Automatically compare remedial models

Fits several remedial models for heteroscedasticity and compares their performance using AIC and residual RMSE. Currently evaluates weighted least squares and robust regression against the original model.

Usage

autoCompareRemediations(model, data = NULL)

Arguments

model

Fitted lm model or formula.

data

Optional data frame if model is a formula.

Value

A list with components metrics, models, and best indicating the recommended method.

Examples

data(mtcars)
m <- lm(mpg ~ wt + qsec, mtcars)
autoCompareRemediations(m)
#> $models
#> $models$OLS
#> 
#> Call:
#> lm(formula = mpg ~ wt + qsec, data = mtcars)
#> 
#> Coefficients:
#> (Intercept)           wt         qsec  
#>     19.7462      -5.0480       0.9292  
#> 
#> 
#> $models$WLS
#> 
#> Call:
#> lm(formula = mpg ~ wt + qsec, data = mtcars)
#> 
#> Coefficients:
#> (Intercept)           wt         qsec  
#>      14.083       -4.728        1.194  
#> 
#> 
#> $models$Robust
#> Call:
#> rlm(formula = form, data = data)
#> Converged in 5 iterations
#> 
#> Coefficients:
#> (Intercept)          wt        qsec 
#>  20.6990369  -5.1045692   0.8756602 
#> 
#> Degrees of freedom: 32 total; 29 residual
#> Scale estimate: 2.6 
#> 
#> 
#> $metrics
#>        Method      AIC     RMSE Recommended
#> OLS       OLS 156.7205 2.471485       FALSE
#> WLS       WLS 151.6246 2.525729        TRUE
#> Robust Robust 156.9512 2.480412       FALSE
#> 
#> $best
#> [1] "WLS"
#>