# output from best subsets regression is as expected Code ols_step_best_subset(model) Output Best Subsets Regression ------------------------------ Model Index Predictors ------------------------------ 1 wt 2 hp wt 3 hp wt qsec 4 disp hp wt qsec ------------------------------ Subsets Regression Summary --------------------------------------------------------------------------------------------------------------------------------- Adj. Pred Model R-Square R-Square R-Square C(p) AIC SBIC SBC MSEP FPE HSP APC --------------------------------------------------------------------------------------------------------------------------------- 1 0.7528 0.7446 0.7087 12.4809 166.0294 74.2916 170.4266 296.9167 9.8572 0.3199 0.2801 2 0.8268 0.8148 0.7811 2.3690 156.6523 66.5755 162.5153 215.5104 7.3563 0.2402 0.2091 3 0.8348 0.8171 0.782 3.0617 157.1426 67.7238 164.4713 213.1929 7.4756 0.2461 0.2124 4 0.8351 0.8107 0.771 5.0000 159.0696 70.0408 167.8640 220.8882 7.9497 0.2644 0.2259 --------------------------------------------------------------------------------------------------------------------------------- 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 # output from best subsets regression is as expected when using different metric Code ols_step_best_subset(model, metric = "aic") Output Best Subsets Regression ------------------------------ Model Index Predictors ------------------------------ 1 wt 2 hp wt 3 hp wt qsec 4 disp hp wt qsec ------------------------------ Subsets Regression Summary --------------------------------------------------------------------------------------------------------------------------------- Adj. Pred Model R-Square R-Square R-Square C(p) AIC SBIC SBC MSEP FPE HSP APC --------------------------------------------------------------------------------------------------------------------------------- 1 0.7528 0.7446 0.7087 12.4809 166.0294 74.2916 170.4266 296.9167 9.8572 0.3199 0.2801 2 0.8268 0.8148 0.7811 2.3690 156.6523 66.5755 162.5153 215.5104 7.3563 0.2402 0.2091 3 0.8348 0.8171 0.782 3.0617 157.1426 67.7238 164.4713 213.1929 7.4756 0.2461 0.2124 4 0.8351 0.8107 0.771 5.0000 159.0696 70.0408 167.8640 220.8882 7.9497 0.2644 0.2259 --------------------------------------------------------------------------------------------------------------------------------- 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