lmSubsets
Overview
Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020).
Installation
# Install package from CRAN:
install.packages("lmSubsets")
Usage
# load package
library("lmSubsets")
# load some data
data("AirPollution", package = "lmSubsets")
head(AirPollution)
## precipitation temperature1 temperature7 age household education housing
## 1 36 27 71 8.1 3.34 11.4 81.5
## 2 35 23 72 11.1 3.14 11.0 78.8
## 3 44 29 74 10.4 3.21 9.8 81.6
## 4 47 45 79 6.5 3.41 11.1 77.5
## 5 43 35 77 7.6 3.44 9.6 84.6
## 6 53 45 80 7.7 3.45 10.2 66.8
## population noncauc whitecollar income hydrocarbon nox so2 humidity mortality
## 1 3243 8.8 42.6 11.7 21 15 59 59 921.870
## 2 4281 3.5 50.7 14.4 8 10 39 57 997.875
## 3 4260 0.8 39.4 12.4 6 6 33 54 962.354
## 4 3125 27.1 50.2 20.6 18 8 24 56 982.291
## 5 6441 24.4 43.7 14.3 43 38 206 55 1071.289
## 6 3325 38.5 43.1 25.5 30 32 72 54 1030.380
All-subsets regression
# compute 3 best submodels for every subset size
lm_all <- lmSubsets(mortality ~ ., data = AirPollution, nbest = 3)
lm_all
## Call:
## lmSubsets(formula = mortality ~ ., data = AirPollution, nbest = 3)
##
## Deviance:
## [best, size (tolerance)] = RSS
## 2 (0) 3 (0) 4 (0) 5 (0) 6 (0) 7 (0) 8 (0) 9 (0)
## 1st 133694.5 99841.07 82388.53 69154.11 64633.79 60538.76 58385.72 57379.21
## 2nd 168695.5 103859.31 83335.14 72250.33 65659.86 62288.70 58870.48 57617.43
## 3rd 169041.4 109202.60 85241.98 74666.42 66554.64 62953.77 60057.48 57748.66
## 10 (0) 11 (0) 12 (0) 13 (0) 14 (0) 15 (0) 16 (0)
## 1st 55358.05 54221.58 53921.82 53712.66 53696.00 53683.31 53680.02
## 2nd 56185.55 54718.93 54146.37 53874.74 53696.65 53690.20
## 3rd 56550.95 55260.67 54186.59 53900.78 53709.86 53695.48
##
## Subset:
## [variable, best] = size
## 1st 2nd 3rd
## +(Intercept) 2-16 2-15 2-15
## precipitation 5-16 4,6-15 2,6-15
## temperature1 4-16 3,5-15 5-15
## temperature7 7-16 6,8-15 8-15
## age 9-16 11-15 11-15
## household 8-16 7,9-15 5,10-15
## education 3-4,6-16 2,5,7-15 4-5,7,9-15
## housing 13-16 12,14-15 8,14-15
## population 11-16 8-10,12-15 6-9,12-15
## noncauc 2-16 3-15 3-15
## whitecollar 14-16 15
## income 16 13,15 11,14-15
## hydrocarbon 10-16 10-15 9-15
## nox 10-16 10-15 9-15
## so2 5-9,12-16 4-9,11,13-15 3-4,6-8,10,13-15
## humidity 15-16 14 12-13,15
# plot deviances and BICs
plot(lm_all)
# plot heatmap, hilite 10 best submodels (BIC)
image(lm_all, best = 1:3, hilite = 1:10, hilite_penalty = "BIC")
# evaluate summary statistics
summary(lm_all)
## Call:
## lmSubsets(formula = mortality ~ ., data = AirPollution, nbest = 3)
##
## Statistics:
## SIZE BEST sigma R2 R2adj pval Cp AIC BIC
## 2 1 48.01123 0.4144106 0.4043142 2.8849e-08 53.585642 638.8107 645.0938
## 2 53.93092 0.2611043 0.2483647 3.0221e-05 82.274971 652.7630 659.0460
## 3 53.98617 0.2595895 0.2468238 3.2154e-05 82.558453 652.8859 659.1689
## 3 1 41.85209 0.5626906 0.5473464 5.7874e-11 27.836910 623.2920 631.6694
## 2 42.68598 0.5450905 0.5291288 1.7818e-10 31.130547 625.6595 634.0369
## 3 43.77025 0.5216866 0.5049037 7.4440e-10 35.510292 628.6696 637.0469
## 4 1 38.35653 0.6391337 0.6198016 1.9699e-12 15.531554 613.7640 624.2358
## 2 38.57626 0.6349875 0.6154333 2.7041e-12 16.307465 614.4495 624.9212
## 3 39.01510 0.6266355 0.6066338 5.0630e-12 17.870447 615.8069 626.2786
## 5 1 35.45910 0.6971012 0.6750722 1.0975e-13 6.683675 605.2575 617.8236
## 2 36.24421 0.6835396 0.6605243 3.5924e-13 9.221560 607.8855 620.4516
## 3 36.84523 0.6729570 0.6491721 8.7460e-13 11.201957 609.8591 622.4252
## 6 1 34.59656 0.7169005 0.6906875 1.1157e-13 4.978493 603.2015 617.8619
## 2 34.87009 0.7124062 0.6857771 1.6918e-13 5.819540 604.1465 618.8069
## 3 35.10688 0.7084870 0.6814951 2.4190e-13 6.552961 604.9587 619.6191
## 7 1 33.79705 0.7348369 0.7048185 1.1482e-13 3.621912 601.2743 618.0290
## 2 34.28204 0.7271721 0.6962859 2.3951e-13 5.056290 602.9841 619.7388
## 3 34.46458 0.7242590 0.6930431 3.1495e-13 5.601433 603.6213 620.3761
## 8 1 33.50824 0.7442674 0.7098418 2.4093e-13 3.857124 601.1015 619.9506
## 2 33.64706 0.7421441 0.7074327 2.9669e-13 4.254470 601.5977 620.4468
## 3 33.98458 0.7369449 0.7015337 4.9030e-13 5.227424 602.7954 621.6445
## 9 1 33.54225 0.7486759 0.7092525 7.6686e-13 5.032120 602.0582 623.0016
## 2 33.61181 0.7476325 0.7080454 8.4893e-13 5.227381 602.3068 623.2502
## 3 33.65006 0.7470577 0.7073805 8.9767e-13 5.334944 602.4433 623.3867
## 10 1 33.27403 0.7575287 0.7138839 1.4806e-12 5.375433 601.9066 624.9444
## 2 33.52180 0.7539042 0.7096070 2.1117e-12 6.053710 602.7968 625.8346
## 3 33.63063 0.7523037 0.7077184 2.4657e-12 6.353221 603.1858 626.2236
## 11 1 33.26504 0.7625065 0.7140385 3.9595e-12 6.443899 602.6620 627.7941
## 2 33.41725 0.7603281 0.7114155 4.8987e-12 6.851561 603.2098 628.3420
## 3 33.58227 0.7579552 0.7085584 6.1626e-12 7.295611 603.8010 628.9331
## 12 1 33.51673 0.7638195 0.7096948 1.4379e-11 8.198194 604.3294 631.5559
## 2 33.58645 0.7628359 0.7084858 1.5800e-11 8.382254 604.5787 631.8052
## 3 33.59892 0.7626598 0.7082693 1.6068e-11 8.415219 604.6233 631.8498
## 13 1 33.80566 0.7647356 0.7046681 5.1381e-11 10.026756 606.0962 635.4170
## 2 33.85663 0.7640257 0.7037769 5.4909e-11 10.159607 606.2770 635.5978
## 3 33.86481 0.7639116 0.7036338 5.5497e-11 10.180949 606.3060 635.6268
## 14 1 34.16584 0.7648086 0.6983414 1.8846e-10 12.013101 608.0776 639.4928
## 2 34.16604 0.7648057 0.6983378 1.8850e-10 12.013627 608.0783 639.4935
## 3 34.17025 0.7647479 0.6982636 1.8950e-10 12.024458 608.0931 639.5082
## 15 1 34.53929 0.7648641 0.6917108 6.5757e-10 14.002698 610.0634 643.5729
## 2 34.54150 0.7648340 0.6916712 6.5932e-10 14.008341 610.0711 643.5806
## 3 34.54320 0.7648108 0.6916409 6.6067e-10 14.012672 610.0770 643.5865
## 16 1 34.92851 0.7648786 0.6847235 2.1933e-09 16.000000 612.0597 647.6636
Best-subset regression
# compute 20 best (BIC) submodels
lm_best <- lmSelect(mortality ~ ., data = AirPollution, nbest = 20)
lm_best
## Call:
## lmSelect(formula = mortality ~ ., data = AirPollution, nbest = 20)
##
## Criterion:
## [best] = BIC
## 1st 2nd 3rd 4th 5th 6th 7th 8th
## 617.8236 617.8619 618.0290 618.8069 619.6191 619.7388 619.8733 619.9506
## 9th 10th 11th 12th 13th 14th 15th 16th
## 620.3761 620.4269 620.4468 620.4516 620.5732 620.6156 620.8005 620.9322
## 17th 18th 19th 20th
## 621.0169 621.0900 621.1458 621.1550
##
## Subset:
## best
## +(Intercept) 1-20
## precipitation 1-11,14-19
## temperature1 1-20
## temperature7 3-4,8,10-11,17-18
## age 13,16,19
## household 6,8,20
## education 2-3,6,8-9,11-14,19-20
## housing 15,18
## population 5,9-11
## noncauc 1-20
## whitecollar 7,17
## income
## hydrocarbon
## nox
## so2 1-20
## humidity 14
# plot deviances and BIC
plot(lm_best)
# show heatmap
image(lm_best, hilite = 1)
# evaluate summary statistics
summary(lm_best)
## Call:
## lmSelect(formula = mortality ~ ., data = AirPollution, nbest = 20)
##
## Statistics:
## SIZE BEST sigma R2 R2adj pval Cp AIC BIC
## 5 1 35.45910 0.6971012 0.6750722 1.0975e-13 6.683675 605.2575 617.8236
## 6 2 34.59656 0.7169005 0.6906875 1.1157e-13 4.978493 603.2015 617.8619
## 7 3 33.79705 0.7348369 0.7048185 1.1482e-13 3.621912 601.2743 618.0290
## 6 4 34.87009 0.7124062 0.6857771 1.6918e-13 5.819540 604.1465 618.8069
## 6 5 35.10688 0.7084870 0.6814951 2.4190e-13 6.552961 604.9587 619.6191
## 7 6 34.28204 0.7271721 0.6962859 2.3951e-13 5.056290 602.9841 619.7388
## 6 7 35.18135 0.7072491 0.6801425 2.7055e-13 6.784624 605.2129 619.8733
## 8 8 33.50824 0.7442674 0.7098418 2.4093e-13 3.857124 601.1015 619.9506
## 7 9 34.46458 0.7242590 0.6930431 3.1495e-13 5.601433 603.6213 620.3761
## 7 10 34.47918 0.7240254 0.6927829 3.2190e-13 5.645161 603.6721 620.4269
## 8 11 33.64706 0.7421441 0.7074327 2.9669e-13 4.254470 601.5977 620.4468
## 5 12 36.24421 0.6835396 0.6605243 3.5924e-13 9.221560 607.8855 620.4516
## 6 13 35.38715 0.7038141 0.6763895 3.6815e-13 7.427446 605.9128 620.5732
## 7 14 34.53343 0.7231562 0.6918154 3.4908e-13 5.807818 603.8608 620.6156
## 6 15 35.45422 0.7026903 0.6751616 4.0686e-13 7.637752 606.1401 620.8005
## 6 16 35.49316 0.7020369 0.6744477 4.3113e-13 7.760027 606.2718 620.9322
## 7 17 34.64911 0.7212983 0.6897472 4.1474e-13 6.155493 604.2621 621.0169
## 7 18 34.67023 0.7209585 0.6893689 4.2797e-13 6.219089 604.3352 621.0900
## 7 19 34.68636 0.7206987 0.6890797 4.3835e-13 6.267703 604.3911 621.1458
## 6 20 35.55912 0.7009283 0.6732365 4.7553e-13 7.967486 606.4946 621.1550
Authors
Marc Hofmann (maintainer), Cristian Gatu, Erricos J. Kontoghiorghes, Ana Colubi, Achim Zeileis