rfPredVar: rfPredVar
In RFinfer: Inference for Random Forests
Description
Usage
Arguments
Details
Value
Examples
Description
Generate predictions and prediction variances from a random
forest based on the infinitesimal jackknife.
Usage
1
2
Arguments
random.forest
A random forest trained with keep.inbag=TRUE. See
details for more information.
rf.data
The data used to train rf
pred.data
The data used to predict with the forest; defaults to
rf.data if not given
CI
Should 95% confidence intervals based on the CLT be returned along
with predictions and prediction variances?
tree.type
either 'ci' for conditional inference tree or 'rf' for
traditional CART tree
prog.bar
should progress bar be shown? (only applicable when
tree.type='ci')
Details
The random forest trained with keep.inbag=TRUE is supplied
only for the purpose of defining the resampling scheme. The function builds
a new random forest based on the tree.type setting. However, the
resamples are maintained identically to the supplied random forest. This
allows for direct comparison of the tree methods without having to account
for variation in resampling.
Currently, the CI methods are much more computationally intensive because
there is no C implementation of the CI random forest method that indicates
the number of times that each sample is included in each resample. In
order to carry out our simulations using V_IJ^B, we had to use a
pure R implementation of CI random forests. This is different for CART
random forests, where a C implementation already exists in the
randomForest package. However, it should be noted that the
difference in computational times is due to the random forest creation
step, not the implementation of V_IJ^B. This should not be an
issue in the future when a C implementation of CI random forests is
created.
Note: This function does not use the default predict method for forests
produced by cforest. The predictions here are the direct averages of
all tree predictions, instead of using the observation weights. Therefore,
predictions from this function will likely differ from
predict.cforest when using subsampling.
This function currently only works with regression forests – not
classification forests.
Value
A data frame with the predictions and prediction variances (and
optionally 95% confidence interval)
Examples
1
2
3
4
5
Example output
Loading required package: randomForest
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
pred pred.ij.var l.ci u.ci
1 40.68394 10.3243791 20.448533 60.91935
2 31.23464 2.1249845 27.069750 35.39954
3 24.42364 4.9763487 14.670175 34.17710
4 26.23401 4.9245955 16.581976 35.88604
7 30.41251 11.7472413 7.388340 53.43668
8 20.82393 5.1863699 10.658836 30.98903
9 18.08028 7.0128366 4.335370 31.82518
12 23.84250 1.9964695 19.929488 27.75550
13 24.32984 2.3206905 19.781374 28.87831
14 23.22877 1.9498478 19.407141 27.05040
15 16.41163 2.1531510 12.191536 20.63173
16 22.89714 3.3257768 16.378736 29.41554
17 24.42264 4.6504045 15.308014 33.53726
18 17.12517 4.9267799 7.468861 26.78148
19 24.17524 2.8559941 18.577593 29.77288
20 15.60213 3.5943783 8.557277 22.64698
21 14.80434 5.1228547 4.763728 24.84495
22 22.47148 5.3485178 11.988575 32.95438
23 16.07760 4.7919063 6.685632 25.46956
24 21.97477 2.2999363 17.466980 26.48256
28 25.07361 6.0356704 13.243909 36.90330
29 43.75174 18.8493608 6.807675 80.69581
30 67.92751 86.0774362 -100.781165 236.63618
31 45.20698 3.3241004 38.691860 51.72209
38 31.40482 5.6779091 20.276322 42.53332
40 54.43472 6.6966972 41.309434 67.56000
41 47.22202 3.6473395 40.073365 54.37067
44 34.53692 11.0752420 12.829844 56.24399
47 26.37849 3.7915776 18.947130 33.80984
48 26.48912 6.2648569 14.210225 38.76801
49 18.62717 -0.3822833 19.376428 17.87791
50 21.06895 4.0076580 13.214087 28.92382
51 22.34322 4.4901967 13.542595 31.14384
62 85.41890 79.9764037 -71.331971 242.16977
63 60.33807 22.7640628 15.721323 104.95481
64 45.03540 10.1288603 25.183199 64.88760
66 69.78987 8.1555914 53.805201 85.77453
67 45.10477 8.7085353 28.036351 62.17318
68 81.65542 17.4971821 47.361572 115.94927
69 80.55342 16.6550312 47.910158 113.19668
70 82.81652 19.1353453 45.311931 120.32111
71 70.88492 11.8755704 47.609229 94.16061
73 25.74753 2.9004357 20.062779 31.43228
74 35.45849 9.2057600 17.415528 53.50144
76 23.44657 9.5001710 4.826574 42.06656
77 49.26942 7.6943699 34.188731 64.35011
78 39.32425 2.8524770 33.733500 44.91500
79 67.34612 22.9123487 22.438741 112.25350
80 74.28935 6.1825912 62.171696 86.40701
81 53.71845 4.5642399 44.772707 62.66420
82 26.51204 6.7764517 13.230442 39.79364
85 63.86172 11.0388360 42.225998 85.49744
86 71.10199 21.6278259 28.712226 113.49175
87 38.91070 16.9229689 5.742291 72.07911
88 47.59893 8.4102798 31.115088 64.08278
89 73.74812 8.2436453 57.590871 89.90537
90 68.61989 6.3177802 56.237264 81.00251
91 63.49962 7.1383454 49.508719 77.49052
92 55.50592 8.8667743 38.127361 72.88448
93 44.81673 18.3713230 8.809602 80.82386
94 29.59337 22.1735327 -13.865959 73.05269
95 38.12710 23.4200750 -7.775403 84.02960
99 90.84453 34.9696685 22.305243 159.38382
100 68.61757 14.6060237 39.990286 97.24485
101 74.81873 19.2278297 37.132880 112.50459
104 54.10463 7.3973183 39.606156 68.60311
105 38.04923 5.3443980 27.574406 48.52406
106 37.75507 15.7235580 6.937459 68.57267
108 23.27051 4.7597069 13.941655 32.59936
109 43.74520 20.6211303 3.328527 84.16187
110 30.68584 6.2496176 18.436817 42.93487
111 33.02927 3.8656576 25.452717 40.60582
112 34.17393 4.6873041 24.986986 43.36088
113 27.09758 1.3505187 24.450608 29.74454
114 17.27018 1.5299276 14.271573 20.26878
116 44.38040 24.7751155 -4.177934 92.93873
117 79.65520 126.8141159 -168.895900 328.20630
118 71.22003 10.3968061 50.842668 91.59740
120 70.22293 13.4450248 43.871169 96.57470
121 91.06390 31.7532246 28.828723 153.29908
122 82.41903 11.8553475 59.182979 105.65509
123 79.91493 10.0492894 60.218688 99.61118
124 72.15672 15.2593623 42.248921 102.06452
125 79.39492 4.1839329 71.194566 87.59528
126 83.12062 3.2672144 76.717001 89.52425
127 79.75546 4.6926379 70.558056 88.95286
128 49.13521 6.9421416 35.528864 62.74156
129 38.64165 6.0320986 26.818949 50.46434
130 35.69016 6.3746106 23.196154 48.18417
131 32.23633 5.7188431 21.027601 43.44505
132 27.45174 2.4345158 22.680174 32.22330
133 27.58210 1.2041738 25.221967 29.94224
134 34.67612 5.4935957 23.908873 45.44337
135 24.01117 0.2648451 23.492079 24.53025
136 43.31077 12.3259993 19.152258 67.46929
137 17.84842 2.2848305 13.370235 22.32661
138 20.98007 1.5734511 17.896163 24.06398
139 38.17963 3.3778651 31.559134 44.80012
140 24.87453 1.8581234 21.232678 28.51639
141 18.40092 1.7382313 14.994050 21.80779
142 24.90554 1.0068481 22.932151 26.87892
143 39.84663 19.4323806 1.759862 77.93339
144 23.34297 1.5864256 20.233634 26.45231
145 19.15875 0.9492059 17.298345 21.01916
146 30.88619 5.1948743 20.704428 41.06796
147 20.41757 4.8593395 10.893435 29.94170
148 21.08585 3.2237037 14.767506 27.40419
149 39.15747 5.9789174 27.439003 50.87593
151 31.59813 11.1922658 9.661695 53.53457
152 33.58547 9.7879199 14.401495 52.76944
153 36.83620 5.3753407 26.300725 47.37167
RFinfer documentation built on May 30, 2017, 12:20 a.m.
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Description Usage Arguments Details Value Examples
Description
Generate predictions and prediction variances from a random forest based on the infinitesimal jackknife.
Usage
1 2 |
Arguments
random.forest |
A random forest trained with |
rf.data |
The data used to train |
pred.data |
The data used to predict with the forest; defaults to
|
CI |
Should 95% confidence intervals based on the CLT be returned along with predictions and prediction variances? |
tree.type |
either 'ci' for conditional inference tree or 'rf' for traditional CART tree |
prog.bar |
should progress bar be shown? (only applicable when
|
Details
The random forest trained with keep.inbag=TRUE is supplied
only for the purpose of defining the resampling scheme. The function builds
a new random forest based on the tree.type setting. However, the
resamples are maintained identically to the supplied random forest. This
allows for direct comparison of the tree methods without having to account
for variation in resampling.
Currently, the CI methods are much more computationally intensive because
there is no C implementation of the CI random forest method that indicates
the number of times that each sample is included in each resample. In
order to carry out our simulations using V_IJ^B, we had to use a
pure R implementation of CI random forests. This is different for CART
random forests, where a C implementation already exists in the
randomForest package. However, it should be noted that the
difference in computational times is due to the random forest creation
step, not the implementation of V_IJ^B. This should not be an
issue in the future when a C implementation of CI random forests is
created.
Note: This function does not use the default predict method for forests
produced by cforest. The predictions here are the direct averages of
all tree predictions, instead of using the observation weights. Therefore,
predictions from this function will likely differ from
predict.cforest when using subsampling.
This function currently only works with regression forests – not classification forests.
Value
A data frame with the predictions and prediction variances (and optionally 95% confidence interval)
Examples
1 2 3 4 5 |
Example output
Loading required package: randomForest
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
pred pred.ij.var l.ci u.ci
1 40.68394 10.3243791 20.448533 60.91935
2 31.23464 2.1249845 27.069750 35.39954
3 24.42364 4.9763487 14.670175 34.17710
4 26.23401 4.9245955 16.581976 35.88604
7 30.41251 11.7472413 7.388340 53.43668
8 20.82393 5.1863699 10.658836 30.98903
9 18.08028 7.0128366 4.335370 31.82518
12 23.84250 1.9964695 19.929488 27.75550
13 24.32984 2.3206905 19.781374 28.87831
14 23.22877 1.9498478 19.407141 27.05040
15 16.41163 2.1531510 12.191536 20.63173
16 22.89714 3.3257768 16.378736 29.41554
17 24.42264 4.6504045 15.308014 33.53726
18 17.12517 4.9267799 7.468861 26.78148
19 24.17524 2.8559941 18.577593 29.77288
20 15.60213 3.5943783 8.557277 22.64698
21 14.80434 5.1228547 4.763728 24.84495
22 22.47148 5.3485178 11.988575 32.95438
23 16.07760 4.7919063 6.685632 25.46956
24 21.97477 2.2999363 17.466980 26.48256
28 25.07361 6.0356704 13.243909 36.90330
29 43.75174 18.8493608 6.807675 80.69581
30 67.92751 86.0774362 -100.781165 236.63618
31 45.20698 3.3241004 38.691860 51.72209
38 31.40482 5.6779091 20.276322 42.53332
40 54.43472 6.6966972 41.309434 67.56000
41 47.22202 3.6473395 40.073365 54.37067
44 34.53692 11.0752420 12.829844 56.24399
47 26.37849 3.7915776 18.947130 33.80984
48 26.48912 6.2648569 14.210225 38.76801
49 18.62717 -0.3822833 19.376428 17.87791
50 21.06895 4.0076580 13.214087 28.92382
51 22.34322 4.4901967 13.542595 31.14384
62 85.41890 79.9764037 -71.331971 242.16977
63 60.33807 22.7640628 15.721323 104.95481
64 45.03540 10.1288603 25.183199 64.88760
66 69.78987 8.1555914 53.805201 85.77453
67 45.10477 8.7085353 28.036351 62.17318
68 81.65542 17.4971821 47.361572 115.94927
69 80.55342 16.6550312 47.910158 113.19668
70 82.81652 19.1353453 45.311931 120.32111
71 70.88492 11.8755704 47.609229 94.16061
73 25.74753 2.9004357 20.062779 31.43228
74 35.45849 9.2057600 17.415528 53.50144
76 23.44657 9.5001710 4.826574 42.06656
77 49.26942 7.6943699 34.188731 64.35011
78 39.32425 2.8524770 33.733500 44.91500
79 67.34612 22.9123487 22.438741 112.25350
80 74.28935 6.1825912 62.171696 86.40701
81 53.71845 4.5642399 44.772707 62.66420
82 26.51204 6.7764517 13.230442 39.79364
85 63.86172 11.0388360 42.225998 85.49744
86 71.10199 21.6278259 28.712226 113.49175
87 38.91070 16.9229689 5.742291 72.07911
88 47.59893 8.4102798 31.115088 64.08278
89 73.74812 8.2436453 57.590871 89.90537
90 68.61989 6.3177802 56.237264 81.00251
91 63.49962 7.1383454 49.508719 77.49052
92 55.50592 8.8667743 38.127361 72.88448
93 44.81673 18.3713230 8.809602 80.82386
94 29.59337 22.1735327 -13.865959 73.05269
95 38.12710 23.4200750 -7.775403 84.02960
99 90.84453 34.9696685 22.305243 159.38382
100 68.61757 14.6060237 39.990286 97.24485
101 74.81873 19.2278297 37.132880 112.50459
104 54.10463 7.3973183 39.606156 68.60311
105 38.04923 5.3443980 27.574406 48.52406
106 37.75507 15.7235580 6.937459 68.57267
108 23.27051 4.7597069 13.941655 32.59936
109 43.74520 20.6211303 3.328527 84.16187
110 30.68584 6.2496176 18.436817 42.93487
111 33.02927 3.8656576 25.452717 40.60582
112 34.17393 4.6873041 24.986986 43.36088
113 27.09758 1.3505187 24.450608 29.74454
114 17.27018 1.5299276 14.271573 20.26878
116 44.38040 24.7751155 -4.177934 92.93873
117 79.65520 126.8141159 -168.895900 328.20630
118 71.22003 10.3968061 50.842668 91.59740
120 70.22293 13.4450248 43.871169 96.57470
121 91.06390 31.7532246 28.828723 153.29908
122 82.41903 11.8553475 59.182979 105.65509
123 79.91493 10.0492894 60.218688 99.61118
124 72.15672 15.2593623 42.248921 102.06452
125 79.39492 4.1839329 71.194566 87.59528
126 83.12062 3.2672144 76.717001 89.52425
127 79.75546 4.6926379 70.558056 88.95286
128 49.13521 6.9421416 35.528864 62.74156
129 38.64165 6.0320986 26.818949 50.46434
130 35.69016 6.3746106 23.196154 48.18417
131 32.23633 5.7188431 21.027601 43.44505
132 27.45174 2.4345158 22.680174 32.22330
133 27.58210 1.2041738 25.221967 29.94224
134 34.67612 5.4935957 23.908873 45.44337
135 24.01117 0.2648451 23.492079 24.53025
136 43.31077 12.3259993 19.152258 67.46929
137 17.84842 2.2848305 13.370235 22.32661
138 20.98007 1.5734511 17.896163 24.06398
139 38.17963 3.3778651 31.559134 44.80012
140 24.87453 1.8581234 21.232678 28.51639
141 18.40092 1.7382313 14.994050 21.80779
142 24.90554 1.0068481 22.932151 26.87892
143 39.84663 19.4323806 1.759862 77.93339
144 23.34297 1.5864256 20.233634 26.45231
145 19.15875 0.9492059 17.298345 21.01916
146 30.88619 5.1948743 20.704428 41.06796
147 20.41757 4.8593395 10.893435 29.94170
148 21.08585 3.2237037 14.767506 27.40419
149 39.15747 5.9789174 27.439003 50.87593
151 31.59813 11.1922658 9.661695 53.53457
152 33.58547 9.7879199 14.401495 52.76944
153 36.83620 5.3753407 26.300725 47.37167
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