Stepwise backward regression fails when model formula contains inline functions or interaction variables

[aravindhebbali]

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

>library(caret)
>data("Sacramento")
>lm_fit2 <- lm(price  ~ beds + baths + log(sqft), data = Sacramento)
> ols_step_backward(lm_fit2)
We are eliminating variables based on p value...
Error in eval(predvars, data, env) : object 'sqft' not found
Called from: eval(predvars, data, env)

lm_fit1 <- lm(log(price)  ~ . - city, data = Sacramento)
> ols_step_backward(lm_fit1)
We are eliminating variables based on p value...
Error in eval(predvars, data, env) : object 'price' not found
Called from: eval(predvars, data, env)


# interaction variables
> lm_fit3 <- lm(mpg ~ disp + hp + wt + am * disp, data = mtcars)
> ols_step_backward(lm_fit3)
We are eliminating variables based on p value...
Error in ols_mallows_cp(fr$model, model) : 
  model must be a subset of full model
Called from: ols_mallows_cp(fr$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_step_backward() does not return an error when the 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_step_backward(lm_fit2)
We are eliminating variables based on p value...
No more variables satisfy the condition of prem: 0.3
Backward Elimination Method                                                  

Candidate Terms:                                                             

1 . beds                                                                     
2 . baths                                                                    
3 . log(sqft)                                                                

----------------------------------------------------------------------------
                            Elimination Summary                              
----------------------------------------------------------------------------
        Variable                  Adj.                                          
Step    Removed     R-Square    R-Square     C(p)       AIC          RMSE       
----------------------------------------------------------------------------
   1    baths          0.591       0.590    2.9559    23784.5900    83981.7543    
----------------------------------------------------------------------------

# interaction variables
> lm_fit3 <- lm(mpg ~ disp + hp + wt + am * disp, data = mtcars)
> ols_step_backward(lm_fit3)
We are eliminating variables based on p value...
No more variables satisfy the condition of prem: 0.3
Backward Elimination Method                                              

Candidate Terms:                                                         

1 . disp                                                                 
2 . hp                                                                   
3 . wt                                                                   
4 . am                                                                   
5 . disp:am                                                              

------------------------------------------------------------------------
                          Elimination Summary                            
------------------------------------------------------------------------
        Variable                  Adj.                                      
Step    Removed     R-Square    R-Square     C(p)       AIC        RMSE     
------------------------------------------------------------------------
   1    disp           0.853       0.831    4.0081    155.3638    2.4747    
------------------------------------------------------------------------