# load packages
library(olsrr)
#>
#> Attaching package: 'olsrr'
#> The following object is masked from 'package:datasets':
#>
#> rivers
# regression
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
# store output
k <- ols_step_best_subset(model)
# access metrics
result <- k$metrics
# sort by aic
# base R
result[order(result$aic), ]
#> mindex n predictors rsquare adjr predrsq cp aic
#> 2 2 2 hp wt 0.8267855 0.8148396 0.7810871 2.369005 156.6523
#> 3 3 3 hp wt qsec 0.8347678 0.8170643 0.7819955 3.061665 157.1426
#> 4 4 4 disp hp wt qsec 0.8351443 0.8107212 0.7710297 5.000000 159.0696
#> 1 1 1 wt 0.7528328 0.7445939 0.7086954 12.480939 166.0294
#> sbic sbc msep fpe apc hsp
#> 2 66.57546 162.5153 215.5104 7.356327 0.2090520 0.2402066
#> 3 67.72382 164.4713 213.1929 7.475597 0.2124414 0.2461102
#> 4 70.04081 167.8640 220.8882 7.949661 0.2259134 0.2644378
#> 1 74.29156 170.4266 296.9167 9.857235 0.2801228 0.3199103
# dplyr
dplyr::arrange(result, aic)
#> mindex n predictors rsquare adjr predrsq cp aic
#> 2 2 2 hp wt 0.8267855 0.8148396 0.7810871 2.369005 156.6523
#> 3 3 3 hp wt qsec 0.8347678 0.8170643 0.7819955 3.061665 157.1426
#> 4 4 4 disp hp wt qsec 0.8351443 0.8107212 0.7710297 5.000000 159.0696
#> 1 1 1 wt 0.7528328 0.7445939 0.7086954 12.480939 166.0294
#> sbic sbc msep fpe apc hsp
#> 2 66.57546 162.5153 215.5104 7.356327 0.2090520 0.2402066
#> 3 67.72382 164.4713 213.1929 7.475597 0.2124414 0.2461102
#> 4 70.04081 167.8640 220.8882 7.949661 0.2259134 0.2644378
#> 1 74.29156 170.4266 296.9167 9.857235 0.2801228 0.3199103Created on 2022-03-08 by the reprex package (v0.3.0)