Last updated on 2026-03-30 14:50:59 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.1 | 29.66 | 676.63 | 706.29 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.1.1 | 15.94 | 537.23 | 553.17 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.1.1 | 43.00 | 896.77 | 939.77 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.1.1 | 47.00 | 1193.40 | 1240.40 | OK | |
| r-devel-macos-arm64 | 0.1.1 | 6.00 | 136.00 | 142.00 | OK | |
| r-devel-windows-x86_64 | 0.1.1 | 29.00 | 565.00 | 594.00 | OK | |
| r-patched-linux-x86_64 | 0.1.1 | 23.46 | 681.91 | 705.37 | OK | |
| r-release-linux-x86_64 | 0.1.1 | 23.50 | 684.43 | 707.93 | OK | |
| r-release-macos-arm64 | 0.1.1 | OK | ||||
| r-release-macos-x86_64 | 0.1.1 | 15.00 | 434.00 | 449.00 | OK | |
| r-release-windows-x86_64 | 0.1.1 | 27.00 | 556.00 | 583.00 | OK | |
| r-oldrel-macos-arm64 | 0.1.1 | NOTE | ||||
| r-oldrel-macos-x86_64 | 0.1.1 | 16.00 | 456.00 | 472.00 | NOTE | |
| r-oldrel-windows-x86_64 | 0.1.1 | 38.00 | 710.00 | 748.00 | NOTE |
Version: 0.1.1
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘cram_bandit.Rmd’ using rmarkdown
--- finished re-building ‘cram_bandit.Rmd’
--- re-building ‘cram_bandit_helpers.Rmd’ using rmarkdown
--- finished re-building ‘cram_bandit_helpers.Rmd’
--- re-building ‘cram_bandit_simulation.Rmd’ using rmarkdown
--- finished re-building ‘cram_bandit_simulation.Rmd’
--- re-building ‘cram_ml.Rmd’ using rmarkdown
--- finished re-building ‘cram_ml.Rmd’
--- re-building ‘cram_policy_part_1.Rmd’ using rmarkdown
--- finished re-building ‘cram_policy_part_1.Rmd’
--- re-building ‘cram_policy_part_2.Rmd’ using rmarkdown
--- finished re-building ‘cram_policy_part_2.Rmd’
--- re-building ‘cram_policy_simulation.Rmd’ using rmarkdown
*** caught segfault ***
address 0x7f3efcdfe9c8, cause 'memory not mapped'
Traceback:
1: (function (train_matrix, outcome_index, sample_weight_index, use_sample_weights, mtry, num_trees, min_node_size, sample_fraction, honesty, honesty_fraction, honesty_prune_leaves, ci_group_size, alpha, imbalance_penalty, clusters, samples_per_cluster, compute_oob_predictions, num_threads, seed, legacy_seed, verbose) { .Call("_grf_regression_train", PACKAGE = "grf", train_matrix, outcome_index, sample_weight_index, use_sample_weights, mtry, num_trees, min_node_size, sample_fraction, honesty, honesty_fraction, honesty_prune_leaves, ci_group_size, alpha, imbalance_penalty, clusters, samples_per_cluster, compute_oob_predictions, num_threads, seed, legacy_seed, verbose)})(outcome_index = 3L, sample_weight_index = 4, use_sample_weights = FALSE, train_matrix = c(1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 2, 3, 3, 2, 4, 2, 4, 1, 2, 5, 1, 5, 3, 3, 1, 3, 5, 1, 3, 4, 1, 2, 2, 1, 5, 4, 5, 4, 5, 2, 3, 4, 2, 4, 3, 4, 5, 5, 3, 4, 3, 4, 1, 5, 1, 4, 3, 2, 3, 1, 5, 5, 4, 5, 4, 4, 5, 3, 5, 2, 5, 3, 1, 4, 2, 4, 4, 1, 4, 5, 3, 1, 4, 2, 3, 1, 3, 4, 4, 2, 5, 5, 5, 3, 4, 3, 5, 3, 1, 1, 2, 5, 5, 5, 2, 3, 3, 4, 1, 1, 2, 5, 1, 5, 3, 3, 2, 5, 5, 4, 4, 1, 5, 4, 4, 2, 5, 5, 4, 1, 1, 4, 1, 1, 4, 4, 1, 1, 1, 2, 3, 4, 5, 3, 1, 5, 5, 4, 2, 1, 5, 2, 3, 1, 5, 1, 5, 4, 1, 3, 1, 4, 4, 1, 2, 1, 3, 1, 5, 4, 1, 5, 2, 3, 5, 5, 3, 5, 2, 2, 5, 4, 5, 5, 3, 5, 2, 3, 1, 3, 5, 2, 3, 3, 5, 5, 3, 1, 3, 3, 2, 3, 3, 2, 1, 5, 4, 4, 2, 4, 1, 1, 4, 4, 4, 3, 1, 1, 3, 5, 5, 1, 5, 5, 1, 2, 1, 2, 1, 4, 1, 4, 3, 4, 1, 3, 2, 1, 1, 1, 4, 5, 4, 5, 3, 2, 5, 1, 4, 3, 1, 4, 1, 2, 3, 2, 4, 4, 1, 1, 2, 5, 2, 1, 3, 2, 1, 1, 2, 4, 4, 1, 5, 2, 2, 4, 2, 5, 3, 3, 1, 3, 5, 2, 4, 1, 5, 5, 5, 1, 5, 5, 1, 2, 1, 2, 3, 3, 2, 5, 4, 3, 3, 4, 1, 3, 5, 5, 1, 2, 4, 5, 3, 4, 3, 3, 1, 4, 5, 2, 5, 1, 1, 1, 2, 4, 3, 4, 1, 3, 5, 2, 1, 1, 2, 1, 2, 2, 5, 1, 5, 3, 2, 2, 2, 4, 1, 3, 4, 2, 5, 2, 2, 5, 2, 5, 1, 4, 2, 4, 5, 1, 3, 4, 1, 3, 1, 5, 2, 1, 5, 4, 3, 1, 5, 3, 1, 1, 4, 2, 3, 1, 3, 4, 5, 4, 5, 2, 1, 4, 2, 2, 3, 4, 2, 4, 2, 2, 5, 4, 4, 2, 1, 5, 5, 4, 2, 2, 2, 2, 0.707588353835588, -0.738527704739573, 0.418982404924464, 0.787738847475178, -0.324685911490835, -0.476246894615578, 0.0597499373846007, -1.57214415914549, -0.723065969939874, 0.54319405923209, -0.458365332711106, -0.865512862653374, 0.32430434416138, 0.436523478910183, 1.26318517608949, -1.18548008459731, 0.310480749443137, 1.26318517608949, 0.687916772975828, 0.600708823672418, 1.65090746733669, -0.349650387953555, -0.57438868976327, 1.26318517608949, -0.51606383094478, 2.19881034888372, 1.23247587848534, 1.10992028971364, 0.617985817166529, -0.280395335170247, -0.738527704739573, -1.31080153332797, -0.723065969939874, 0.600708823672418, -0.236279568941097, -0.741336096272828, 1.67569693240319, 0.332202578950118, -0.788602837850243, -1.04917700666607, 2.12845189901618, -1.04917700666607, -0.441163216905286, -0.573973479297987, 0.0847372921971965, 0.0597499373846007, 0.418982404924464, -1.2847157223178, -0.738527704739573, -1.57214415914549, -0.182925388372727, -0.992507150392037, -0.372438756103829, 0.54319405923209, 1.05271146557933, 0.0597499373846007, -0.034067253738464, 0.436523478910183, -0.594617267459511, -0.611165916680421, -0.182925388372727, 0.636569674033849, -1.23627311888329, 0.0377883991710788, -0.71721816157401, 0.754053785184521, -1.04917700666607, -1.23627311888329, -0.324685911490835, -0.865512862653374, 0.368964527385086, -0.499292017172261, -1.09599626707466, 0.787738847475178, 1.31241297643351, 0.701784335374711, 0.368964527385086, -1.31080153332797, -0.324685911490835, -0.349650387953555, 0.76904224100091, -1.31701613230524, -0.182925388372727, 0.687916772975828, 0.0597499373846007, 3.2410399349424, -0.895363357977542, -1.00837660827701, 1.95529396549246, -1.25127136162494, -0.723065969939874, 0.76904224100091, 0.214538826629216, 0.76904224100091, -0.349650387953555, 0.636569674033849, 0.418982404924464, 0.600708823672418, 1.65090746733669, -0.441163216905286, -0.611165916680421, 0.76904224100091, 1.65090746733669, 1.23247587848534, -1.00837660827701, 0.418982404924464, -0.119452606630659, 0.76904224100091, -0.594617267459511, -0.262197489402468, 1.10992028971364, -1.23627311888329, -0.0903195939658516, 0.0597499373846007, 1.10992028971364, 0.119245236427584, 0.332202578950118, -0.573973479297987, -0.0540281250854405, 0.0847372921971965, 1.95529396549246, -1.31080153332797, -0.458365332711106, -1.26015524475811, -0.262197489402468, -1.09599626707466, -1.26015524475811, 1.95529396549246, -0.499292017172261, -0.280395335170247, 0.88465049897692, -1.09599626707466, -0.51606383094478, -0.416857588160432, -1.57214415914549, -0.41433994791886, 2.10010894052567, 0.243687429599092, -0.476246894615578, 1.65090746733669, 0.617985817166529, 0.119245236427584, 2.12845189901618, -0.781536487054751, -0.41433994791886, -1.26015524475811, -0.034067253738464, -1.31080153332797, 1.10984813892972, -0.236279568941097, 1.65090746733669, -0.741336096272828, 1.05271146557933, -0.458365332711106, 0.516862044313609, -1.26015524475811, 0.0945835281735714, -0.265145056696353, 0.0652930335253153, -0.0540281250854405, -0.458365332711106, 1.67569693240319, -0.280395335170247, 0.418982404924464, -0.51606383094478, 1.23247587848534, 0.0945835281735714, 0.54319405923209, 0.976973386685621, 0.56298953322048, 0.214538826629216, -0.262197489402468, -0.992507150392037, 2.10010894052567, 0.636569674033849, 1.23247587848534, -0.119452606630659, 0.418982404924464, -1.51466765378175, 0.418982404924464, -0.865512862653374, 0.787738847475178, -0.236279568941097, 0.636569674033849, -0.182925388372727, 0.332202578950118, 0.418982404924464, -1.26015524475811, -1.60153617357459, -0.788602837850243, 0.707588353835588, -1.28703047603518, 0.436523478910183, -1.06332613397119, 0.701784335374711, 0.332202578950118, -0.530906522170303, 1.99721338474797, -0.349650387953555, -0.197175894348552, 1.10984813892972, -1.51466765378175, -0.262197489402468, -0.0540281250854405, 0.0377883991710788, -1.18548008459731, 0.701784335374711, 0.298227591540715, -1.28703047603518, -0.483780625708744, 0.332202578950118, 1.26318517608949, -1.31701613230524, 0.76904224100091, 1.65090746733669, -0.119452606630659, -0.499292017172261, -0.119452606630659, -0.499292017172261, -0.530906522170303, 0.0847372921971965, 0.754053785184521, 0.368964527385086, -0.324685911490835, -1.23627311888329, 0.368964527385086, -0.119452606630659, -1.57214415914549, 0.298227591540715, 0.0847372921971965, -1.04917700666607, -0.363657297095253, 0.754053785184521, 1.23247587848534, 1.31241297643351, 0.56298953322048, -0.034067253738464, -1.51466765378175, 0.0597499373846007, 0.88465049897692, 1.10984813892972, 0.0377883991710788, -1.26015524475811, -0.280395335170247, -0.738527704739573, -0.374580857767014, -0.530906522170303, 0.243687429599092, -0.441163216905286, -1.01559257860354, -0.723065969939874, 1.67569693240319, 0.56298953322048, -1.4617555849959, 0.687916772975828, -0.374580857767014, 0.701784335374711, 1.65090746733669, -0.476246894615578, 0.243687429599092, -0.262197489402468, -0.499292017172261, -0.992507150392037, -1.06332613397119, -1.06332613397119, 0.600708823672418, 0.976973386685621, -0.0903195939658516, 1.31241297643351, -1.00837660827701, 0.298227591540715, -1.00837660827701, 1.23247587848534, -0.349650387953555, -1.31080153332797, 0.701784335374711, -0.594617267459511, 0.76904224100091, -0.895363357977542, 1.95529396549246, -0.895363357977542, -0.034067253738464, 1.95529396549246, -0.723065969939874, -1.23627311888329, 0.976973386685621, 0.418982404924464, 2.12845189901618, 0.707588353835588, -0.594617267459511, -1.31080153332797, 0.687916772975828, -1.28703047603518, -0.372438756103829, 1.65090746733669, 0.0945835281735714, -1.31701613230524, -0.594617267459511, -0.458365332711106, -0.611165916680421, -0.0540281250854405, -0.992507150392037, 0.636569674033849, 0.0597499373846007, -0.236279568941097, -1.18548008459731, -1.26015524475811, -0.741336096272828, -0.363657297095253, 0.707588353835588, 0.310480749443137, 1.26318517608949, -1.26015524475811, -1.01559257860354, -0.349650387953555, 0.754053785184521, -1.00837660827701, -1.31080153332797, 0.298227591540715, -0.236279568941097, 0.76904224100091, 0.707588353835588, -1.25127136162494, -1.26015524475811, -0.374580857767014, 0.701784335374711, -0.349650387953555, 0.516862044313609, 2.10010894052567, -1.23627311888329, -1.31701613230524, 0.368964527385086, -0.611165916680421, -1.06332613397119, 0.976973386685621, 0.0597499373846007, -1.57214415914549, 3.2410399349424, -0.741336096272828, 0.516862044313609, 0.54319405923209, -0.215380507641693, -0.374580857767014, 0.214538826629216, -0.70459646368007, -0.483780625708744, 0.701784335374711, 1.99721338474797, -0.57438868976327, -0.372438756103829, 1.23247587848534, -0.441163216905286, 3.2410399349424, -0.262197489402468, 0.298227591540715, 0.0945835281735714, 0.0847372921971965, -0.41433994791886, -0.280395335170247, -1.25127136162494, -0.992507150392037, -0.262197489402468, 0.0945835281735714, 0.0847372921971965, -1.31701613230524, -0.416857588160432, -1.23627311888329, 1.65090746733669, -0.0540281250854405, -1.06332613397119, 1.31241297643351, 1.26318517608949, 1.31241297643351, -0.0540281250854405, -0.573973479297987, -0.262197489402468, 0.310480749443137, -0.374580857767014, -0.441163216905286, -0.372438756103829, 0.707588353835588, -0.119452606630659, 0.418982404924464, 1.99721338474797, -1.06332613397119, -0.741336096272828, -0.280395335170247, -0.280395335170247, 2.10010894052567, -0.372438756103829, 1.99721338474797, -0.280395335170247, -1.01559257860354, -0.895363357977542, 0.617985817166529, -1.09599626707466, 0.976973386685621, 0.56298953322048, -0.280395335170247, -0.215380507641693, -1.38892030144774, 0.730027750453853, 0.234552704115215, 0.0681461754066568, 0.643992366314268, -0.43572975141948, -1.60282881636463, 0.29364999248409, 2.31402480242287, 0.0230259811923776, -0.672070013676843, -0.225948646704572, -1.34089123821519, -0.489449686093942, 1.1862121860468, -0.03028963458361, 0.0788717892826449, -1.78086890709098, -0.121672237032497, 1.0034304328788, -0.229187959782573, -1.68476991095509, 0.346654356013892, -1.3911603289879, -0.0514091606478224, -0.408350742640181, 0.386557785696473, -0.599289270019641, 0.478801778425106, 2.38572973590791, -2.62420422109518, -1.67431701633726, 0.739726247960841, 0.31983478985324, 0.927158985648812, 0.603061078503197, -0.302271094587873, -0.0527369357041771, -0.25951945082025, -0.188211795289035, 1.35275953102268, -1.01290579656789, -0.97493344130451, 1.07696007114566, 0.457006442518673, -0.17387047863048, 0.789976072257721, 1.38852064370227, -0.787996658311867, -1.26564111552514, 0.221183153970504, -0.769035435990812, -0.401783062581892, -1.52229505586456, -0.178528344442375, -0.338608290444984, -1.02226114339887, -0.455963592179821, -0.102040922937136, 2.41919837909073, 1.84000532042574, -2.09365949618254, 0.738405120166828, 1.72002521705752, 1.40727535557759, 1.21250017241327, 0.483548222758434, 0.248634407578129, -0.180893469023116, -0.947984281770593, 0.670263861008379, 0.0408142909684297, -0.613947884095503, 0.294737911419117, -0.221253502977999, -0.398913818769696, 0.510903762682928, 0.942719789953502, -1.04439240924221, 0.398672425272152, 1.72383065667675, -0.351921148534632, -1.36954880596103, -0.563789210220781, -0.228789486007742, 0.431373623406222, -0.20688641314428, -0.232009201680886, -1.30881091771916, 0.805275888876049, 0.324956824938195, -0.592373984500934, -1.45322149987626, 1.02307611517736, 0.156612964820455, 1.35164066371437, 0.553154268076795, -1.11416333028041, 0.0452766241979749, 2.11392059206364, -1.37142438915542, 0.47040205488933, -1.85980162944181, -0.892725967722428, 0.550332547415438, -0.794541071709226, -0.0333719847781946, -2.73257554856829, -1.66339720361606, 0.223833956609664, 1.47656179355303, 0.847634947652214, 0.160471006993165, -0.645170739684543, -1.96094607976103, -1.393965736165, 0.602214522063677, -0.334363199851311, -0.00685196828492263, -0.776053067556501, -0.213419391285488, -0.483033596418618, -0.779929796305915, 0.0718118521131252, -0.270608190362966, -1.26696202866618, -0.593621344224326, 1.08379156636067, -1.56992445153843, -1.08827034303695, -0.729347638582214, -0.777764784179114, -0.016297687111955, -1.13888681442717, -0.212054993329965, -0.247672603997786, 0.993657694681047, -0.396386046298297, 2.21583491775214, 0.160833125864682, -0.0729913528868687, -0.651740475070305, 0.759988324022376, 1.01148053163328, 0.891010143846125, 0.0459130015398892, 0.519329947691572, 0.180471375121504, -0.165262546492826, 0.969721905944203, 0.56882331565379, -1.32330611743958, 0.825372051980621, -1.91420756838995, 0.0678177404776742, 0.413785457977836, -0.117899158509332, -0.952212621853439, -0.43179743399057, -0.500911457274318, 1.59199728057924, 0.746524595192507, 1.74861677045671, -0.117788148024258, 0.251284243382911, -0.0629205148258259, 1.23234252986996, -0.229712212603064, -0.66487951016619, 0.22149673400402, -1.39990628053775, 0.438262082627285, -2.62461743899758, -0.0404480314188124, 1.58571009869214, -0.808039226489727, 0.396442270096405, -1.08865943108627, 1.08539625457053, 1.19772324388373, -1.24402520393183, 0.993194633615532, 2.55662517667628, -0.202981373932045, 1.14060544585341, -0.640788098394918, 1.39235474688611, -0.954706020391458, 1.03628994385804, 0.634488298400074, 0.387930785516864, 0.218547649295692, -0.276990784332844, 0.408237951849533, 0.118249457909391, 0.284307239834182, -1.38596218500665, -0.066716620424021, -0.604679504135337, -0.306440965198335, 0.910422325885682, 2.65296599367053, -0.417795040904441, -1.65368931657805, 1.61365660312281, -0.710094850547078, -0.311523582558323, 1.11773903810845, -0.529806393298965, -0.616933410914125, -0.187737688973872, -1.14501666106255, -0.487703723062109, -0.504946666404446, 1.30958941043932, -0.0882272636445799, 0.203441887730905, 0.527124410347688, -0.244120317170367, -1.62328228747231, -0.462748895973649, 1.17608717954651, 0.643864693746541, 0.171190167274327, 0.911192353557058, 0.316690232943516, -1.27090846250727, -1.41769608194333, -0.606346252970155, 0.162014559704842, 0.578807126144846, 0.141226846027082, 0.909732365336242, -1.8347333611157, -0.625400325101487, 0.931195009063406, -1.25492923889488, 2.9879517716602, -0.388605949792868, -0.426946406565663, -0.441345589531624, 0.346388480946732, 1.11100081290011, 0.681049947331931, 0.34351540664961, -0.564443262471138, 1.08404074822488, -0.558523641270207, 0.170277165488717, -0.284779423565062, -1.07373926940842, 0.642193915240031, -0.757312572402853, -0.00193005864032314, 0.105205419475864, 2.19521890376177, 0.505728179919568, 0.0996856379391837, 1.14393097348841, -0.272809620709314, -1.43750739479717, -1.17965651948571, 0.334511315033989, -0.552598543043276, 0.382316618630531, 1.44950822843011, 0.537138674625523, -0.161181176377435, -0.292395486371033, -1.15600967093991, 0.224252242948389, -0.979969250237834, -1.0631865500922, 0.622318567134563, -0.466578763916795, 0.102001523444305, -0.473009482161808, -3.23091830578033, -2.11221123235875, 2.24110659636699, -0.231608852381857, -0.0158065519520948, -0.258763634777808, -0.588624438986248, 1.49355898517264, 0.409719342048034, -0.558771497529283, 1.08250496509779, 0.0116624481161528, -0.468476464354708, -2.26700962047404, 0.388760878424406, 0.366509874419916, 0.646733523897682, -0.768945359792974, -1.54784230524557, 1.97182669378426, 1.45083758194726, 0.668919279239853, 2.29916491401138, 1.11179103771506, 0.0366641119435814, -0.827814457432656, 0.698568965964181, -0.403140448349392, -0.129544855344173, 0.28374362229638, -0.627260414737765, -0.28477507343038, 0.734718433840792, 0.63991214696638, 0.42103928604782, -1.27804840732161, -0.588235333262095, -0.272218324830386, -1.33640553445707, 1.96059606076205, -1.43192302380368, 0.771478468705446, -0.448779128823081, -1.71123691895019, -0.44717784797179, 0.872642903797512, 0.0172435470972571, -0.156091176511188, -0.500495205056316, 0.695667662732051, 1.80962356907915, -0.695105204395499, -0.827073906478573, -1.74361140203617, -1.34629866379843, 1.514710286238, -0.468803927456235, 1.13140122231854, -1.21213800937968, -0.327474777823489, 0.160092466128878, 0.267718490671936, 0.132938879736172, 1.04199952102276, 0.863675899268996, -0.276756285615858, 0.226531129167382, 2.89084386436826, -0.31426812179635, -1.34570362230527, -1.47353389804505, -0.0555333562355194, -0.602172005041169, 0.250414742846566, 0.376569439971125, -0.136937526969143, -0.289185540367475, 1.01844599296818, -1.0898020407926, -0.672107415382938, 0.857473353196317, 0.263284246725938, 0.0523972526933849, -0.665611010955793, -0.107015680522293, -0.539745964300712, 0.546127357829245, 1.24171682685563, 1.22649619890921, 0.589460764617792, 0.0590344290809297, -0.549186422956771, 0.0631179476759736, 1.5666089822591, -0.0885032483214761, 0.295544900318868, -0.877041552433476, 0.214903481520077, 1.41507857879813, -0.896410488565367, -0.0407486986456548, 0.64083796652413, 0.0742075608322534, 1.92251120452101, 0.761209207305022, 1.39289185953756, 0.927867559939151, 0.529369628635474, -1.42080208356828, 0.234010189340094, 0.520942180831283, -0.292679580868902, -0.707042525734397, -0.804884829215233, 1.43524310204578, 0.580444407503398, -0.580024061779065, 0.76531901416266, -1.15489735647673, 2.42417841119804, 0.522354903078695, 0.749416613804051, -0.214255662246703), num_trees = 50, clusters = numeric(0), samples_per_cluster = 0, sample_fraction = 0.5, mtry = 3, min_node_size = 5, honesty = TRUE, honesty_fraction = 0.5, honesty_prune_leaves = TRUE, alpha = 0.05, imbalance_penalty = 0, ci_group_size = 1, compute_oob_predictions = TRUE, num_threads = 0, seed = 1673957982.2205, legacy_seed = FALSE, verbose = FALSE)
2: do.call(what, args, quote, envir)
3: do.call.rcpp(regression_train, c(data, args))
4: (function (X, Y, num.trees = 2000, sample.weights = NULL, clusters = NULL, equalize.cluster.weights = FALSE, sample.fraction = 0.5, mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)), min.node.size = 5, honesty = TRUE, honesty.fraction = 0.5, honesty.prune.leaves = TRUE, alpha = 0.05, imbalance.penalty = 0, ci.group.size = 2, tune.parameters = "none", tune.num.trees = 50, tune.num.reps = 100, tune.num.draws = 1000, compute.oob.predictions = TRUE, num.threads = NULL, seed = runif(1, 0, .Machine$integer.max)) { has.missing.values <- validate_X(X, allow.na = TRUE) validate_sample_weights(sample.weights, X) Y <- validate_observations(Y, X) clusters <- validate_clusters(clusters, X) samples.per.cluster <- validate_equalize_cluster_weights(equalize.cluster.weights, clusters, sample.weights) num.threads <- validate_num_threads(num.threads) all.tunable.params <- c("sample.fraction", "mtry", "min.node.size", "honesty.fraction", "honesty.prune.leaves", "alpha", "imbalance.penalty") default.parameters <- list(sample.fraction = 0.5, mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)), min.node.size = 5, honesty.fraction = 0.5, honesty.prune.leaves = TRUE, alpha = 0.05, imbalance.penalty = 0) data <- create_train_matrices(X, outcome = Y, sample.weights = sample.weights) args <- list(num.trees = num.trees, clusters = clusters, samples.per.cluster = samples.per.cluster, sample.fraction = sample.fraction, mtry = mtry, min.node.size = min.node.size, honesty = honesty, honesty.fraction = honesty.fraction, honesty.prune.leaves = honesty.prune.leaves, alpha = alpha, imbalance.penalty = imbalance.penalty, ci.group.size = ci.group.size, compute.oob.predictions = compute.oob.predictions, num.threads = num.threads, seed = seed, legacy.seed = get_legacy_seed(), verbose = get_verbose()) tuning.output <- NULL if (!identical(tune.parameters, "none")) { if (identical(tune.parameters, "all")) { tune.parameters <- all.tunable.params } else { tune.parameters <- unique(match.arg(tune.parameters, all.tunable.params, several.ok = TRUE)) } if (!honesty) { tune.parameters <- tune.parameters[!grepl("honesty", tune.parameters)] } tune.parameters.defaults <- default.parameters[tune.parameters] tuning.output <- tune_forest(data = data, nrow.X = nrow(X), ncol.X = ncol(X), args = args, tune.parameters = tune.parameters, tune.parameters.defaults = tune.parameters.defaults, tune.num.trees = tune.num.trees, tune.num.reps = tune.num.reps, tune.num.draws = tune.num.draws, train = regression_train) args <- utils::modifyList(args, as.list(tuning.output[["params"]])) } forest <- do.call.rcpp(regression_train, c(data, args)) class(forest) <- c("regression_forest", "grf") forest[["seed"]] <- seed forest[["num.threads"]] <- num.threads forest[["ci.group.size"]] <- ci.group.size forest[["X.orig"]] <- X forest[["Y.orig"]] <- Y forest[["sample.weights"]] <- sample.weights forest[["clusters"]] <- clusters forest[["equalize.cluster.weights"]] <- equalize.cluster.weights forest[["tunable.params"]] <- args[all.tunable.params] forest[["tuning.output"]] <- tuning.output forest[["has.missing.values"]] <- has.missing.values forest})(Y = c(-1.38892030144774, 0.730027750453853, 0.234552704115215, 0.0681461754066568, 0.643992366314268, -0.43572975141948, -1.60282881636463, 0.29364999248409, 2.31402480242287, 0.0230259811923776, -0.672070013676843, -0.225948646704572, -1.34089123821519, -0.489449686093942, 1.1862121860468, -0.03028963458361, 0.0788717892826449, -1.78086890709098, -0.121672237032497, 1.0034304328788, -0.229187959782573, -1.68476991095509, 0.346654356013892, -1.3911603289879, -0.0514091606478224, -0.408350742640181, 0.386557785696473, -0.599289270019641, 0.478801778425106, 2.38572973590791, -2.62420422109518, -1.67431701633726, 0.739726247960841, 0.31983478985324, 0.927158985648812, 0.603061078503197, -0.302271094587873, -0.0527369357041771, -0.25951945082025, -0.188211795289035, 1.35275953102268, -1.01290579656789, -0.97493344130451, 1.07696007114566, 0.457006442518673, -0.17387047863048, 0.789976072257721, 1.38852064370227, -0.787996658311867, -1.26564111552514, 0.221183153970504, -0.769035435990812, -0.401783062581892, -1.52229505586456, -0.178528344442375, -0.338608290444984, -1.02226114339887, -0.455963592179821, -0.102040922937136, 2.41919837909073, 1.84000532042574, -2.09365949618254, 0.738405120166828, 1.72002521705752, 1.40727535557759, 1.21250017241327, 0.483548222758434, 0.248634407578129, -0.180893469023116, -0.947984281770593, 0.670263861008379, 0.0408142909684297, -0.613947884095503, 0.294737911419117, -0.221253502977999, -0.398913818769696, 0.510903762682928, 0.942719789953502, -1.04439240924221, 0.398672425272152, 1.72383065667675, -0.351921148534632, -1.36954880596103, -0.563789210220781, -0.228789486007742, 0.431373623406222, -0.20688641314428, -0.232009201680886, -1.30881091771916, 0.805275888876049, 0.324956824938195, -0.592373984500934, -1.45322149987626, 1.02307611517736, 0.156612964820455, 1.35164066371437, 0.553154268076795, -1.11416333028041, 0.0452766241979749, 2.11392059206364, -1.37142438915542, 0.47040205488933, -1.85980162944181, -0.892725967722428, 0.550332547415438, -0.794541071709226, -0.0333719847781946, -2.73257554856829, -1.66339720361606, 0.223833956609664, 1.47656179355303, 0.847634947652214, 0.160471006993165, -0.645170739684543, -1.96094607976103, -1.393965736165, 0.602214522063677, -0.334363199851311, -0.00685196828492263, -0.776053067556501, -0.213419391285488, -0.483033596418618, -0.779929796305915, 0.0718118521131252, -0.270608190362966, -1.26696202866618, -0.593621344224326, 1.08379156636067, -1.56992445153843, -1.08827034303695, -0.729347638582214, -0.777764784179114, -0.016297687111955, -1.13888681442717, -0.212054993329965, -0.247672603997786, 0.993657694681047, -0.396386046298297, 2.21583491775214, 0.160833125864682, -0.0729913528868687, -0.651740475070305, 0.759988324022376, 1.01148053163328, 0.891010143846125, 0.0459130015398892, 0.519329947691572, 0.180471375121504, -0.165262546492826, 0.969721905944203, 0.56882331565379, -1.32330611743958, 0.825372051980621, -1.91420756838995, 0.0678177404776742, 0.413785457977836, -0.117899158509332, -0.952212621853439, -0.43179743399057, -0.500911457274318, 1.59199728057924, 0.746524595192507, 1.74861677045671, -0.117788148024258, 0.251284243382911, -0.0629205148258259, 1.23234252986996, -0.229712212603064, -0.66487951016619, 0.22149673400402, -1.39990628053775, 0.438262082627285, -2.62461743899758, -0.0404480314188124, 1.58571009869214, -0.808039226489727, 0.396442270096405, -1.08865943108627, 1.08539625457053, 1.19772324388373, -1.24402520393183, 0.993194633615532, 2.55662517667628, -0.202981373932045, 1.14060544585341, -0.640788098394918, 1.39235474688611, -0.954706020391458, 1.03628994385804, 0.634488298400074, 0.387930785516864, 0.218547649295692, -0.276990784332844, 0.408237951849533, 0.118249457909391, 0.284307239834182, -1.38596218500665, -0.066716620424021, -0.604679504135337, -0.306440965198335, 0.910422325885682, 2.65296599367053, -0.417795040904441, -1.65368931657805, 1.61365660312281, -0.710094850547078, -0.311523582558323, 1.11773903810845, -0.529806393298965, -0.616933410914125, -0.187737688973872, -1.14501666106255, -0.487703723062109, -0.504946666404446, 1.30958941043932, -0.0882272636445799, 0.203441887730905, 0.527124410347688, -0.244120317170367, -1.62328228747231, -0.462748895973649, 1.17608717954651, 0.643864693746541, 0.171190167274327, 0.911192353557058, 0.316690232943516, -1.27090846250727, -1.41769608194333, -0.606346252970155, 0.162014559704842, 0.578807126144846, 0.141226846027082, 0.909732365336242, -1.8347333611157, -0.625400325101487, 0.931195009063406, -1.25492923889488, 2.9879517716602, -0.388605949792868, -0.426946406565663, -0.441345589531624, 0.346388480946732, 1.11100081290011, 0.681049947331931, 0.34351540664961, -0.564443262471138, 1.08404074822488, -0.558523641270207, 0.170277165488717, -0.284779423565062, -1.07373926940842, 0.642193915240031, -0.757312572402853, -0.00193005864032314, 0.105205419475864, 2.19521890376177, 0.505728179919568, 0.0996856379391837, 1.14393097348841, -0.272809620709314, -1.43750739479717, -1.17965651948571, 0.334511315033989, -0.552598543043276, 0.382316618630531, 1.44950822843011, 0.537138674625523, -0.161181176377435, -0.292395486371033, -1.15600967093991, 0.224252242948389, -0.979969250237834, -1.0631865500922, 0.622318567134563, -0.466578763916795, 0.102001523444305, -0.473009482161808, -3.23091830578033, -2.11221123235875, 2.24110659636699, -0.231608852381857, -0.0158065519520948, -0.258763634777808, -0.588624438986248, 1.49355898517264, 0.409719342048034, -0.558771497529283, 1.08250496509779, 0.0116624481161528, -0.468476464354708, -2.26700962047404, 0.388760878424406, 0.366509874419916, 0.646733523897682, -0.768945359792974, -1.54784230524557, 1.97182669378426, 1.45083758194726, 0.668919279239853, 2.29916491401138, 1.11179103771506, 0.0366641119435814, -0.827814457432656, 0.698568965964181, -0.403140448349392, -0.129544855344173, 0.28374362229638, -0.627260414737765, -0.28477507343038, 0.734718433840792, 0.63991214696638, 0.42103928604782, -1.27804840732161, -0.588235333262095, -0.272218324830386, -1.33640553445707, 1.96059606076205, -1.43192302380368, 0.771478468705446, -0.448779128823081, -1.71123691895019, -0.44717784797179, 0.872642903797512, 0.0172435470972571, -0.156091176511188, -0.500495205056316, 0.695667662732051, 1.80962356907915, -0.695105204395499, -0.827073906478573, -1.74361140203617, -1.34629866379843, 1.514710286238, -0.468803927456235, 1.13140122231854, -1.21213800937968, -0.327474777823489, 0.160092466128878, 0.267718490671936, 0.132938879736172, 1.04199952102276, 0.863675899268996, -0.276756285615858, 0.226531129167382, 2.89084386436826, -0.31426812179635, -1.34570362230527, -1.47353389804505, -0.0555333562355194, -0.602172005041169, 0.250414742846566, 0.376569439971125, -0.136937526969143, -0.289185540367475, 1.01844599296818, -1.0898020407926, -0.672107415382938, 0.857473353196317, 0.263284246725938, 0.0523972526933849, -0.665611010955793, -0.107015680522293, -0.539745964300712, 0.546127357829245, 1.24171682685563, 1.22649619890921, 0.589460764617792, 0.0590344290809297, -0.549186422956771, 0.0631179476759736, 1.5666089822591, -0.0885032483214761, 0.295544900318868, -0.877041552433476, 0.214903481520077, 1.41507857879813, -0.896410488565367, -0.0407486986456548, 0.64083796652413, 0.0742075608322534, 1.92251120452101, 0.761209207305022, 1.39289185953756, 0.927867559939151, 0.529369628635474, -1.42080208356828, 0.234010189340094, 0.520942180831283, -0.292679580868902, -0.707042525734397, -0.804884829215233, 1.43524310204578, 0.580444407503398, -0.580024061779065, 0.76531901416266, -1.15489735647673, 2.42417841119804, 0.522354903078695, 0.749416613804051, -0.214255662246703), X = c(1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 2, 3, 3, 2, 4, 2, 4, 1, 2, 5, 1, 5, 3, 3, 1, 3, 5, 1, 3, 4, 1, 2, 2, 1, 5, 4, 5, 4, 5, 2, 3, 4, 2, 4, 3, 4, 5, 5, 3, 4, 3, 4, 1, 5, 1, 4, 3, 2, 3, 1, 5, 5, 4, 5, 4, 4, 5, 3, 5, 2, 5, 3, 1, 4, 2, 4, 4, 1, 4, 5, 3, 1, 4, 2, 3, 1, 3, 4, 4, 2, 5, 5, 5, 3, 4, 3, 5, 3, 1, 1, 2, 5, 5, 5, 2, 3, 3, 4, 1, 1, 2, 5, 1, 5, 3, 3, 2, 5, 5, 4, 4, 1, 5, 4, 4, 2, 5, 5, 4, 1, 1, 4, 1, 1, 4, 4, 1, 1, 1, 2, 3, 4, 5, 3, 1, 5, 5, 4, 2, 1, 5, 2, 3, 1, 5, 1, 5, 4, 1, 3, 1, 4, 4, 1, 2, 1, 3, 1, 5, 4, 1, 5, 2, 3, 5, 5, 3, 5, 2, 2, 5, 4, 5, 5, 3, 5, 2, 3, 1, 3, 5, 2, 3, 3, 5, 5, 3, 1, 3, 3, 2, 3, 3, 2, 1, 5, 4, 4, 2, 4, 1, 1, 4, 4, 4, 3, 1, 1, 3, 5, 5, 1, 5, 5, 1, 2, 1, 2, 1, 4, 1, 4, 3, 4, 1, 3, 2, 1, 1, 1, 4, 5, 4, 5, 3, 2, 5, 1, 4, 3, 1, 4, 1, 2, 3, 2, 4, 4, 1, 1, 2, 5, 2, 1, 3, 2, 1, 1, 2, 4, 4, 1, 5, 2, 2, 4, 2, 5, 3, 3, 1, 3, 5, 2, 4, 1, 5, 5, 5, 1, 5, 5, 1, 2, 1, 2, 3, 3, 2, 5, 4, 3, 3, 4, 1, 3, 5, 5, 1, 2, 4, 5, 3, 4, 3, 3, 1, 4, 5, 2, 5, 1, 1, 1, 2, 4, 3, 4, 1, 3, 5, 2, 1, 1, 2, 1, 2, 2, 5, 1, 5, 3, 2, 2, 2, 4, 1, 3, 4, 2, 5, 2, 2, 5, 2, 5, 1, 4, 2, 4, 5, 1, 3, 4, 1, 3, 1, 5, 2, 1, 5, 4, 3, 1, 5, 3, 1, 1, 4, 2, 3, 1, 3, 4, 5, 4, 5, 2, 1, 4, 2, 2, 3, 4, 2, 4, 2, 2, 5, 4, 4, 2, 1, 5, 5, 4, 2, 2, 2, 2, 0.707588353835588, -0.738527704739573, 0.418982404924464, 0.787738847475178, -0.324685911490835, -0.476246894615578, 0.0597499373846007, -1.57214415914549, -0.723065969939874, 0.54319405923209, -0.458365332711106, -0.865512862653374, 0.32430434416138, 0.436523478910183, 1.26318517608949, -1.18548008459731, 0.310480749443137, 1.26318517608949, 0.687916772975828, 0.600708823672418, 1.65090746733669, -0.349650387953555, -0.57438868976327, 1.26318517608949, -0.51606383094478, 2.19881034888372, 1.23247587848534, 1.10992028971364, 0.617985817166529, -0.280395335170247, -0.738527704739573, -1.31080153332797, -0.723065969939874, 0.600708823672418, -0.236279568941097, -0.741336096272828, 1.67569693240319, 0.332202578950118, -0.788602837850243, -1.04917700666607, 2.12845189901618, -1.04917700666607, -0.441163216905286, -0.573973479297987, 0.0847372921971965, 0.0597499373846007, 0.418982404924464, -1.2847157223178, -0.738527704739573, -1.57214415914549, -0.182925388372727, -0.992507150392037, -0.372438756103829, 0.54319405923209, 1.05271146557933, 0.0597499373846007, -0.034067253738464, 0.436523478910183, -0.594617267459511, -0.611165916680421, -0.182925388372727, 0.636569674033849, -1.23627311888329, 0.0377883991710788, -0.71721816157401, 0.754053785184521, -1.04917700666607, -1.23627311888329, -0.324685911490835, -0.865512862653374, 0.368964527385086, -0.499292017172261, -1.09599626707466, 0.787738847475178, 1.31241297643351, 0.701784335374711, 0.368964527385086, -1.31080153332797, -0.324685911490835, -0.349650387953555, 0.76904224100091, -1.31701613230524, -0.182925388372727, 0.687916772975828, 0.0597499373846007, 3.2410399349424, -0.895363357977542, -1.00837660827701, 1.95529396549246, -1.25127136162494, -0.723065969939874, 0.76904224100091, 0.214538826629216, 0.76904224100091, -0.349650387953555, 0.636569674033849, 0.418982404924464, 0.600708823672418, 1.65090746733669, -0.441163216905286, -0.611165916680421, 0.76904224100091, 1.65090746733669, 1.23247587848534, -1.00837660827701, 0.418982404924464, -0.119452606630659, 0.76904224100091, -0.594617267459511, -0.262197489402468, 1.10992028971364, -1.23627311888329, -0.0903195939658516, 0.0597499373846007, 1.10992028971364, 0.119245236427584, 0.332202578950118, -0.573973479297987, -0.0540281250854405, 0.0847372921971965, 1.95529396549246, -1.31080153332797, -0.458365332711106, -1.26015524475811, -0.262197489402468, -1.09599626707466, -1.26015524475811, 1.95529396549246, -0.499292017172261, -0.280395335170247, 0.88465049897692, -1.09599626707466, -0.51606383094478, -0.416857588160432, -1.57214415914549, -0.41433994791886, 2.10010894052567, 0.243687429599092, -0.476246894615578, 1.65090746733669, 0.617985817166529, 0.119245236427584, 2.12845189901618, -0.781536487054751, -0.41433994791886, -1.26015524475811, -0.034067253738464, -1.31080153332797, 1.10984813892972, -0.236279568941097, 1.65090746733669, -0.741336096272828, 1.05271146557933, -0.458365332711106, 0.516862044313609, -1.26015524475811, 0.0945835281735714, -0.265145056696353, 0.0652930335253153, -0.0540281250854405, -0.458365332711106, 1.67569693240319, -0.280395335170247, 0.418982404924464, -0.51606383094478, 1.23247587848534, 0.0945835281735714, 0.54319405923209, 0.976973386685621, 0.56298953322048, 0.214538826629216, -0.262197489402468, -0.992507150392037, 2.10010894052567, 0.636569674033849, 1.23247587848534, -0.119452606630659, 0.418982404924464, -1.51466765378175, 0.418982404924464, -0.865512862653374, 0.787738847475178, -0.236279568941097, 0.636569674033849, -0.182925388372727, 0.332202578950118, 0.418982404924464, -1.26015524475811, -1.60153617357459, -0.788602837850243, 0.707588353835588, -1.28703047603518, 0.436523478910183, -1.06332613397119, 0.701784335374711, 0.332202578950118, -0.530906522170303, 1.99721338474797, -0.349650387953555, -0.197175894348552, 1.10984813892972, -1.51466765378175, -0.262197489402468, -0.0540281250854405, 0.0377883991710788, -1.18548008459731, 0.701784335374711, 0.298227591540715, -1.28703047603518, -0.483780625708744, 0.332202578950118, 1.26318517608949, -1.31701613230524, 0.76904224100091, 1.65090746733669, -0.119452606630659, -0.499292017172261, -0.119452606630659, -0.499292017172261, -0.530906522170303, 0.0847372921971965, 0.754053785184521, 0.368964527385086, -0.324685911490835, -1.23627311888329, 0.368964527385086, -0.119452606630659, -1.57214415914549, 0.298227591540715, 0.0847372921971965, -1.04917700666607, -0.363657297095253, 0.754053785184521, 1.23247587848534, 1.31241297643351, 0.56298953322048, -0.034067253738464, -1.51466765378175, 0.0597499373846007, 0.88465049897692, 1.10984813892972, 0.0377883991710788, -1.26015524475811, -0.280395335170247, -0.738527704739573, -0.374580857767014, -0.530906522170303, 0.243687429599092, -0.441163216905286, -1.01559257860354, -0.723065969939874, 1.67569693240319, 0.56298953322048, -1.4617555849959, 0.687916772975828, -0.374580857767014, 0.701784335374711, 1.65090746733669, -0.476246894615578, 0.243687429599092, -0.262197489402468, -0.499292017172261, -0.992507150392037, -1.06332613397119, -1.06332613397119, 0.600708823672418, 0.976973386685621, -0.0903195939658516, 1.31241297643351, -1.00837660827701, 0.298227591540715, -1.00837660827701, 1.23247587848534, -0.349650387953555, -1.31080153332797, 0.701784335374711, -0.594617267459511, 0.76904224100091, -0.895363357977542, 1.95529396549246, -0.895363357977542, -0.034067253738464, 1.95529396549246, -0.723065969939874, -1.23627311888329, 0.976973386685621, 0.418982404924464, 2.12845189901618, 0.707588353835588, -0.594617267459511, -1.31080153332797, 0.687916772975828, -1.28703047603518, -0.372438756103829, 1.65090746733669, 0.0945835281735714, -1.31701613230524, -0.594617267459511, -0.458365332711106, -0.611165916680421, -0.0540281250854405, -0.992507150392037, 0.636569674033849, 0.0597499373846007, -0.236279568941097, -1.18548008459731, -1.26015524475811, -0.741336096272828, -0.363657297095253, 0.707588353835588, 0.310480749443137, 1.26318517608949, -1.26015524475811, -1.01559257860354, -0.349650387953555, 0.754053785184521, -1.00837660827701, -1.31080153332797, 0.298227591540715, -0.236279568941097, 0.76904224100091, 0.707588353835588, -1.25127136162494, -1.26015524475811, -0.374580857767014, 0.701784335374711, -0.349650387953555, 0.516862044313609, 2.10010894052567, -1.23627311888329, -1.31701613230524, 0.368964527385086, -0.611165916680421, -1.06332613397119, 0.976973386685621, 0.0597499373846007, -1.57214415914549, 3.2410399349424, -0.741336096272828, 0.516862044313609, 0.54319405923209, -0.215380507641693, -0.374580857767014, 0.214538826629216, -0.70459646368007, -0.483780625708744, 0.701784335374711, 1.99721338474797, -0.57438868976327, -0.372438756103829, 1.23247587848534, -0.441163216905286, 3.2410399349424, -0.262197489402468, 0.298227591540715, 0.0945835281735714, 0.0847372921971965, -0.41433994791886, -0.280395335170247, -1.25127136162494, -0.992507150392037, -0.262197489402468, 0.0945835281735714, 0.0847372921971965, -1.31701613230524, -0.416857588160432, -1.23627311888329, 1.65090746733669, -0.0540281250854405, -1.06332613397119, 1.31241297643351, 1.26318517608949, 1.31241297643351, -0.0540281250854405, -0.573973479297987, -0.262197489402468, 0.310480749443137, -0.374580857767014, -0.441163216905286, -0.372438756103829, 0.707588353835588, -0.119452606630659, 0.418982404924464, 1.99721338474797, -1.06332613397119, -0.741336096272828, -0.280395335170247, -0.280395335170247, 2.10010894052567, -0.372438756103829, 1.99721338474797, -0.280395335170247, -1.01559257860354, -0.895363357977542, 0.617985817166529, -1.09599626707466, 0.976973386685621, 0.56298953322048, -0.280395335170247, -0.215380507641693), num.trees = 50, sample.weights = NULL, clusters = numeric(0), equalize.cluster.weights = FALSE, sample.fraction = 0.5, mtry = 3, min.node.size = 5, honesty = TRUE, honesty.fraction = 0.5, honesty.prune.leaves = TRUE, alpha = 0.05, imbalance.penalty = 0, ci.group.size = 1, tune.parameters = "none", num.threads = 0, seed = 1673957982.2205)
5: do.call(regression_forest, c(Y = list(Y), args.orthog))
6: (function (X, Y, W, Y.hat = NULL, W.hat = NULL, num.trees = 2000, sample.weights = NULL, clusters = NULL, equalize.cluster.weights = FALSE, sample.fraction = 0.5, mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)), min.node.size = 5, honesty = TRUE, honesty.fraction = 0.5, honesty.prune.leaves = TRUE, alpha = 0.05, imbalance.penalty = 0, stabilize.splits = TRUE, ci.group.size = 2, tune.parameters = "none", tune.num.trees = 200, tune.num.reps = 50, tune.num.draws = 1000, compute.oob.predictions = TRUE, num.threads = NULL, seed = runif(1, 0, .Machine$integer.max)) { has.missing.values <- validate_X(X, allow.na = TRUE) validate_sample_weights(sample.weights, X) Y <- validate_observations(Y, X) W <- validate_observations(W, X) clusters <- validate_clusters(clusters, X) samples.per.cluster <- validate_equalize_cluster_weights(equalize.cluster.weights, clusters, sample.weights) num.threads <- validate_num_threads(num.threads) all.tunable.params <- c("sample.fraction", "mtry", "min.node.size", "honesty.fraction", "honesty.prune.leaves", "alpha", "imbalance.penalty") default.parameters <- list(sample.fraction = 0.5, mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)), min.node.size = 5, honesty.fraction = 0.5, honesty.prune.leaves = TRUE, alpha = 0.05, imbalance.penalty = 0) args.orthog <- list(X = X, num.trees = max(50, num.trees/4), sample.weights = sample.weights, clusters = clusters, equalize.cluster.weights = equalize.cluster.weights, sample.fraction = sample.fraction, mtry = mtry, min.node.size = 5, honesty = TRUE, honesty.fraction = 0.5, honesty.prune.leaves = honesty.prune.leaves, alpha = alpha, imbalance.penalty = imbalance.penalty, ci.group.size = 1, tune.parameters = tune.parameters, num.threads = num.threads, seed = seed) if (is.null(Y.hat)) { forest.Y <- do.call(regression_forest, c(Y = list(Y), args.orthog)) Y.hat <- predict(forest.Y)$predictions } else if (length(Y.hat) == 1) { Y.hat <- rep(Y.hat, nrow(X)) } else if (length(Y.hat) != nrow(X)) { stop("Y.hat has incorrect length.") } if (is.null(W.hat)) { forest.W <- do.call(regression_forest, c(Y = list(W), args.orthog)) W.hat <- predict(forest.W)$predictions } else if (length(W.hat) == 1) { W.hat <- rep(W.hat, nrow(X)) } else if (length(W.hat) != nrow(X)) { stop("W.hat has incorrect length.") } Y.centered <- Y - Y.hat W.centered <- W - W.hat data <- create_train_matrices(X, outcome = Y.centered, treatment = W.centered, sample.weights = sample.weights) args <- list(num.trees = num.trees, clusters = clusters, samples.per.cluster = samples.per.cluster, sample.fraction = sample.fraction, mtry = mtry, min.node.size = min.node.size, honesty = honesty, honesty.fraction = honesty.fraction, honesty.prune.leaves = honesty.prune.leaves, alpha = alpha, imbalance.penalty = imbalance.penalty, stabilize.splits = stabilize.splits, ci.group.size = ci.group.size, compute.oob.predictions = compute.oob.predictions, num.threads = num.threads, seed = seed, reduced.form.weight = 0, legacy.seed = get_legacy_seed(), verbose = get_verbose()) tuning.output <- NULL if (!identical(tune.parameters, "none")) { if (identical(tune.parameters, "all")) { tune.parameters <- all.tunable.params } else { tune.parameters <- unique(match.arg(tune.parameters, all.tunable.params, several.ok = TRUE)) } if (!honesty) { tune.parameters <- tune.parameters[!grepl("honesty", tune.parameters)] } tune.parameters.defaults <- default.parameters[tune.parameters] tuning.output <- tune_forest(data = data, nrow.X = nrow(X), ncol.X = ncol(X), args = args, tune.parameters = tune.parameters, tune.parameters.defaults = tune.parameters.defaults, tune.num.trees = tune.num.trees, tune.num.reps = tune.num.reps, tune.num.draws = tune.num.draws, train = causal_train) args <- utils::modifyList(args, as.list(tuning.output[["params"]])) } forest <- do.call.rcpp(causal_train, c(data, args)) class(forest) <- c("causal_forest", "grf") forest[["seed"]] <- seed forest[["num.threads"]] <- num.threads forest[["ci.group.size"]] <- ci.group.size forest[["X.orig"]] <- X forest[["Y.orig"]] <- Y forest[["W.orig"]] <- W forest[["Y.hat"]] <- Y.hat forest[["W.hat"]] <- W.hat forest[["clusters"]] <- clusters forest[["equalize.cluster.weights"]] <- equalize.cluster.weights forest[["sample.weights"]] <- sample.weights forest[["tunable.params"]] <- args[all.tunable.params] forest[["tuning.output"]] <- tuning.output forest[["has.missing.values"]] <- has.missing.values forest})(X = c(1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 2, 3, 3, 2, 4, 2, 4, 1, 2, 5, 1, 5, 3, 3, 1, 3, 5, 1, 3, 4, 1, 2, 2, 1, 5, 4, 5, 4, 5, 2, 3, 4, 2, 4, 3, 4, 5, 5, 3, 4, 3, 4, 1, 5, 1, 4, 3, 2, 3, 1, 5, 5, 4, 5, 4, 4, 5, 3, 5, 2, 5, 3, 1, 4, 2, 4, 4, 1, 4, 5, 3, 1, 4, 2, 3, 1, 3, 4, 4, 2, 5, 5, 5, 3, 4, 3, 5, 3, 1, 1, 2, 5, 5, 5, 2, 3, 3, 4, 1, 1, 2, 5, 1, 5, 3, 3, 2, 5, 5, 4, 4, 1, 5, 4, 4, 2, 5, 5, 4, 1, 1, 4, 1, 1, 4, 4, 1, 1, 1, 2, 3, 4, 5, 3, 1, 5, 5, 4, 2, 1, 5, 2, 3, 1, 5, 1, 5, 4, 1, 3, 1, 4, 4, 1, 2, 1, 3, 1, 5, 4, 1, 5, 2, 3, 5, 5, 3, 5, 2, 2, 5, 4, 5, 5, 3, 5, 2, 3, 1, 3, 5, 2, 3, 3, 5, 5, 3, 1, 3, 3, 2, 3, 3, 2, 1, 5, 4, 4, 2, 4, 1, 1, 4, 4, 4, 3, 1, 1, 3, 5, 5, 1, 5, 5, 1, 2, 1, 2, 1, 4, 1, 4, 3, 4, 1, 3, 2, 1, 1, 1, 4, 5, 4, 5, 3, 2, 5, 1, 4, 3, 1, 4, 1, 2, 3, 2, 4, 4, 1, 1, 2, 5, 2, 1, 3, 2, 1, 1, 2, 4, 4, 1, 5, 2, 2, 4, 2, 5, 3, 3, 1, 3, 5, 2, 4, 1, 5, 5, 5, 1, 5, 5, 1, 2, 1, 2, 3, 3, 2, 5, 4, 3, 3, 4, 1, 3, 5, 5, 1, 2, 4, 5, 3, 4, 3, 3, 1, 4, 5, 2, 5, 1, 1, 1, 2, 4, 3, 4, 1, 3, 5, 2, 1, 1, 2, 1, 2, 2, 5, 1, 5, 3, 2, 2, 2, 4, 1, 3, 4, 2, 5, 2, 2, 5, 2, 5, 1, 4, 2, 4, 5, 1, 3, 4, 1, 3, 1, 5, 2, 1, 5, 4, 3, 1, 5, 3, 1, 1, 4, 2, 3, 1, 3, 4, 5, 4, 5, 2, 1, 4, 2, 2, 3, 4, 2, 4, 2, 2, 5, 4, 4, 2, 1, 5, 5, 4, 2, 2, 2, 2, 0.707588353835588, -0.738527704739573, 0.418982404924464, 0.787738847475178, -0.324685911490835, -0.476246894615578, 0.0597499373846007, -1.57214415914549, -0.723065969939874, 0.54319405923209, -0.458365332711106, -0.865512862653374, 0.32430434416138, 0.436523478910183, 1.26318517608949, -1.18548008459731, 0.310480749443137, 1.26318517608949, 0.687916772975828, 0.600708823672418, 1.65090746733669, -0.349650387953555, -0.57438868976327, 1.26318517608949, -0.51606383094478, 2.19881034888372, 1.23247587848534, 1.10992028971364, 0.617985817166529, -0.280395335170247, -0.738527704739573, -1.31080153332797, -0.723065969939874, 0.600708823672418, -0.236279568941097, -0.741336096272828, 1.67569693240319, 0.332202578950118, -0.788602837850243, -1.04917700666607, 2.12845189901618, -1.04917700666607, -0.441163216905286, -0.573973479297987, 0.0847372921971965, 0.0597499373846007, 0.418982404924464, -1.2847157223178, -0.738527704739573, -1.57214415914549, -0.182925388372727, -0.992507150392037, -0.372438756103829, 0.54319405923209, 1.05271146557933, 0.0597499373846007, -0.034067253738464, 0.436523478910183, -0.594617267459511, -0.611165916680421, -0.182925388372727, 0.636569674033849, -1.23627311888329, 0.0377883991710788, -0.71721816157401, 0.754053785184521, -1.04917700666607, -1.23627311888329, -0.324685911490835, -0.865512862653374, 0.368964527385086, -0.499292017172261, -1.09599626707466, 0.787738847475178, 1.31241297643351, 0.701784335374711, 0.368964527385086, -1.31080153332797, -0.324685911490835, -0.349650387953555, 0.76904224100091, -1.31701613230524, -0.182925388372727, 0.687916772975828, 0.0597499373846007, 3.2410399349424, -0.895363357977542, -1.00837660827701, 1.95529396549246, -1.25127136162494, -0.723065969939874, 0.76904224100091, 0.214538826629216, 0.76904224100091, -0.349650387953555, 0.636569674033849, 0.418982404924464, 0.600708823672418, 1.65090746733669, -0.441163216905286, -0.611165916680421, 0.76904224100091, 1.65090746733669, 1.23247587848534, -1.00837660827701, 0.418982404924464, -0.119452606630659, 0.76904224100091, -0.594617267459511, -0.262197489402468, 1.10992028971364, -1.23627311888329, -0.0903195939658516, 0.0597499373846007, 1.10992028971364, 0.119245236427584, 0.332202578950118, -0.573973479297987, -0.0540281250854405, 0.0847372921971965, 1.95529396549246, -1.31080153332797, -0.458365332711106, -1.26015524475811, -0.262197489402468, -1.09599626707466, -1.26015524475811, 1.95529396549246, -0.499292017172261, -0.280395335170247, 0.88465049897692, -1.09599626707466, -0.51606383094478, -0.416857588160432, -1.57214415914549, -0.41433994791886, 2.10010894052567, 0.243687429599092, -0.476246894615578, 1.65090746733669, 0.617985817166529, 0.119245236427584, 2.12845189901618, -0.781536487054751, -0.41433994791886, -1.26015524475811, -0.034067253738464, -1.31080153332797, 1.10984813892972, -0.236279568941097, 1.65090746733669, -0.741336096272828, 1.05271146557933, -0.458365332711106, 0.516862044313609, -1.26015524475811, 0.0945835281735714, -0.265145056696353, 0.0652930335253153, -0.0540281250854405, -0.458365332711106, 1.67569693240319, -0.280395335170247, 0.418982404924464, -0.51606383094478, 1.23247587848534, 0.0945835281735714, 0.54319405923209, 0.976973386685621, 0.56298953322048, 0.214538826629216, -0.262197489402468, -0.992507150392037, 2.10010894052567, 0.636569674033849, 1.23247587848534, -0.119452606630659, 0.418982404924464, -1.51466765378175, 0.418982404924464, -0.865512862653374, 0.787738847475178, -0.236279568941097, 0.636569674033849, -0.182925388372727, 0.332202578950118, 0.418982404924464, -1.26015524475811, -1.60153617357459, -0.788602837850243, 0.707588353835588, -1.28703047603518, 0.436523478910183, -1.06332613397119, 0.701784335374711, 0.332202578950118, -0.530906522170303, 1.99721338474797, -0.349650387953555, -0.197175894348552, 1.10984813892972, -1.51466765378175, -0.262197489402468, -0.0540281250854405, 0.0377883991710788, -1.18548008459731, 0.701784335374711, 0.298227591540715, -1.28703047603518, -0.483780625708744, 0.332202578950118, 1.26318517608949, -1.31701613230524, 0.76904224100091, 1.65090746733669, -0.119452606630659, -0.499292017172261, -0.119452606630659, -0.499292017172261, -0.530906522170303, 0.0847372921971965, 0.754053785184521, 0.368964527385086, -0.324685911490835, -1.23627311888329, 0.368964527385086, -0.119452606630659, -1.57214415914549, 0.298227591540715, 0.0847372921971965, -1.04917700666607, -0.363657297095253, 0.754053785184521, 1.23247587848534, 1.31241297643351, 0.56298953322048, -0.034067253738464, -1.51466765378175, 0.0597499373846007, 0.88465049897692, 1.10984813892972, 0.0377883991710788, -1.26015524475811, -0.280395335170247, -0.738527704739573, -0.374580857767014, -0.530906522170303, 0.243687429599092, -0.441163216905286, -1.01559257860354, -0.723065969939874, 1.67569693240319, 0.56298953322048, -1.4617555849959, 0.687916772975828, -0.374580857767014, 0.701784335374711, 1.65090746733669, -0.476246894615578, 0.243687429599092, -0.262197489402468, -0.499292017172261, -0.992507150392037, -1.06332613397119, -1.06332613397119, 0.600708823672418, 0.976973386685621, -0.0903195939658516, 1.31241297643351, -1.00837660827701, 0.298227591540715, -1.00837660827701, 1.23247587848534, -0.349650387953555, -1.31080153332797, 0.701784335374711, -0.594617267459511, 0.76904224100091, -0.895363357977542, 1.95529396549246, -0.895363357977542, -0.034067253738464, 1.95529396549246, -0.723065969939874, -1.23627311888329, 0.976973386685621, 0.418982404924464, 2.12845189901618, 0.707588353835588, -0.594617267459511, -1.31080153332797, 0.687916772975828, -1.28703047603518, -0.372438756103829, 1.65090746733669, 0.0945835281735714, -1.31701613230524, -0.594617267459511, -0.458365332711106, -0.611165916680421, -0.0540281250854405, -0.992507150392037, 0.636569674033849, 0.0597499373846007, -0.236279568941097, -1.18548008459731, -1.26015524475811, -0.741336096272828, -0.363657297095253, 0.707588353835588, 0.310480749443137, 1.26318517608949, -1.26015524475811, -1.01559257860354, -0.349650387953555, 0.754053785184521, -1.00837660827701, -1.31080153332797, 0.298227591540715, -0.236279568941097, 0.76904224100091, 0.707588353835588, -1.25127136162494, -1.26015524475811, -0.374580857767014, 0.701784335374711, -0.349650387953555, 0.516862044313609, 2.10010894052567, -1.23627311888329, -1.31701613230524, 0.368964527385086, -0.611165916680421, -1.06332613397119, 0.976973386685621, 0.0597499373846007, -1.57214415914549, 3.2410399349424, -0.741336096272828, 0.516862044313609, 0.54319405923209, -0.215380507641693, -0.374580857767014, 0.214538826629216, -0.70459646368007, -0.483780625708744, 0.701784335374711, 1.99721338474797, -0.57438868976327, -0.372438756103829, 1.23247587848534, -0.441163216905286, 3.2410399349424, -0.262197489402468, 0.298227591540715, 0.0945835281735714, 0.0847372921971965, -0.41433994791886, -0.280395335170247, -1.25127136162494, -0.992507150392037, -0.262197489402468, 0.0945835281735714, 0.0847372921971965, -1.31701613230524, -0.416857588160432, -1.23627311888329, 1.65090746733669, -0.0540281250854405, -1.06332613397119, 1.31241297643351, 1.26318517608949, 1.31241297643351, -0.0540281250854405, -0.573973479297987, -0.262197489402468, 0.310480749443137, -0.374580857767014, -0.441163216905286, -0.372438756103829, 0.707588353835588, -0.119452606630659, 0.418982404924464, 1.99721338474797, -1.06332613397119, -0.741336096272828, -0.280395335170247, -0.280395335170247, 2.10010894052567, -0.372438756103829, 1.99721338474797, -0.280395335170247, -1.01559257860354, -0.895363357977542, 0.617985817166529, -1.09599626707466, 0.976973386685621, 0.56298953322048, -0.280395335170247, -0.215380507641693), Y = c(-1.38892030144774, 0.730027750453853, 0.234552704115215, 0.0681461754066568, 0.643992366314268, -0.43572975141948, -1.60282881636463, 0.29364999248409, 2.31402480242287, 0.0230259811923776, -0.672070013676843, -0.225948646704572, -1.34089123821519, -0.489449686093942, 1.1862121860468, -0.03028963458361, 0.0788717892826449, -1.78086890709098, -0.121672237032497, 1.0034304328788, -0.229187959782573, -1.68476991095509, 0.346654356013892, -1.3911603289879, -0.0514091606478224, -0.408350742640181, 0.386557785696473, -0.599289270019641, 0.478801778425106, 2.38572973590791, -2.62420422109518, -1.67431701633726, 0.739726247960841, 0.31983478985324, 0.927158985648812, 0.603061078503197, -0.302271094587873, -0.0527369357041771, -0.25951945082025, -0.188211795289035, 1.35275953102268, -1.01290579656789, -0.97493344130451, 1.07696007114566, 0.457006442518673, -0.17387047863048, 0.789976072257721, 1.38852064370227, -0.787996658311867, -1.26564111552514, 0.221183153970504, -0.769035435990812, -0.401783062581892, -1.52229505586456, -0.178528344442375, -0.338608290444984, -1.02226114339887, -0.455963592179821, -0.102040922937136, 2.41919837909073, 1.84000532042574, -2.09365949618254, 0.738405120166828, 1.72002521705752, 1.40727535557759, 1.21250017241327, 0.483548222758434, 0.248634407578129, -0.180893469023116, -0.947984281770593, 0.670263861008379, 0.0408142909684297, -0.613947884095503, 0.294737911419117, -0.221253502977999, -0.398913818769696, 0.510903762682928, 0.942719789953502, -1.04439240924221, 0.398672425272152, 1.72383065667675, -0.351921148534632, -1.36954880596103, -0.563789210220781, -0.228789486007742, 0.431373623406222, -0.20688641314428, -0.232009201680886, -1.30881091771916, 0.805275888876049, 0.324956824938195, -0.592373984500934, -1.45322149987626, 1.02307611517736, 0.156612964820455, 1.35164066371437, 0.553154268076795, -1.11416333028041, 0.0452766241979749, 2.11392059206364, -1.37142438915542, 0.47040205488933, -1.85980162944181, -0.892725967722428, 0.550332547415438, -0.794541071709226, -0.0333719847781946, -2.73257554856829, -1.66339720361606, 0.223833956609664, 1.47656179355303, 0.847634947652214, 0.160471006993165, -0.645170739684543, -1.96094607976103, -1.393965736165, 0.602214522063677, -0.334363199851311, -0.00685196828492263, -0.776053067556501, -0.213419391285488, -0.483033596418618, -0.779929796305915, 0.0718118521131252, -0.270608190362966, -1.26696202866618, -0.593621344224326, 1.08379156636067, -1.56992445153843, -1.08827034303695, -0.729347638582214, -0.777764784179114, -0.016297687111955, -1.13888681442717, -0.212054993329965, -0.247672603997786, 0.993657694681047, -0.396386046298297, 2.21583491775214, 0.160833125864682, -0.0729913528868687, -0.651740475070305, 0.759988324022376, 1.01148053163328, 0.891010143846125, 0.0459130015398892, 0.519329947691572, 0.180471375121504, -0.165262546492826, 0.969721905944203, 0.56882331565379, -1.32330611743958, 0.825372051980621, -1.91420756838995, 0.0678177404776742, 0.413785457977836, -0.117899158509332, -0.952212621853439, -0.43179743399057, -0.500911457274318, 1.59199728057924, 0.746524595192507, 1.74861677045671, -0.117788148024258, 0.251284243382911, -0.0629205148258259, 1.23234252986996, -0.229712212603064, -0.66487951016619, 0.22149673400402, -1.39990628053775, 0.438262082627285, -2.62461743899758, -0.0404480314188124, 1.58571009869214, -0.808039226489727, 0.396442270096405, -1.08865943108627, 1.08539625457053, 1.19772324388373, -1.24402520393183, 0.993194633615532, 2.55662517667628, -0.202981373932045, 1.14060544585341, -0.640788098394918, 1.39235474688611, -0.954706020391458, 1.03628994385804, 0.634488298400074, 0.387930785516864, 0.218547649295692, -0.276990784332844, 0.408237951849533, 0.118249457909391, 0.284307239834182, -1.38596218500665, -0.066716620424021, -0.604679504135337, -0.306440965198335, 0.910422325885682, 2.65296599367053, -0.417795040904441, -1.65368931657805, 1.61365660312281, -0.710094850547078, -0.311523582558323, 1.11773903810845, -0.529806393298965, -0.616933410914125, -0.187737688973872, -1.14501666106255, -0.487703723062109, -0.504946666404446, 1.30958941043932, -0.0882272636445799, 0.203441887730905, 0.527124410347688, -0.244120317170367, -1.62328228747231, -0.462748895973649, 1.17608717954651, 0.643864693746541, 0.171190167274327, 0.911192353557058, 0.316690232943516, -1.27090846250727, -1.41769608194333, -0.606346252970155, 0.162014559704842, 0.578807126144846, 0.141226846027082, 0.909732365336242, -1.8347333611157, -0.625400325101487, 0.931195009063406, -1.25492923889488, 2.9879517716602, -0.388605949792868, -0.426946406565663, -0.441345589531624, 0.346388480946732, 1.11100081290011, 0.681049947331931, 0.34351540664961, -0.564443262471138, 1.08404074822488, -0.558523641270207, 0.170277165488717, -0.284779423565062, -1.07373926940842, 0.642193915240031, -0.757312572402853, -0.00193005864032314, 0.105205419475864, 2.19521890376177, 0.505728179919568, 0.0996856379391837, 1.14393097348841, -0.272809620709314, -1.43750739479717, -1.17965651948571, 0.334511315033989, -0.552598543043276, 0.382316618630531, 1.44950822843011, 0.537138674625523, -0.161181176377435, -0.292395486371033, -1.15600967093991, 0.224252242948389, -0.979969250237834, -1.0631865500922, 0.622318567134563, -0.466578763916795, 0.102001523444305, -0.473009482161808, -3.23091830578033, -2.11221123235875, 2.24110659636699, -0.231608852381857, -0.0158065519520948, -0.258763634777808, -0.588624438986248, 1.49355898517264, 0.409719342048034, -0.558771497529283, 1.08250496509779, 0.0116624481161528, -0.468476464354708, -2.26700962047404, 0.388760878424406, 0.366509874419916, 0.646733523897682, -0.768945359792974, -1.54784230524557, 1.97182669378426, 1.45083758194726, 0.668919279239853, 2.29916491401138, 1.11179103771506, 0.0366641119435814, -0.827814457432656, 0.698568965964181, -0.403140448349392, -0.129544855344173, 0.28374362229638, -0.627260414737765, -0.28477507343038, 0.734718433840792, 0.63991214696638, 0.42103928604782, -1.27804840732161, -0.588235333262095, -0.272218324830386, -1.33640553445707, 1.96059606076205, -1.43192302380368, 0.771478468705446, -0.448779128823081, -1.71123691895019, -0.44717784797179, 0.872642903797512, 0.0172435470972571, -0.156091176511188, -0.500495205056316, 0.695667662732051, 1.80962356907915, -0.695105204395499, -0.827073906478573, -1.74361140203617, -1.34629866379843, 1.514710286238, -0.468803927456235, 1.13140122231854, -1.21213800937968, -0.327474777823489, 0.160092466128878, 0.267718490671936, 0.132938879736172, 1.04199952102276, 0.863675899268996, -0.276756285615858, 0.226531129167382, 2.89084386436826, -0.31426812179635, -1.34570362230527, -1.47353389804505, -0.0555333562355194, -0.602172005041169, 0.250414742846566, 0.376569439971125, -0.136937526969143, -0.289185540367475, 1.01844599296818, -1.0898020407926, -0.672107415382938, 0.857473353196317, 0.263284246725938, 0.0523972526933849, -0.665611010955793, -0.107015680522293, -0.539745964300712, 0.546127357829245, 1.24171682685563, 1.22649619890921, 0.589460764617792, 0.0590344290809297, -0.549186422956771, 0.0631179476759736, 1.5666089822591, -0.0885032483214761, 0.295544900318868, -0.877041552433476, 0.214903481520077, 1.41507857879813, -0.896410488565367, -0.0407486986456548, 0.64083796652413, 0.0742075608322534, 1.92251120452101, 0.761209207305022, 1.39289185953756, 0.927867559939151, 0.529369628635474, -1.42080208356828, 0.234010189340094, 0.520942180831283, -0.292679580868902, -0.707042525734397, -0.804884829215233, 1.43524310204578, 0.580444407503398, -0.580024061779065, 0.76531901416266, -1.15489735647673, 2.42417841119804, 0.522354903078695, 0.749416613804051, -0.214255662246703), W = c(0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0), num.trees = 100)
7: do.call(model, c(list(X = X, Y = Y, W = D), model_params))
8: fit_model(model, X_subset, Y_subset, D_subset, model_type, learner_type, model_params, propensity)
9: `[.data.table`(cumulative_data_dt, , { X_subset <- as.matrix(X_cumul[[1]]) D_subset <- as.numeric(D_cumul[[1]]) Y_subset <- as.numeric(Y_cumul[[1]]) if (!(is.null(model_type))) { trained_model <- fit_model(model, X_subset, Y_subset, D_subset, model_type, learner_type, model_params, propensity) learned_policy <- model_predict(trained_model, X, D, model_type, learner_type, model_params) } else { trained_model <- custom_fit(X_subset, Y_subset, D_subset) learned_policy <- custom_predict(trained_model, X, D) } final_model <- if (t == nb_batch) trained_model else NULL .(learned_policy = list(learned_policy), final_model = list(final_model))}, by = t)
10: cumulative_data_dt[, { X_subset <- as.matrix(X_cumul[[1]]) D_subset <- as.numeric(D_cumul[[1]]) Y_subset <- as.numeric(Y_cumul[[1]]) if (!(is.null(model_type))) { trained_model <- fit_model(model, X_subset, Y_subset, D_subset, model_type, learner_type, model_params, propensity) learned_policy <- model_predict(trained_model, X, D, model_type, learner_type, model_params) } else { trained_model <- custom_fit(X_subset, Y_subset, D_subset) learned_policy <- custom_predict(trained_model, X, D) } final_model <- if (t == nb_batch) trained_model else NULL .(learned_policy = list(learned_policy), final_model = list(final_model))}, by = t]
11: cram_learning(X_matrix, D_slice, Y_slice, batch, model_type = model_type, learner_type = learner_type, baseline_policy = baseline_policy, parallelize_batch = parallelize_batch, model_params = model_params, custom_fit = custom_fit, custom_predict = custom_predict)
12: `[.data.table`(big_X, , { X_matrix <- as.matrix(.SD[, !c("Y", "D"), with = FALSE]) D_slice <- D Y_slice <- Y learning_result <- cram_learning(X_matrix, D_slice, Y_slice, batch, model_type = model_type, learner_type = learner_type, baseline_policy = baseline_policy, parallelize_batch = parallelize_batch, model_params = model_params, custom_fit = custom_fit, custom_predict = custom_predict) policies <- learning_result$policies batch_indices <- learning_result$batch_indices final_policy_model <- learning_result$final_policy_model nb_batch <- length(batch_indices) delta_estimate <- cram_estimator(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) policy_value_estimate <- cram_policy_value_estimator(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) X_pred <- as.matrix(new_big_X[, !c("sim_id"), with = FALSE]) pred_policies_sim_truth <- model_predict(final_policy_model, X_pred, new_D, model_type, learner_type, model_params) expected_length <- nb_simulations_truth * sample_size if (length(pred_policies_sim_truth) != expected_length) { message("Length mismatch: pred_policies_sim_truth has length ", length(pred_policies_sim_truth), " but expected ", expected_length) } D_1 <- rep(1, nrow(new_big_X)) D_0 <- rep(0, nrow(new_big_X)) Y_1 <- new_big_X[, .(Y = dgp_Y(D_1[.I], .SD)), by = sim_id][, Y] Y_0 <- new_big_X[, .(Y = dgp_Y(D_0[.I], .SD)), by = sim_id][, Y] true_policy_value <- mean(Y_1 * pred_policies_sim_truth + Y_0 * (1 - pred_policies_sim_truth)) baseline_policy_vec <- rep(unlist(baseline_policy), times = nb_simulations_truth) true_delta <- mean((Y_1 - Y_0) * (pred_policies_sim_truth - baseline_policy_vec)) final_policy <- policies[, nb_batch + 1] proportion_treated <- mean(final_policy) delta_asymptotic_variance <- cram_variance_estimator(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) delta_asymptotic_sd <- sqrt(delta_asymptotic_variance) delta_standard_error <- delta_asymptotic_sd delta_ci_lower <- delta_estimate - z_value * delta_standard_error delta_ci_upper <- delta_estimate + z_value * delta_standard_error delta_confidence_interval <- c(delta_ci_lower, delta_ci_upper) policy_value_asymptotic_variance <- cram_variance_estimator_policy_value(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) policy_value_asymptotic_sd <- sqrt(policy_value_asymptotic_variance) policy_value_standard_error <- policy_value_asymptotic_sd policy_value_ci_lower <- policy_value_estimate - z_value * policy_value_standard_error policy_value_ci_upper <- policy_value_estimate + z_value * policy_value_standard_error policy_value_confidence_interval <- c(policy_value_ci_lower, policy_value_ci_upper) .(proportion_treated = proportion_treated, delta_estimate = delta_estimate, delta_asymptotic_variance = delta_asymptotic_variance, delta_standard_error = delta_standard_error, delta_ci_lower = delta_ci_lower, delta_ci_upper = delta_ci_upper, policy_value_estimate = policy_value_estimate, policy_value_asymptotic_variance = policy_value_asymptotic_variance, policy_value_standard_error = policy_value_standard_error, policy_value_ci_lower = policy_value_ci_lower, policy_value_ci_upper = policy_value_ci_upper, true_delta = true_delta, true_policy_value = true_policy_value)}, by = sim_id)
13: big_X[, { X_matrix <- as.matrix(.SD[, !c("Y", "D"), with = FALSE]) D_slice <- D Y_slice <- Y learning_result <- cram_learning(X_matrix, D_slice, Y_slice, batch, model_type = model_type, learner_type = learner_type, baseline_policy = baseline_policy, parallelize_batch = parallelize_batch, model_params = model_params, custom_fit = custom_fit, custom_predict = custom_predict) policies <- learning_result$policies batch_indices <- learning_result$batch_indices final_policy_model <- learning_result$final_policy_model nb_batch <- length(batch_indices) delta_estimate <- cram_estimator(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) policy_value_estimate <- cram_policy_value_estimator(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) X_pred <- as.matrix(new_big_X[, !c("sim_id"), with = FALSE]) pred_policies_sim_truth <- model_predict(final_policy_model, X_pred, new_D, model_type, learner_type, model_params) expected_length <- nb_simulations_truth * sample_size if (length(pred_policies_sim_truth) != expected_length) { message("Length mismatch: pred_policies_sim_truth has length ", length(pred_policies_sim_truth), " but expected ", expected_length) } D_1 <- rep(1, nrow(new_big_X)) D_0 <- rep(0, nrow(new_big_X)) Y_1 <- new_big_X[, .(Y = dgp_Y(D_1[.I], .SD)), by = sim_id][, Y] Y_0 <- new_big_X[, .(Y = dgp_Y(D_0[.I], .SD)), by = sim_id][, Y] true_policy_value <- mean(Y_1 * pred_policies_sim_truth + Y_0 * (1 - pred_policies_sim_truth)) baseline_policy_vec <- rep(unlist(baseline_policy), times = nb_simulations_truth) true_delta <- mean((Y_1 - Y_0) * (pred_policies_sim_truth - baseline_policy_vec)) final_policy <- policies[, nb_batch + 1] proportion_treated <- mean(final_policy) delta_asymptotic_variance <- cram_variance_estimator(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) delta_asymptotic_sd <- sqrt(delta_asymptotic_variance) delta_standard_error <- delta_asymptotic_sd delta_ci_lower <- delta_estimate - z_value * delta_standard_error delta_ci_upper <- delta_estimate + z_value * delta_standard_error delta_confidence_interval <- c(delta_ci_lower, delta_ci_upper) policy_value_asymptotic_variance <- cram_variance_estimator_policy_value(X_matrix, Y_slice, D_slice, policies, batch_indices, propensity = propensity) policy_value_asymptotic_sd <- sqrt(policy_value_asymptotic_variance) policy_value_standard_error <- policy_value_asymptotic_sd policy_value_ci_lower <- policy_value_estimate - z_value * policy_value_standard_error policy_value_ci_upper <- policy_value_estimate + z_value * policy_value_standard_error policy_value_confidence_interval <- c(policy_value_ci_lower, policy_value_ci_upper) .(proportion_treated = proportion_treated, delta_estimate = delta_estimate, delta_asymptotic_variance = delta_asymptotic_variance, delta_standard_error = delta_standard_error, delta_ci_lower = delta_ci_lower, delta_ci_upper = delta_ci_upper, policy_value_estimate = policy_value_estimate, policy_value_asymptotic_variance = policy_value_asymptotic_variance, policy_value_standard_error = policy_value_standard_error, policy_value_ci_lower = policy_value_ci_lower, policy_value_ci_upper = policy_value_ci_upper, true_delta = true_delta, true_policy_value = true_policy_value)}, by = sim_id]
14: cram_simulation(X = X_data, dgp_D = dgp_D, dgp_Y = dgp_Y, batch = batch, nb_simulations = nb_simulations, nb_simulations_truth = nb_simulations_truth, sample_size = 500)
15: eval(expr, envir)
16: eval(expr, envir)
17: withVisible(eval(expr, envir))
18: withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)})
19: eval(call)
20: eval(call)
21: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers)
22: doWithOneRestart(return(expr), restart)
23: withOneRestart(expr, restarts[[1L]])
24: withRestartList(expr, restarts[-nr])
25: doWithOneRestart(return(expr), restart)
26: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
27: withRestartList(expr, restarts)
28: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
29: evaluate::evaluate(...)
30: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
31: in_dir(input_dir(), expr)
32: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
33: eng_r(options)
34: block_exec(params)
35: call_block(x)
36: process_group(group)
37: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
38: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
39: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
40: process_file(text, output)
41: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
42: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
43: vweave_rmarkdown(...)
44: engine$weave(file, quiet = quiet, encoding = enc)
45: doTryCatch(return(expr), name, parentenv, handler)
46: tryCatchOne(expr, names, parentenv, handlers[[1L]])
47: tryCatchList(expr, classes, parentenv, handlers)
48: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
49: tools:::.buildOneVignette("cram_policy_simulation.Rmd", "/data/gannet/ripley/R/packages/tests-clang/cramR.Rcheck/vign_test/cramR", TRUE, FALSE, "cram_policy_simulation", "UTF-8", "/tmp/RtmpVSyhuw/working_dir/RtmpJceFki/file120ce34898c7a1.rds")
An irrecoverable exception occurred. R is aborting now ...
--- re-building ‘quickstart.Rmd’ using rmarkdown
--- finished re-building ‘quickstart.Rmd’
SUMMARY: processing the following file failed:
‘cram_policy_simulation.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.1.1
Check: installed package size
Result: NOTE
installed size is 5.2Mb
sub-directories of 1Mb or more:
doc 2.2Mb
help 2.4Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64