymous> -> prescreen.objective
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 6.2.6
Check: tests
Result: ERROR
Running ‘test_imputeRobust.R’
Running ‘tinytest.R’ [34s/103s]
Running the tests in ‘tests/tinytest.R’ failed.
Complete output:
> if ( requireNamespace("tinytest", quietly=TRUE) ){
+ tinytest::test_package("VIM")
+ }
Loading required package: colorspace
Loading required package: grid
VIM is ready to use.
Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
Attaching package: 'VIM'
The following object is masked from 'package:datasets':
sleep
test_IRMI_ordered.R........... 0 tests
test_IRMI_ordered.R........... 0 tests v1 v2 co v1 v2 co
-3.096425 -3.659158 2.000000 3.701561 3.974700 22.000000
test_IRMI_ordered.R........... 1 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 2 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-3.096425 -3.659158 2.000000 3.701561 3.974700 22.000000
Start: AIC=1384.09
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- m 1 1364.2 1382.2
- co 1 1365.4 1383.4
- c 4 1371.8 1383.8
<none> 1364.1 1384.1
- b 1 1366.8 1384.8
- v2 1 1367.4 1385.4
- v1 1 1367.8 1385.8
Step: AIC=1382.24
y ~ v1 + v2 + b + c + co
Df Deviance AIC
- co 1 1365.5 1381.5
- c 4 1371.8 1381.8
<none> 1364.2 1382.2
- b 1 1366.9 1382.9
- v2 1 1367.6 1383.6
- v1 1 1367.9 1383.9
Step: AIC=1381.55
y ~ v1 + v2 + b + c
Df Deviance AIC
- c 4 1373.1 1381.1
<none> 1365.5 1381.5
- b 1 1368.2 1382.2
- v2 1 1368.8 1382.8
- v1 1 1369.0 1383.0
Step: AIC=1381.06
y ~ v1 + v2 + b
Df Deviance AIC
<none> 1373.1 1381.1
- b 1 1375.5 1381.5
- v2 1 1376.2 1382.2
- v1 1 1376.7 1382.7
Start: AIC=1299.65
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1282.1 1294.1
- m 1 1279.7 1297.7
- co 1 1280.0 1298.0
<none> 1279.7 1299.7
- b 1 1301.4 1319.4
- v2 1 1305.2 1323.2
- v1 1 1309.3 1327.3
Step: AIC=1294.07
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- m 1 1282.2 1292.2
- co 1 1282.4 1292.4
<none> 1282.1 1294.1
- b 1 1303.7 1313.7
- v2 1 1307.6 1317.6
- v1 1 1311.9 1321.9
Step: AIC=1292.18
y ~ v1 + v2 + b + co
Df Deviance AIC
- co 1 1282.5 1290.5
<none> 1282.2 1292.2
- b 1 1303.9 1311.9
- v2 1 1307.8 1315.8
- v1 1 1312.2 1320.2
Step: AIC=1290.52
y ~ v1 + v2 + b
Df Deviance AIC
<none> 1282.5 1290.5
- b 1 1304.2 1310.2
- v2 1 1308.0 1314.0
- v1 1 1312.2 1318.2
Start: AIC=1299.87
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1282.0 1294.0
- co 1 1279.9 1297.9
- m 1 1280.0 1298.0
<none> 1279.9 1299.9
- v2 1 1304.4 1322.4
- b 1 1304.6 1322.6
- v1 1 1310.7 1328.7
Step: AIC=1293.99
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1282.0 1292.0
- m 1 1282.2 1292.2
<none> 1282.0 1294.0
- v2 1 1306.5 1316.5
- b 1 1306.7 1316.7
- v1 1 1312.9 1322.9
Step: AIC=1292.03
y ~ v1 + v2 + m + b
Df Deviance AIC
- m 1 1282.2 1290.2
<none> 1282.0 1292.0
- v2 1 1306.5 1314.5
- b 1 1306.7 1314.7
- v1 1 1312.9 1320.9
Step: AIC=1290.21
y ~ v1 + v2 + b
Df Deviance AIC
<none> 1282.2 1290.2
- v2 1 1306.8 1312.8
- b 1 1307.0 1313.0
- v1 1 1313.3 1319.3
test_IRMI_ordered.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 4 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-3.096425 -3.659158 2.000000 3.701561 3.974700 22.000000
Start: AIC=1384.09
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- m 1 1364.2 1382.2
- co 1 1365.4 1383.4
- c 4 1371.8 1383.8
<none> 1364.1 1384.1
- b 1 1366.8 1384.8
- v2 1 1367.4 1385.4
- v1 1 1367.8 1385.8
Step: AIC=1382.24
y ~ v1 + v2 + b + c + co
Df Deviance AIC
- co 1 1365.5 1381.5
- c 4 1371.8 1381.8
<none> 1364.2 1382.2
- b 1 1366.9 1382.9
- v2 1 1367.6 1383.6
- v1 1 1367.9 1383.9
Step: AIC=1381.55
y ~ v1 + v2 + b + c
Df Deviance AIC
- c 4 1373.1 1381.1
<none> 1365.5 1381.5
- b 1 1368.2 1382.2
- v2 1 1368.8 1382.8
- v1 1 1369.0 1383.0
Step: AIC=1381.06
y ~ v1 + v2 + b
Df Deviance AIC
<none> 1373.1 1381.1
- b 1 1375.5 1381.5
- v2 1 1376.2 1382.2
- v1 1 1376.7 1382.7
Start: AIC=1299.65
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1282.1 1294.1
- m 1 1279.7 1297.7
- co 1 1280.0 1298.0
<none> 1279.7 1299.7
- b 1 1301.4 1319.4
- v2 1 1305.2 1323.2
- v1 1 1309.3 1327.3
Step: AIC=1294.07
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- m 1 1282.2 1292.2
- co 1 1282.4 1292.4
<none> 1282.1 1294.1
- b 1 1303.7 1313.7
- v2 1 1307.6 1317.6
- v1 1 1311.9 1321.9
Step: AIC=1292.18
y ~ v1 + v2 + b + co
Df Deviance AIC
- co 1 1282.5 1290.5
<none> 1282.2 1292.2
- b 1 1303.9 1311.9
- v2 1 1307.8 1315.8
- v1 1 1312.2 1320.2
Step: AIC=1290.52
y ~ v1 + v2 + b
Df Deviance AIC
<none> 1282.5 1290.5
- b 1 1304.2 1310.2
- v2 1 1308.0 1314.0
- v1 1 1312.2 1318.2
Start: AIC=1299.87
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1282.0 1294.0
- co 1 1279.9 1297.9
- m 1 1280.0 1298.0
<none> 1279.9 1299.9
- v2 1 1304.4 1322.4
- b 1 1304.6 1322.6
- v1 1 1310.7 1328.7
Step: AIC=1293.99
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1282.0 1292.0
- m 1 1282.2 1292.2
<none> 1282.0 1294.0
- v2 1 1306.5 1316.5
- b 1 1306.7 1316.7
- v1 1 1312.9 1322.9
Step: AIC=1292.03
y ~ v1 + v2 + m + b
Df Deviance AIC
- m 1 1282.2 1290.2
<none> 1282.0 1292.0
- v2 1 1306.5 1314.5
- b 1 1306.7 1314.7
- v1 1 1312.9 1320.9
Step: AIC=1290.21
y ~ v1 + v2 + b
Df Deviance AIC
<none> 1282.2 1290.2
- v2 1 1306.8 1312.8
- b 1 1307.0 1313.0
- v1 1 1313.3 1319.3
test_IRMI_ordered.R........... 5 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 6 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-3.096425 -3.659158 2.000000 3.701561 3.974700 22.000000
test_IRMI_ordered.R........... 7 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 8 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 9 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-3.096425 -3.659158 2.000000 3.701561 3.974700 22.000000
test_IRMI_ordered.R........... 10 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 11 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-3.096425 -3.659158 2.000000 3.701561 3.974700 22.000000
test_IRMI_ordered.R........... 12 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 13 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m26.1s<1b>[0m
test_aggFunctions.R........... 0 tests kNN ordered
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 1 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 5 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 6 tests <1b>[0;32mOK<1b>[0m <1b>[0;36m94ms<1b>[0m
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
test_data_frame.R............. 0 tests
test_data_frame.R............. 0 tests b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 0 tests b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 0 tests
test_data_frame.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 2 tests <1b>[0;32mOK<1b>[0m b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 2 tests <1b>[0;32mOK<1b>[0m b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 3 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.6s<1b>[0m
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 0 tests x y x y
-3.112492 -2.320055 2.715151 2.696512
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 0 tests x y x y
-3.112492 -2.320055 2.715151 2.696512
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 2 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.3s<1b>[0m
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
Missings in variables:
Variable Count
NonD 14
Dream 12
Sleep 4
Span 4
Gest 4
test_graphics.R............... 0 tests
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Span Gest Pred Exp
0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Span Gest Pred
1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp
0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000
Exp Danger
5.000 5.000
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
Imputed missings in variables:
Variable Count
NonD 14
Dream 12
Sleep 4
Span 4
Gest 4
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 2 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 2 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 2 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m Exp Exp
1 5
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 4 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 4 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 5 tests <1b>[0;32mOK<1b>[0m Ca Bi Ca Bi
1.10e+02 6.00e-03 4.17e+04 3.89e+00
Ca As Ca As
110.0 0.1 41700.0 30.7
test_graphics.R............... 5 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 8 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 8 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 9 tests <1b>[0;32mOK<1b>[0m Bi Bi
0.006 3.890
As As
0.1 30.7
test_graphics.R............... 9 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 10 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 10 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 10 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 11 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep BodyWgt BrainWgt Dream Sleep
0.005 0.140 0.000 2.600 6654.000 5712.000 6.600 19.900
BodyWgt BrainWgt NonD Sleep BodyWgt BrainWgt NonD Sleep
0.005 0.140 2.100 2.600 6654.000 5712.000 17.900 19.900
BodyWgt BrainWgt NonD Dream BodyWgt BrainWgt NonD Dream
0.005 0.140 2.100 0.000 6654.000 5712.000 17.900 6.600
test_graphics.R............... 11 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 11 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 12 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 12 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 12 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 13 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep Span Gest BodyWgt BrainWgt
0.005 0.140 0.000 2.600 2.000 12.000 6654.000 5712.000
Dream Sleep Span Gest
6.600 19.900 100.000 645.000
BodyWgt BrainWgt NonD Sleep Span Gest BodyWgt BrainWgt
0.005 0.140 2.100 2.600 2.000 12.000 6654.000 5712.000
NonD Sleep Span Gest
17.900 19.900 100.000 645.000
BodyWgt BrainWgt NonD Dream Span Gest BodyWgt BrainWgt
0.005 0.140 2.100 0.000 2.000 12.000 6654.000 5712.000
NonD Dream Span Gest
17.900 6.600 100.000 645.000
BodyWgt BrainWgt NonD Dream Sleep Gest BodyWgt BrainWgt
0.005 0.140 2.100 0.000 2.600 12.000 6654.000 5712.000
NonD Dream Sleep Gest
17.900 6.600 19.900 645.000
BodyWgt BrainWgt NonD Dream Sleep Span BodyWgt BrainWgt
0.005 0.140 2.100 0.000 2.600 2.000 6654.000 5712.000
NonD Dream Sleep Span
17.900 6.600 19.900 100.000
test_graphics.R............... 13 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 13 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 14 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 14 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 15 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Span Gest Pred Exp
0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Span Gest Pred
1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp
0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000
Exp Danger
5.000 5.000
test_graphics.R............... 15 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 16 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 17 tests <1b>[0;32mOK<1b>[0m Al_XRF Ca_XRF Fe_XRF K_XRF Mg_XRF Mn_XRF Na_XRF P_XRF Si_XRF Ti_XRF Al_XRF
2.920 0.030 0.590 0.360 0.120 0.015 0.080 0.004 17.050 0.053 12.080
Ca_XRF Fe_XRF K_XRF Mg_XRF Mn_XRF Na_XRF P_XRF Si_XRF Ti_XRF
6.760 12.350 5.240 7.320 0.356 4.870 0.589 40.270 1.900
test_graphics.R............... 17 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 18 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 19 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 20 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 21 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 22 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 22 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 22 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 23 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep BodyWgt BrainWgt Dream Sleep
0.005 0.140 0.000 2.600 6654.000 5712.000 6.600 19.900
BodyWgt BrainWgt NonD Sleep BodyWgt BrainWgt NonD Sleep
0.005 0.140 2.100 2.600 6654.000 5712.000 17.900 19.900
BodyWgt BrainWgt NonD Dream BodyWgt BrainWgt NonD Dream
0.005 0.140 2.100 0.000 6654.000 5712.000 17.900 6.600
test_graphics.R............... 23 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 23 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 24 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 24 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 24 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 25 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 25 tests <1b>[0;32mOK<1b>[0m Exp Exp
1 5
test_graphics.R............... 25 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 26 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 27 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 28 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 29 tests <1b>[0;32mOK<1b>[0m CaO CaO
-1.3010300 0.9758911
test_graphics.R............... 30 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 30 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 31 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 31 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 32 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m29.7s<1b>[0m
hotdeck
test_hotdeck.R................ 0 tests
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 1 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 1 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 1 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 2 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 2 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 3 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 5 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 5 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 6 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 6 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 7 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 7 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 8 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 8 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 9 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 10 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 10 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 10 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 11 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 12 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 13 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 14 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 15 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 15 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 15 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 16 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 17 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 18 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 19 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.3s<1b>[0m
test_impNA.R.................. 0 tests
test_impNA.R.................. 0 tests
test_impNA.R.................. 0 tests
test_impNA.R.................. 0 tests BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
0.005 0.140 0.000 2.900 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
0.005 0.140 2.100 2.900 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
Exp Danger
5.000 5.000
test_impNA.R.................. 0 tests
test_impNA.R.................. 1 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 2 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 2 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 3 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 4 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.3s<1b>[0m
impPCA
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
Iterations: 4
test_impPCA.R................. 0 tests
test_impPCA.R................. 1 tests <1b>[0;32mOK<1b>[0m
Iterations: 4
test_impPCA.R................. 1 tests <1b>[0;32mOK<1b>[0m
test_impPCA.R................. 2 tests <1b>[0;32mOK<1b>[0m
Iterations: 0
test_impPCA.R................. 2 tests <1b>[0;32mOK<1b>[0m
test_impPCA.R................. 3 tests <1b>[0;32mOK<1b>[0m
Iterations: 0
test_impPCA.R................. 3 tests <1b>[0;32mOK<1b>[0m
test_impPCA.R................. 4 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.6s<1b>[0m
test_irmi_types.R............. 0 tests z z
-0.3308959 2.0121804
test_irmi_types.R............. 0 tests
test_irmi_types.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 3 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 5 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m num1 num2 num3 num1 num2 num3
-3.087610 -4.001394 -3.237928 3.349508 3.615635 2.820386
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 7 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 8 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.9s<1b>[0m
test_kNN.R.................... 0 tests kNN general
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests y y
1 6
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests y y
1 6
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m Detected as categorical variable:
x,x_imp,y_imp
Detected as ordinal variable:
Detected as numerical variable:
y
0 items ofvariable:x imputed
6items ofvariable:y imputed
Time difference of 0.06897974 secs
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m y z
1.000000 1.000000
RandomVariableForImputation y
-1.372898 6.000000
z RandomVariableForImputation
6.000000 2.212962
z RandomVariableForImputation
1.000000 -1.372898
z RandomVariableForImputation
6.000000 2.212962
y z
1.000000 1.000000
RandomVariableForImputation y
-1.372898 6.000000
z RandomVariableForImputation
6.000000 2.212962
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
z y2
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
y z
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 z23
-0.2369185 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 z23
1.0393184 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 RandomVariableForImputation
1.0000000 -1.5949014
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 y23
1.0393184 6.0000000
z23 RandomVariableForImputation
6.0000000 2.0511976
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 4 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 4 tests <1b>[0;32mOK<1b>[0m z z
1 6
test_kNN.R.................... 4 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 5 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 5 tests <1b>[0;32mOK<1b>[0m z z
1 6
test_kNN.R.................... 5 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 6 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 7 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 7 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 8 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m z z
1 6
y y
1 6
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 10 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 11 tests <1b>[0;32mOK<1b>[0m z z
1 6
y y
1 5
test_kNN.R.................... 11 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 12 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
y z m
1.0000000 1.0000000 -0.2369185
yrandomForestFeature y z
1.9920333 6.0000000 6.0000000
m yrandomForestFeature
1.0393184 4.7261667
y z m
1.0000000 1.0000000 -0.2369185
mrandomForestFeature y z
-0.0470906 6.0000000 6.0000000
m mrandomForestFeature
1.0393184 0.8751475
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 14 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
yrandomForestFeature yrandomForestFeature
2.010567 4.776767
mrandomForestFeature mrandomForestFeature
-0.03546328 0.88466971
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 16 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 2.079833 6.000000
yrandomForestFeature
4.979000
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 18 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 1.992700 6.000000
yrandomForestFeature
5.023967
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 20 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m y y
1 6
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m y y
1 6
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m Detected as categorical variable:
x,x_imp,y_imp
Detected as ordinal variable:
Detected as numerical variable:
y
0 items ofvariable:x imputed
6items ofvariable:y imputed
Time difference of 0.2428172 secs
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m y z
1.000000 1.000000
RandomVariableForImputation y
-1.130797 6.000000
z RandomVariableForImputation
6.000000 1.380325
z RandomVariableForImputation
1.000000 -1.130797
z RandomVariableForImputation
6.000000 1.380325
y z
1.000000 1.000000
RandomVariableForImputation y
-1.130797 6.000000
z RandomVariableForImputation
6.000000 1.380325
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
z y2
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
y z
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 z23
-0.2369185 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 z23
1.0393184 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 RandomVariableForImputation
1.0000000 -1.6220983
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 y23
1.0393184 6.0000000
z23 RandomVariableForImputation
6.0000000 1.1502594
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 25 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 25 tests <1b>[0;32mOK<1b>[0m z z
1 6
test_kNN.R.................... 25 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 26 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 26 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 27 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
y z m
1.0000000 1.0000000 -0.2369185
yrandomForestFeature y z
2.1596667 6.0000000 6.0000000
m yrandomForestFeature
1.0393184 4.6204667
y z m
1.0000000 1.0000000 -0.2369185
mrandomForestFeature y z
-0.0639622 6.0000000 6.0000000
m mrandomForestFeature
1.0393184 0.8543806
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 29 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
yrandomForestFeature yrandomForestFeature
2.107267 4.717967
mrandomForestFeature mrandomForestFeature
-0.04484091 0.86808182
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 31 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 2.009067 6.000000
yrandomForestFeature
4.966033
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 33 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 2.000833 6.000000
yrandomForestFeature
4.953033
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 35 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 36 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 36 tests <1b>[0;32mOK<1b>[0m col2 col3 col2 col3
3 5 4 6
test_kNN.R.................... 37 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 37 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 37 tests <1b>[0;32mOK<1b>[0m col2 col3 col2 col3
3 5 4 6
test_kNN.R.................... 38 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 38 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 38 tests <1b>[0;32mOK<1b>[0m col2 col3 col2 col3
3 5 4 6
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m weighted catFun without missings in the distance variables
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m x y x y
1 1 10 10
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m weighted catFun with missings in the distance variables
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m x y x y
1 1 10 10
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m x y x y
1 1 10 10
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m10.9s<1b>[0m
test_kNN_exact.R.............. 0 tests kNN exact results
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 0 tests Detected as categorical variable:
Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp
Detected as ordinal variable:
ord
Detected as numerical variable:
X1,X2,ClassNum,Row,Row2
0 items ofvariable:Class imputed
0 items ofvariable:X1 imputed
X1 X1
1 1
2items ofvariable:X2 imputed
0 items ofvariable:ClassNum imputed
0 items ofvariable:Row imputed
0 items ofvariable:Row2 imputed
0 items ofvariable:ord imputed
Time difference of 0.1785314 secs
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 2 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 2 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 3 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 4 tests <1b>[0;32mOK<1b>[0m X1 ClassNum X1 ClassNum
1 1 1 2
test_kNN_exact.R.............. 4 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 5 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 7 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 8 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 8 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 9 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 10 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 10 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 11 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 13 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 14 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 14 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 15 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 16 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 16 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 17 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m Detected as categorical variable:
Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp
Detected as ordinal variable:
ord
Detected as numerical variable:
X1,X2,ClassNum,Row,Row2
0 items ofvariable:Class imputed
0 items ofvariable:X1 imputed
X1 X1
1 1
2items ofvariable:X2 imputed
0 items ofvariable:ClassNum imputed
0 items ofvariable:Row imputed
0 items ofvariable:Row2 imputed
0 items ofvariable:ord imputed
Time difference of 0.4723027 secs
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 19 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 20 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 20 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 21 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 22 tests <1b>[0;32mOK<1b>[0m X1 ClassNum X1 ClassNum
1 1 1 2
test_kNN_exact.R.............. 22 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 23 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 25 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 26 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 26 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 27 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 28 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 28 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 29 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 31 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 32 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 32 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 33 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 34 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 34 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 35 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 36 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.6s<1b>[0m
test_kNN_iqr.R................ 0 tests kNN iqr
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests y z y z
-1.914359 -2.888921 2.307978 2.649167
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 1 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.1s<1b>[0m
test_kNN_ordered.R............ 0 tests kNN ordered
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 0 tests y z y z
1 1 6 6
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 1 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 2 tests <1b>[0;32mOK<1b>[0m y z y z
1 1 6 6
test_kNN_ordered.R............ 2 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 3 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 4 tests <1b>[0;32mOK<1b>[0m y z y z
1 1 6 6
test_kNN_ordered.R............ 4 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 5 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 6 tests <1b>[0;32mOK<1b>[0m y z y z
1 1 6 6
test_kNN_ordered.R............ 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 7 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 8 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.3s<1b>[0m
test_matchImpute.R............ 0 tests matchImpute general
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 1 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 1 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 2 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 3 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 4 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 5 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 5 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 6 tests <1b>[0;32mOK<1b>[0m <1b>[0;36m89ms<1b>[0m
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 1 tests <1b>[0;32mOK<1b>[0m No missings in x.
test_rangerImpute.R........... 1 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.4s<1b>[0m
test_regressionImp.R.......... 0 tests
test_regressionImp.R.......... 0 tests
test_regressionImp.R.......... 1 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 1 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 2 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 2 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 3 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 3 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;36m70ms<1b>[0m
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 1 tests <1b>[0;32mOK<1b>[0m No missings in x.
test_xgboostImpute.R.......... 1 tests <1b>[0;32mOK<1b>[0m
test_xgboostImpute.R.......... 2 tests <1b>[0;32mOK<1b>[0m Error in process.y.margin.and.objective(y, base_margin, objective, params) :
Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
Calls: <Anonymous> ... xgboostImpute -> <Anonymous> -> process.y.margin.and.objective
In addition: There were 20 warnings (use warnings() to see them)
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 6.2.6
Check: tests
Result: ERROR
Running ‘test_imputeRobust.R’
Running ‘tinytest.R’ [35s/94s]
Running the tests in ‘tests/tinytest.R’ failed.
Complete output:
> if ( requireNamespace("tinytest", quietly=TRUE) ){
+ tinytest::test_package("VIM")
+ }
Loading required package: colorspace
Loading required package: grid
VIM is ready to use.
Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
Attaching package: 'VIM'
The following object is masked from 'package:datasets':
sleep
test_IRMI_ordered.R........... 0 tests
test_IRMI_ordered.R........... 0 tests v1 v2 co v1 v2 co
-2.791516 -3.029305 1.000000 2.960098 3.162890 20.000000
test_IRMI_ordered.R........... 1 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 2 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-2.791516 -3.029305 1.000000 2.960098 3.162890 20.000000
Start: AIC=1389.94
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1371.6 1383.6
- co 1 1369.9 1387.9
- v2 1 1370.0 1388.0
- b 1 1371.6 1389.6
<none> 1369.9 1389.9
- m 1 1372.7 1390.7
- v1 1 1379.4 1397.4
Step: AIC=1383.57
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1371.6 1381.6
- v2 1 1371.7 1381.7
- b 1 1373.4 1383.4
<none> 1371.6 1383.6
- m 1 1374.3 1384.3
- v1 1 1381.1 1391.1
Step: AIC=1381.57
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1371.7 1379.7
- b 1 1373.4 1381.4
<none> 1371.6 1381.6
- m 1 1374.3 1382.3
- v1 1 1381.1 1389.1
Step: AIC=1379.65
y ~ v1 + m + b
Df Deviance AIC
- b 1 1373.5 1379.5
<none> 1371.7 1379.7
- m 1 1374.4 1380.4
- v1 1 1381.2 1387.2
Step: AIC=1379.47
y ~ v1 + m
Df Deviance AIC
<none> 1373.5 1379.5
- m 1 1376.5 1380.5
- v1 1 1382.7 1386.7
Start: AIC=1280.77
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1264.3 1276.3
- co 1 1260.9 1278.9
- v2 1 1261.5 1279.5
<none> 1260.8 1280.8
- b 1 1263.5 1281.5
- m 1 1282.9 1300.9
- v1 1 1358.4 1376.4
Step: AIC=1276.34
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1264.5 1274.5
- v2 1 1264.9 1274.9
<none> 1264.3 1276.3
- b 1 1267.3 1277.3
- m 1 1285.7 1295.7
- v1 1 1360.8 1370.8
Step: AIC=1274.45
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1265.0 1273.0
<none> 1264.5 1274.5
- b 1 1267.4 1275.4
- m 1 1285.8 1293.8
- v1 1 1360.8 1368.8
Step: AIC=1273.01
y ~ v1 + m + b
Df Deviance AIC
<none> 1265.0 1273.0
- b 1 1267.8 1273.8
- m 1 1286.0 1292.0
- v1 1 1361.8 1367.8
Start: AIC=1279.24
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1263.1 1275.1
- co 1 1259.3 1277.3
- v2 1 1259.9 1277.9
<none> 1259.2 1279.2
- b 1 1264.1 1282.1
- m 1 1280.9 1298.9
- v1 1 1357.1 1375.1
Step: AIC=1275.07
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1263.1 1273.1
- v2 1 1263.7 1273.7
<none> 1263.1 1275.1
- b 1 1268.2 1278.2
- m 1 1283.9 1293.9
- v1 1 1359.9 1369.9
Step: AIC=1273.12
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1263.7 1271.7
<none> 1263.1 1273.1
- b 1 1268.2 1276.2
- m 1 1284.0 1292.0
- v1 1 1359.9 1367.9
Step: AIC=1271.69
y ~ v1 + m + b
Df Deviance AIC
<none> 1263.7 1271.7
- b 1 1268.6 1274.6
- m 1 1284.2 1290.2
- v1 1 1360.9 1366.9
Start: AIC=1278.19
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1262.9 1274.9
- co 1 1258.2 1276.2
- v2 1 1258.5 1276.5
<none> 1258.2 1278.2
- b 1 1265.2 1283.2
- m 1 1279.1 1297.1
- v1 1 1356.0 1374.0
Step: AIC=1274.89
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1262.9 1272.9
- v2 1 1263.1 1273.1
<none> 1262.9 1274.9
- b 1 1270.2 1280.2
- m 1 1282.9 1292.9
- v1 1 1359.6 1369.6
Step: AIC=1272.89
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1263.1 1271.1
<none> 1262.9 1272.9
- b 1 1270.2 1278.2
- m 1 1282.9 1290.9
- v1 1 1359.6 1367.6
Step: AIC=1271.12
y ~ v1 + m + b
Df Deviance AIC
<none> 1263.1 1271.1
- b 1 1270.4 1276.4
- m 1 1283.0 1289.0
- v1 1 1360.1 1366.1
Start: AIC=1276.92
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1262.3 1274.3
- co 1 1256.9 1274.9
- v2 1 1257.2 1275.2
<none> 1256.9 1276.9
- b 1 1266.2 1284.2
- m 1 1279.0 1297.0
- v1 1 1353.6 1371.6
Step: AIC=1274.29
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1262.3 1272.3
- v2 1 1262.5 1272.5
<none> 1262.3 1274.3
- b 1 1272.0 1282.0
- m 1 1283.3 1293.3
- v1 1 1357.4 1367.4
Step: AIC=1272.29
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1262.5 1270.5
<none> 1262.3 1272.3
- b 1 1272.0 1280.0
- m 1 1283.3 1291.3
- v1 1 1357.4 1365.4
Step: AIC=1270.54
y ~ v1 + m + b
Df Deviance AIC
<none> 1262.5 1270.5
- b 1 1272.1 1278.1
- m 1 1283.4 1289.4
- v1 1 1357.9 1363.9
Start: AIC=1276.43
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1261.9 1273.9
- co 1 1256.4 1274.4
- v2 1 1256.6 1274.6
<none> 1256.4 1276.4
- b 1 1268.4 1286.4
- m 1 1279.2 1297.2
- v1 1 1350.0 1368.0
Step: AIC=1273.91
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1261.9 1271.9
- v2 1 1262.0 1272.0
<none> 1261.9 1273.9
- b 1 1274.5 1284.5
- m 1 1283.6 1293.6
- v1 1 1354.2 1364.2
Step: AIC=1271.91
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1262.0 1270.0
<none> 1261.9 1271.9
- b 1 1274.6 1282.6
- m 1 1283.6 1291.6
- v1 1 1354.3 1362.3
Step: AIC=1270.04
y ~ v1 + m + b
Df Deviance AIC
<none> 1262.0 1270.0
- b 1 1274.6 1280.6
- m 1 1283.6 1289.6
- v1 1 1354.6 1360.6
Start: AIC=1277.15
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1261.8 1273.8
- co 1 1257.2 1275.2
- v2 1 1257.3 1275.3
<none> 1257.2 1277.2
- b 1 1270.0 1288.0
- m 1 1279.5 1297.5
- v1 1 1350.0 1368.0
Step: AIC=1273.8
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1261.8 1271.8
- v2 1 1261.9 1271.9
<none> 1261.8 1273.8
- b 1 1275.4 1285.4
- m 1 1283.2 1293.2
- v1 1 1353.6 1363.6
Step: AIC=1271.8
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1261.9 1269.9
<none> 1261.8 1271.8
- b 1 1275.4 1283.4
- m 1 1283.2 1291.2
- v1 1 1353.7 1361.7
Step: AIC=1269.91
y ~ v1 + m + b
Df Deviance AIC
<none> 1261.9 1269.9
- b 1 1275.4 1281.4
- m 1 1283.2 1289.2
- v1 1 1354.0 1360.0
test_IRMI_ordered.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 4 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-2.791516 -3.029305 1.000000 2.960098 3.162890 20.000000
Start: AIC=1389.94
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1371.6 1383.6
- co 1 1369.9 1387.9
- v2 1 1370.0 1388.0
- b 1 1371.6 1389.6
<none> 1369.9 1389.9
- m 1 1372.7 1390.7
- v1 1 1379.4 1397.4
Step: AIC=1383.57
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1371.6 1381.6
- v2 1 1371.7 1381.7
- b 1 1373.4 1383.4
<none> 1371.6 1383.6
- m 1 1374.3 1384.3
- v1 1 1381.1 1391.1
Step: AIC=1381.57
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1371.7 1379.7
- b 1 1373.4 1381.4
<none> 1371.6 1381.6
- m 1 1374.3 1382.3
- v1 1 1381.1 1389.1
Step: AIC=1379.65
y ~ v1 + m + b
Df Deviance AIC
- b 1 1373.5 1379.5
<none> 1371.7 1379.7
- m 1 1374.4 1380.4
- v1 1 1381.2 1387.2
Step: AIC=1379.47
y ~ v1 + m
Df Deviance AIC
<none> 1373.5 1379.5
- m 1 1376.5 1380.5
- v1 1 1382.7 1386.7
Start: AIC=1280.77
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1264.3 1276.3
- co 1 1260.9 1278.9
- v2 1 1261.5 1279.5
<none> 1260.8 1280.8
- b 1 1263.5 1281.5
- m 1 1282.9 1300.9
- v1 1 1358.4 1376.4
Step: AIC=1276.34
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1264.5 1274.5
- v2 1 1264.9 1274.9
<none> 1264.3 1276.3
- b 1 1267.3 1277.3
- m 1 1285.7 1295.7
- v1 1 1360.8 1370.8
Step: AIC=1274.45
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1265.0 1273.0
<none> 1264.5 1274.5
- b 1 1267.4 1275.4
- m 1 1285.8 1293.8
- v1 1 1360.8 1368.8
Step: AIC=1273.01
y ~ v1 + m + b
Df Deviance AIC
<none> 1265.0 1273.0
- b 1 1267.8 1273.8
- m 1 1286.0 1292.0
- v1 1 1361.8 1367.8
Start: AIC=1279.24
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1263.1 1275.1
- co 1 1259.3 1277.3
- v2 1 1259.9 1277.9
<none> 1259.2 1279.2
- b 1 1264.1 1282.1
- m 1 1280.9 1298.9
- v1 1 1357.1 1375.1
Step: AIC=1275.07
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1263.1 1273.1
- v2 1 1263.7 1273.7
<none> 1263.1 1275.1
- b 1 1268.2 1278.2
- m 1 1283.9 1293.9
- v1 1 1359.9 1369.9
Step: AIC=1273.12
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1263.7 1271.7
<none> 1263.1 1273.1
- b 1 1268.2 1276.2
- m 1 1284.0 1292.0
- v1 1 1359.9 1367.9
Step: AIC=1271.69
y ~ v1 + m + b
Df Deviance AIC
<none> 1263.7 1271.7
- b 1 1268.6 1274.6
- m 1 1284.2 1290.2
- v1 1 1360.9 1366.9
Start: AIC=1278.19
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1262.9 1274.9
- co 1 1258.2 1276.2
- v2 1 1258.5 1276.5
<none> 1258.2 1278.2
- b 1 1265.2 1283.2
- m 1 1279.1 1297.1
- v1 1 1356.0 1374.0
Step: AIC=1274.89
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1262.9 1272.9
- v2 1 1263.1 1273.1
<none> 1262.9 1274.9
- b 1 1270.2 1280.2
- m 1 1282.9 1292.9
- v1 1 1359.6 1369.6
Step: AIC=1272.89
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1263.1 1271.1
<none> 1262.9 1272.9
- b 1 1270.2 1278.2
- m 1 1282.9 1290.9
- v1 1 1359.6 1367.6
Step: AIC=1271.12
y ~ v1 + m + b
Df Deviance AIC
<none> 1263.1 1271.1
- b 1 1270.4 1276.4
- m 1 1283.0 1289.0
- v1 1 1360.1 1366.1
Start: AIC=1276.92
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1262.3 1274.3
- co 1 1256.9 1274.9
- v2 1 1257.2 1275.2
<none> 1256.9 1276.9
- b 1 1266.2 1284.2
- m 1 1279.0 1297.0
- v1 1 1353.6 1371.6
Step: AIC=1274.29
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1262.3 1272.3
- v2 1 1262.5 1272.5
<none> 1262.3 1274.3
- b 1 1272.0 1282.0
- m 1 1283.3 1293.3
- v1 1 1357.4 1367.4
Step: AIC=1272.29
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1262.5 1270.5
<none> 1262.3 1272.3
- b 1 1272.0 1280.0
- m 1 1283.3 1291.3
- v1 1 1357.4 1365.4
Step: AIC=1270.54
y ~ v1 + m + b
Df Deviance AIC
<none> 1262.5 1270.5
- b 1 1272.1 1278.1
- m 1 1283.4 1289.4
- v1 1 1357.9 1363.9
Start: AIC=1276.43
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1261.9 1273.9
- co 1 1256.4 1274.4
- v2 1 1256.6 1274.6
<none> 1256.4 1276.4
- b 1 1268.4 1286.4
- m 1 1279.2 1297.2
- v1 1 1350.0 1368.0
Step: AIC=1273.91
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1261.9 1271.9
- v2 1 1262.0 1272.0
<none> 1261.9 1273.9
- b 1 1274.5 1284.5
- m 1 1283.6 1293.6
- v1 1 1354.2 1364.2
Step: AIC=1271.91
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1262.0 1270.0
<none> 1261.9 1271.9
- b 1 1274.6 1282.6
- m 1 1283.6 1291.6
- v1 1 1354.3 1362.3
Step: AIC=1270.04
y ~ v1 + m + b
Df Deviance AIC
<none> 1262.0 1270.0
- b 1 1274.6 1280.6
- m 1 1283.6 1289.6
- v1 1 1354.6 1360.6
Start: AIC=1277.15
y ~ v1 + v2 + m + b + c + co
Df Deviance AIC
- c 4 1261.8 1273.8
- co 1 1257.2 1275.2
- v2 1 1257.3 1275.3
<none> 1257.2 1277.2
- b 1 1270.0 1288.0
- m 1 1279.5 1297.5
- v1 1 1350.0 1368.0
Step: AIC=1273.8
y ~ v1 + v2 + m + b + co
Df Deviance AIC
- co 1 1261.8 1271.8
- v2 1 1261.9 1271.9
<none> 1261.8 1273.8
- b 1 1275.4 1285.4
- m 1 1283.2 1293.2
- v1 1 1353.6 1363.6
Step: AIC=1271.8
y ~ v1 + v2 + m + b
Df Deviance AIC
- v2 1 1261.9 1269.9
<none> 1261.8 1271.8
- b 1 1275.4 1283.4
- m 1 1283.2 1291.2
- v1 1 1353.7 1361.7
Step: AIC=1269.91
y ~ v1 + m + b
Df Deviance AIC
<none> 1261.9 1269.9
- b 1 1275.4 1281.4
- m 1 1283.2 1289.2
- v1 1 1354.0 1360.0
test_IRMI_ordered.R........... 5 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 6 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-2.791516 -3.029305 1.000000 2.960098 3.162890 20.000000
test_IRMI_ordered.R........... 7 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 8 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 9 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-2.791516 -3.029305 1.000000 2.960098 3.162890 20.000000
test_IRMI_ordered.R........... 10 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 11 tests <1b>[0;32mOK<1b>[0m v1 v2 co v1 v2 co
-2.791516 -3.029305 1.000000 2.960098 3.162890 20.000000
test_IRMI_ordered.R........... 12 tests <1b>[0;32mOK<1b>[0m
test_IRMI_ordered.R........... 13 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m27.0s<1b>[0m
test_aggFunctions.R........... 0 tests kNN ordered
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 0 tests
test_aggFunctions.R........... 1 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 5 tests <1b>[0;32mOK<1b>[0m
test_aggFunctions.R........... 6 tests <1b>[0;32mOK<1b>[0m <1b>[0;36m77ms<1b>[0m
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
test_data_frame.R............. 0 tests
test_data_frame.R............. 0 tests b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 0 tests b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 0 tests
test_data_frame.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 2 tests <1b>[0;32mOK<1b>[0m b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 2 tests <1b>[0;32mOK<1b>[0m b c b c
1 1 5 4
a c a c
1 1 5 4
a b a b
1 1 5 5
test_data_frame.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_data_frame.R............. 3 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.6s<1b>[0m
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 0 tests x y x y
-2.327352 -2.571533 3.398310 2.313331
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 0 tests x y x y
-2.327352 -2.571533 3.398310 2.313331
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 0 tests
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_gowerDind.R.............. 2 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.5s<1b>[0m
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
test_graphics.R............... 0 tests
Missings in variables:
Variable Count
NonD 14
Dream 12
Sleep 4
Span 4
Gest 4
test_graphics.R............... 0 tests
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Span Gest Pred Exp
0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Span Gest Pred
1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp
0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000
Exp Danger
5.000 5.000
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
Imputed missings in variables:
Variable Count
NonD 14
Dream 12
Sleep 4
Span 4
Gest 4
test_graphics.R............... 1 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 2 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 2 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 2 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m Exp Exp
1 5
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 3 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 4 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 4 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 5 tests <1b>[0;32mOK<1b>[0m Ca Bi Ca Bi
1.10e+02 6.00e-03 4.17e+04 3.89e+00
Ca As Ca As
110.0 0.1 41700.0 30.7
test_graphics.R............... 5 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 6 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 7 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 8 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 8 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 9 tests <1b>[0;32mOK<1b>[0m Bi Bi
0.006 3.890
As As
0.1 30.7
test_graphics.R............... 9 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 10 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 10 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 10 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 11 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep BodyWgt BrainWgt Dream Sleep
0.005 0.140 0.000 2.600 6654.000 5712.000 6.600 19.900
BodyWgt BrainWgt NonD Sleep BodyWgt BrainWgt NonD Sleep
0.005 0.140 2.100 2.600 6654.000 5712.000 17.900 19.900
BodyWgt BrainWgt NonD Dream BodyWgt BrainWgt NonD Dream
0.005 0.140 2.100 0.000 6654.000 5712.000 17.900 6.600
test_graphics.R............... 11 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 11 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 12 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 12 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 12 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 13 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep Span Gest BodyWgt BrainWgt
0.005 0.140 0.000 2.600 2.000 12.000 6654.000 5712.000
Dream Sleep Span Gest
6.600 19.900 100.000 645.000
BodyWgt BrainWgt NonD Sleep Span Gest BodyWgt BrainWgt
0.005 0.140 2.100 2.600 2.000 12.000 6654.000 5712.000
NonD Sleep Span Gest
17.900 19.900 100.000 645.000
BodyWgt BrainWgt NonD Dream Span Gest BodyWgt BrainWgt
0.005 0.140 2.100 0.000 2.000 12.000 6654.000 5712.000
NonD Dream Span Gest
17.900 6.600 100.000 645.000
BodyWgt BrainWgt NonD Dream Sleep Gest BodyWgt BrainWgt
0.005 0.140 2.100 0.000 2.600 12.000 6654.000 5712.000
NonD Dream Sleep Gest
17.900 6.600 19.900 645.000
BodyWgt BrainWgt NonD Dream Sleep Span BodyWgt BrainWgt
0.005 0.140 2.100 0.000 2.600 2.000 6654.000 5712.000
NonD Dream Sleep Span
17.900 6.600 19.900 100.000
test_graphics.R............... 13 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 13 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 14 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 14 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 15 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Span Gest Pred Exp
0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Span Gest Pred
1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp
0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000
Exp Danger
5.000 5.000
test_graphics.R............... 15 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 16 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 17 tests <1b>[0;32mOK<1b>[0m Al_XRF Ca_XRF Fe_XRF K_XRF Mg_XRF Mn_XRF Na_XRF P_XRF Si_XRF Ti_XRF Al_XRF
2.920 0.030 0.590 0.360 0.120 0.015 0.080 0.004 17.050 0.053 12.080
Ca_XRF Fe_XRF K_XRF Mg_XRF Mn_XRF Na_XRF P_XRF Si_XRF Ti_XRF
6.760 12.350 5.240 7.320 0.356 4.870 0.589 40.270 1.900
test_graphics.R............... 17 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 18 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 19 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 20 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 21 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 22 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 22 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 22 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 23 tests <1b>[0;32mOK<1b>[0m BodyWgt BrainWgt Dream Sleep BodyWgt BrainWgt Dream Sleep
0.005 0.140 0.000 2.600 6654.000 5712.000 6.600 19.900
BodyWgt BrainWgt NonD Sleep BodyWgt BrainWgt NonD Sleep
0.005 0.140 2.100 2.600 6654.000 5712.000 17.900 19.900
BodyWgt BrainWgt NonD Dream BodyWgt BrainWgt NonD Dream
0.005 0.140 2.100 0.000 6654.000 5712.000 17.900 6.600
test_graphics.R............... 23 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 23 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 24 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 24 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 24 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 25 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 25 tests <1b>[0;32mOK<1b>[0m Exp Exp
1 5
test_graphics.R............... 25 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 26 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 27 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 28 tests <1b>[0;32mOK<1b>[0m Humidity Humidity
71.6 94.8
Air.Temp Air.Temp
21.42 28.50
test_graphics.R............... 29 tests <1b>[0;32mOK<1b>[0m CaO CaO
-1.3010300 0.9758911
test_graphics.R............... 30 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 30 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 31 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 31 tests <1b>[0;32mOK<1b>[0m
test_graphics.R............... 32 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m29.4s<1b>[0m
hotdeck
test_hotdeck.R................ 0 tests
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 0 tests
test_hotdeck.R................ 1 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 1 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 1 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 2 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 2 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 3 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 4 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 5 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 5 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 6 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 6 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 7 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 7 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 8 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 8 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 9 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 10 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 10 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 10 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 11 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 12 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 13 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 14 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 15 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 15 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 15 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 16 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 17 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 18 tests <1b>[0;32mOK<1b>[0m
test_hotdeck.R................ 19 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.8s<1b>[0m
test_impNA.R.................. 0 tests
test_impNA.R.................. 0 tests
test_impNA.R.................. 0 tests
test_impNA.R.................. 0 tests BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp
0.005 0.140 0.000 2.900 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred
1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp
0.005 0.140 2.100 2.900 2.000 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred
1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000
Exp Danger
5.000 5.000
BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp
0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000
Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred
1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000
Exp Danger
5.000 5.000
test_impNA.R.................. 0 tests
test_impNA.R.................. 1 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 2 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 2 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 3 tests <1b>[0;32mOK<1b>[0m
test_impNA.R.................. 4 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.9s<1b>[0m
impPCA
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
test_impPCA.R................. 0 tests
Iterations: 4
test_impPCA.R................. 0 tests
test_impPCA.R................. 1 tests <1b>[0;32mOK<1b>[0m
Iterations: 4
test_impPCA.R................. 1 tests <1b>[0;32mOK<1b>[0m
test_impPCA.R................. 2 tests <1b>[0;32mOK<1b>[0m
Iterations: 0
test_impPCA.R................. 2 tests <1b>[0;32mOK<1b>[0m
test_impPCA.R................. 3 tests <1b>[0;32mOK<1b>[0m
Iterations: 0
test_impPCA.R................. 3 tests <1b>[0;32mOK<1b>[0m
test_impPCA.R................. 4 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.4s<1b>[0m
test_irmi_types.R............. 0 tests z z
-0.3308959 2.0121804
test_irmi_types.R............. 0 tests
test_irmi_types.R............. 1 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 2 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 3 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 4 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 5 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m num1 num2 num3 num1 num2 num3
-3.087610 -4.001394 -3.237928 3.349508 3.615635 2.820386
test_irmi_types.R............. 6 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 7 tests <1b>[0;32mOK<1b>[0m
test_irmi_types.R............. 8 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.2s<1b>[0m
test_kNN.R.................... 0 tests kNN general
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests y y
1 6
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests y y
1 6
test_kNN.R.................... 0 tests
test_kNN.R.................... 0 tests
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m Detected as categorical variable:
x,x_imp,y_imp
Detected as ordinal variable:
Detected as numerical variable:
y
0 items ofvariable:x imputed
6items ofvariable:y imputed
Time difference of 0.07567096 secs
test_kNN.R.................... 1 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m y z
1.000000 1.000000
RandomVariableForImputation y
-1.372898 6.000000
z RandomVariableForImputation
6.000000 2.212962
z RandomVariableForImputation
1.000000 -1.372898
z RandomVariableForImputation
6.000000 2.212962
y z
1.000000 1.000000
RandomVariableForImputation y
-1.372898 6.000000
z RandomVariableForImputation
6.000000 2.212962
test_kNN.R.................... 2 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
z y2
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
y z
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.5949014 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 z23
-0.2369185 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.5949014
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 z23
1.0393184 6.0000000
m23 RandomVariableForImputation
1.0393184 2.0511976
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 RandomVariableForImputation
1.0000000 -1.5949014
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 y23
1.0393184 6.0000000
z23 RandomVariableForImputation
6.0000000 2.0511976
test_kNN.R.................... 3 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 4 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 4 tests <1b>[0;32mOK<1b>[0m z z
1 6
test_kNN.R.................... 4 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 5 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 5 tests <1b>[0;32mOK<1b>[0m z z
1 6
test_kNN.R.................... 5 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 6 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 7 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 7 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 8 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m z z
1 6
y y
1 6
test_kNN.R.................... 9 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 10 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 11 tests <1b>[0;32mOK<1b>[0m z z
1 6
y y
1 5
test_kNN.R.................... 11 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 12 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
y z m
1.0000000 1.0000000 -0.2369185
yrandomForestFeature y z
1.9946333 6.0000000 6.0000000
m yrandomForestFeature
1.0393184 4.8171000
y z m
1.00000000 1.00000000 -0.23691848
mrandomForestFeature y z
-0.03045094 6.00000000 6.00000000
m mrandomForestFeature
1.03931837 0.90111834
test_kNN.R.................... 13 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 14 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
yrandomForestFeature yrandomForestFeature
2.082400 4.727667
mrandomForestFeature mrandomForestFeature
-0.05045148 0.87127575
test_kNN.R.................... 15 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 16 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 2.032967 6.000000
yrandomForestFeature
4.985967
test_kNN.R.................... 17 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 18 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 1.902000 6.000000
yrandomForestFeature
4.915067
test_kNN.R.................... 19 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 20 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m y y
1 6
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m y y
1 6
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 21 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m Detected as categorical variable:
x,x_imp,y_imp
Detected as ordinal variable:
Detected as numerical variable:
y
0 items ofvariable:x imputed
6items ofvariable:y imputed
Time difference of 0.09569335 secs
test_kNN.R.................... 22 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m y z
1.000000 1.000000
RandomVariableForImputation y
-1.130797 6.000000
z RandomVariableForImputation
6.000000 1.380325
z RandomVariableForImputation
1.000000 -1.130797
z RandomVariableForImputation
6.000000 1.380325
y z
1.000000 1.000000
RandomVariableForImputation y
-1.130797 6.000000
z RandomVariableForImputation
6.000000 1.380325
test_kNN.R.................... 23 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
z y2
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
z2 m2
1.0000000 -0.2369185
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
y z
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
y23 z23
1.0000000 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 m23
1.0000000 -0.2369185
RandomVariableForImputation y
-1.6220983 6.0000000
z y2
6.0000000 6.0000000
z2 m2
6.0000000 1.0393184
y23 z23
6.0000000 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 z23
-0.2369185 1.0000000
m23 RandomVariableForImputation
-0.2369185 -1.6220983
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 z23
1.0393184 6.0000000
m23 RandomVariableForImputation
1.0393184 1.1502594
y z
1.0000000 1.0000000
y2 z2
1.0000000 1.0000000
m2 y23
-0.2369185 1.0000000
z23 RandomVariableForImputation
1.0000000 -1.6220983
y z
6.0000000 6.0000000
y2 z2
6.0000000 6.0000000
m2 y23
1.0393184 6.0000000
z23 RandomVariableForImputation
6.0000000 1.1502594
test_kNN.R.................... 24 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 25 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 25 tests <1b>[0;32mOK<1b>[0m z z
1 6
test_kNN.R.................... 25 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 26 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 26 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 27 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
y z m
1.0000000 1.0000000 -0.2369185
yrandomForestFeature y z
2.1257000 6.0000000 6.0000000
m yrandomForestFeature
1.0393184 4.6920333
y z m
1.00000000 1.00000000 -0.23691848
mrandomForestFeature y z
-0.04213089 6.00000000 6.00000000
m mrandomForestFeature
1.03931837 0.84783840
test_kNN.R.................... 28 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 29 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m y z m y z m
1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184
z m z m
1.0000000 -0.2369185 6.0000000 1.0393184
y z y z
1 1 6 6
yrandomForestFeature yrandomForestFeature
2.125167 4.833167
mrandomForestFeature mrandomForestFeature
-0.09882801 0.85004137
test_kNN.R.................... 30 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 31 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 1.962533 6.000000
yrandomForestFeature
4.974033
test_kNN.R.................... 32 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 33 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m z y z y
1 1 6 6
z z
1 6
z z
1 6
z yrandomForestFeature z
1.000000 1.989633 6.000000
yrandomForestFeature
4.963533
test_kNN.R.................... 34 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 35 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 36 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 36 tests <1b>[0;32mOK<1b>[0m col2 col3 col2 col3
3 5 4 6
test_kNN.R.................... 37 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 37 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 37 tests <1b>[0;32mOK<1b>[0m col2 col3 col2 col3
3 5 4 6
test_kNN.R.................... 38 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 38 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 38 tests <1b>[0;32mOK<1b>[0m col2 col3 col2 col3
3 5 4 6
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m weighted catFun without missings in the distance variables
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m x y x y
1 1 10 10
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m weighted catFun with missings in the distance variables
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m x y x y
1 1 10 10
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m x y x y
1 1 10 10
test_kNN.R.................... 39 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m8.4s<1b>[0m
test_kNN_exact.R.............. 0 tests kNN exact results
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 0 tests Detected as categorical variable:
Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp
Detected as ordinal variable:
ord
Detected as numerical variable:
X1,X2,ClassNum,Row,Row2
0 items ofvariable:Class imputed
0 items ofvariable:X1 imputed
X1 X1
1 1
2items ofvariable:X2 imputed
0 items ofvariable:ClassNum imputed
0 items ofvariable:Row imputed
0 items ofvariable:Row2 imputed
0 items ofvariable:ord imputed
Time difference of 0.07512593 secs
test_kNN_exact.R.............. 0 tests
test_kNN_exact.R.............. 1 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 2 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 2 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 3 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 4 tests <1b>[0;32mOK<1b>[0m X1 ClassNum X1 ClassNum
1 1 1 2
test_kNN_exact.R.............. 4 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 5 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 7 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 8 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 8 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 9 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 10 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 10 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 11 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 12 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 13 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 14 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 14 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 15 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 16 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 16 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 17 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m Detected as categorical variable:
Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp
Detected as ordinal variable:
ord
Detected as numerical variable:
X1,X2,ClassNum,Row,Row2
0 items ofvariable:Class imputed
0 items ofvariable:X1 imputed
X1 X1
1 1
2items ofvariable:X2 imputed
0 items ofvariable:ClassNum imputed
0 items ofvariable:Row imputed
0 items ofvariable:Row2 imputed
0 items ofvariable:ord imputed
Time difference of 0.1795197 secs
test_kNN_exact.R.............. 18 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 19 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 20 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 20 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 21 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 22 tests <1b>[0;32mOK<1b>[0m X1 ClassNum X1 ClassNum
1 1 1 2
test_kNN_exact.R.............. 22 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 23 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 24 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 25 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 26 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 26 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 27 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 28 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 28 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 29 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 30 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 31 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 32 tests <1b>[0;32mOK<1b>[0m Row2 Row2
1 10
Row2 Row2
1 10
test_kNN_exact.R.............. 32 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 33 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 34 tests <1b>[0;32mOK<1b>[0m X1 Row2 X1 Row2
1 1 1 10
X1 Row2 X1 Row2
1 1 1 10
test_kNN_exact.R.............. 34 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 35 tests <1b>[0;32mOK<1b>[0m
test_kNN_exact.R.............. 36 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.0s<1b>[0m
test_kNN_iqr.R................ 0 tests kNN iqr
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests y z y z
-1.914359 -2.888921 2.307978 2.649167
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 0 tests
test_kNN_iqr.R................ 1 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.2s<1b>[0m
test_kNN_ordered.R............ 0 tests kNN ordered
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 0 tests y z y z
1 1 6 6
test_kNN_ordered.R............ 0 tests
test_kNN_ordered.R............ 1 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 2 tests <1b>[0;32mOK<1b>[0m y z y z
1 1 6 6
test_kNN_ordered.R............ 2 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 3 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 4 tests <1b>[0;32mOK<1b>[0m y z y z
1 1 6 6
test_kNN_ordered.R............ 4 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 5 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 6 tests <1b>[0;32mOK<1b>[0m y z y z
1 1 6 6
test_kNN_ordered.R............ 6 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 7 tests <1b>[0;32mOK<1b>[0m
test_kNN_ordered.R............ 8 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.8s<1b>[0m
test_matchImpute.R............ 0 tests matchImpute general
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 0 tests
test_matchImpute.R............ 1 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 1 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 2 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 3 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 4 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 5 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 5 tests <1b>[0;32mOK<1b>[0m
test_matchImpute.R............ 6 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.1s<1b>[0m
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 0 tests
test_rangerImpute.R........... 1 tests <1b>[0;32mOK<1b>[0m No missings in x.
test_rangerImpute.R........... 1 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 2 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 3 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 4 tests <1b>[0;32mOK<1b>[0m
test_rangerImpute.R........... 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.4s<1b>[0m
test_regressionImp.R.......... 0 tests
test_regressionImp.R.......... 0 tests
test_regressionImp.R.......... 1 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 1 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 2 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 2 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 3 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 3 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 4 tests <1b>[0;32mOK<1b>[0m
test_regressionImp.R.......... 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;36m88ms<1b>[0m
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 0 tests
test_xgboostImpute.R.......... 1 tests <1b>[0;32mOK<1b>[0m No missings in x.
test_xgboostImpute.R.......... 1 tests <1b>[0;32mOK<1b>[0m
test_xgboostImpute.R.......... 2 tests <1b>[0;32mOK<1b>[0m Error in process.y.margin.and.objective(y, base_margin, objective, params) :
Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
Calls: <Anonymous> ... xgboostImpute -> <Anonymous> -> process.y.margin.and.objective
In addition: There were 20 warnings (use warnings() to see them)
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 6.2.6
Check: installed package size
Result: NOTE
installed size is 5.3Mb
sub-directories of 1Mb or more:
doc 2.7Mb
libs 1.1Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.