library(olsrr)
##
## Attaching package: 'olsrr'
## The following object is masked from 'package:datasets':
##
## rivers
ols_best_subset(lm(Fertility ~ . , data = swiss))
## Best Subsets Regression
## --------------------------------------------------------------------------
## Model Index Predictors
## --------------------------------------------------------------------------
## 1 Education
## 2 Education Catholic
## 3 Education Catholic Infant.Mortality
## 4 Agriculture Education Catholic Infant.Mortality
## 5 Agriculture Examination Education Catholic Infant.Mortality
## --------------------------------------------------------------------------
##
## Subsets Regression Summary
## ----------------------------------------------------------------------------------------------------------------------------------
## Adj. Pred
## Model R-Square R-Square R-Square C(p) AIC SBIC SBC MSEP FPE HSP APC
## ----------------------------------------------------------------------------------------------------------------------------------
## 1 0.4406 0.4282 0.3976 35.2049 348.4223 213.1276 353.9727 93.1970 93.0244 2.0279 0.6091
## 2 0.5745 0.5552 0.5137 18.4862 337.5636 202.8359 344.9642 74.1870 73.8435 1.6143 0.4835
## 3 0.6625 0.6390 0.5958 8.1782 328.6684 195.2580 337.9192 61.6391 61.1254 1.3412 0.4002
## 4 0.6993 0.6707 0.6203 5.0328 325.2408 193.0144 336.3417 57.5954 56.8488 1.2532 0.3722
## 5 0.7067 0.6710 0.6079 6.0000 326.0716 194.4046 339.0226 58.9893 57.8969 1.2836 0.3791
## ----------------------------------------------------------------------------------------------------------------------------------
## AIC: Akaike Information Criteria
## SBIC: Sawa's Bayesian Information Criteria
## SBC: Schwarz Bayesian Criteria
## MSEP: Estimated error of prediction, assuming multivariate normality
## FPE: Final Prediction Error
## HSP: Hocking's Sp
## APC: Amemiya Prediction Criteria