tests/testthat/_snaps/best-subsets-output.md

In olsrr: Tools for Building OLS Regression Models

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

olsrr documentation built on May 29, 2024, 12:35 p.m.