best subset regression fails when model formula contains inline functions or interaction variables

[aravindhebbali]

ols_best_subset() returns an error when the formula in the model contains inline functions or interaction variables.

> library(caret)
> data("Sacramento")
> lm_fit2 <- lm(price  ~ beds + baths + log(sqft), data = Sacramento)
> ols_best_subset(lm_fit2)
Error in eval(predvars, data, env) : object 'sqft' not found
Called from: eval(predvars, data, env)

# interaction variables
> lm_fit3 <- lm(mpg ~ disp + hp + wt + am * disp, data = mtcars)
> ols_best_subset(lm_fit3)
Error in ols_mallows_cp(out$model, model) : 
  model must be a subset of full model
Called from: ols_mallows_cp(out$model, model)

Session Info

> sessionInfo()
R version 3.4.0 (2017-04-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=English_India.1252  LC_CTYPE=English_India.1252   
[3] LC_MONETARY=English_India.1252 LC_NUMERIC=C                  
[5] LC_TIME=English_India.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Rcpp_0.12.7     gridExtra_2.0.0 tidyr_0.6.0     tibble_1.2     
[5] purrr_0.2.2     dplyr_0.5.0     caret_6.0-76    ggplot2_2.2.1  
[9] lattice_0.20-35

loaded via a namespace (and not attached):
 [1] magrittr_1.5       splines_3.4.0      MASS_7.3-47       
 [4] munsell_0.4.3      colorspace_1.2-7   R6_2.2.1          
 [7] foreach_1.4.3      minqa_1.2.4        stringr_1.1.0     
[10] car_2.1-2          plyr_1.8.4         tools_3.4.0       
[13] parallel_3.4.0     nnet_7.3-12        pbkrtest_0.4-6    
[16] grid_3.4.0         gtable_0.2.0       nlme_3.1-125      
[19] mgcv_1.8-17        quantreg_5.19      DBI_0.5-1         
[22] MatrixModels_0.4-1 iterators_1.0.8    lme4_1.1-11       
[25] lazyeval_0.2.0     assertthat_0.2.0   Matrix_1.2-9      
[28] nloptr_1.0.4       reshape2_1.4.2     ModelMetrics_1.1.0
[31] codetools_0.2-15   stringi_1.1.2      compiler_3.4.0    
[34] scales_0.4.1       stats4_3.4.0       SparseM_1.7  

[aravindhebbali]

ols_best_subset() does not return an error when model formula contains inline functions or interaction variables.

> library(olsrr)
> library(caret)
> data("Sacramento")

> lm_fit2 <- lm(price  ~ beds + baths + log(sqft), data = Sacramento)
> ols_best_subset(lm_fit2)
      Best Subsets Regression      
-----------------------------------
Model Index    Predictors
-----------------------------------
     1         log(sqft)            
     2         beds log(sqft)       
     3         beds baths log(sqft) 
-----------------------------------

                                                                  Subsets Regression Summary                                                                  
--------------------------------------------------------------------------------------------------------------------------------------------------------------
                       Adj.        Pred                                                                                                                        
Model    R-Square    R-Square    R-Square     C(p)         AIC           SBIC          SBC             MSEP                FPE              HSP          APC  
--------------------------------------------------------------------------------------------------------------------------------------------------------------
  1        0.5680      0.5670      0.5655    52.6943    23833.1040    21187.9990    23847.6160    7453739033.0154    7453704671.7157    8006182.8289    0.4340 
  2        0.5910      0.5900      0.5873     2.9559    23784.5900    21139.7082    23803.9393    7075719175.8852    7075637629.2594    7600145.5265    0.4120 
  3        0.5910      0.5900      0.5856     4.0000    23785.6305    21140.7635    23809.8172    7083688577.2837    7083541628.0341    7608705.5907    0.4124 
--------------------------------------------------------------------------------------------------------------------------------------------------------------
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 

# interaction variables
> lm_fit3 <- lm(mpg ~ disp + hp + wt + am * disp, data = mtcars)
> ols_best_subset(lm_fit3)
      Best Subsets Regression       
------------------------------------
Model Index    Predictors
------------------------------------
     1         wt                    
     2         hp wt                 
     3         hp wt am              
     4         hp wt am disp:am      
     5         disp hp wt am disp:am 
------------------------------------

                                                  Subsets Regression Summary                                                   
-------------------------------------------------------------------------------------------------------------------------------
                       Adj.        Pred                                                                                         
Model    R-Square    R-Square    R-Square     C(p)        AIC        SBIC        SBC        MSEP      FPE       HSP       APC  
-------------------------------------------------------------------------------------------------------------------------------
  1        0.7530      0.7450      0.7087    15.7814    166.0294    77.0143    170.4266    9.8972    9.8572    0.3199    0.2801 
  2        0.8270      0.8150      0.7811     4.6820    156.6523    70.3199    162.5153    7.4314    7.3563    0.2402    0.2090 
  3        0.8400      0.8230      0.7879     4.3607    156.1348    72.9956    163.4635    7.3780    7.2438    0.2385    0.2059 
  4        0.8530      0.8310      0.7871     4.0081    155.3638    76.1548    164.1582    7.2864    7.0804    0.2355    0.2012 
  5        0.8530      0.8250      0.7704     6.0000    157.3538    81.8096    167.6140    7.8669    7.5490    0.2543    0.2145 
-------------------------------------------------------------------------------------------------------------------------------
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