Automated selection of model parameters

This function performs an automated selection of the 'best' parameters, updating and returning the "best" model.

Usage

select_parameters(model, ...)

# S3 method for class 'lm'
select_parameters(model, direction = "both", steps = 1000, k = 2, ...)

# S3 method for class 'merMod'
select_parameters(model, direction = "backward", steps = 1000, ...)

Arguments

model

A statistical model (of class lm, glm, or merMod).

...

Arguments passed to or from other methods.

direction

the mode of stepwise search, can be one of "both", "backward", or "forward", with a default of "both". If the scope argument is missing the default for direction is "backward". Values can be abbreviated.

steps

the maximum number of steps to be considered. The default is 1000 (essentially as many as required). It is typically used to stop the process early.

k

The multiple of the number of degrees of freedom used for the penalty. Only k = 2 gives the genuine AIC: k = log(n) is sometimes referred to as BIC or SBC.

Value

The model refitted with optimal number of parameters.

Classical lm and glm

For frequentist GLMs, select_parameters() performs an AIC-based stepwise selection.

Mixed models

For mixed-effects models of class merMod, stepwise selection is based on cAIC4::stepcAIC(). This step function only searches the "best" model based on the random-effects structure, i.e. select_parameters() adds or excludes random-effects until the cAIC can't be improved further.

Examples

model <- lm(mpg ~ ., data = mtcars)
select_parameters(model)
#> 
#> Call:
#> lm(formula = mpg ~ wt + qsec + am, data = mtcars)
#> 
#> Coefficients:
#> (Intercept)           wt         qsec           am  
#>       9.618       -3.917        1.226        2.936  
#> 

model <- lm(mpg ~ cyl * disp * hp * wt, data = mtcars)
select_parameters(model)
#> 
#> Call:
#> lm(formula = mpg ~ cyl + disp + hp + wt + cyl:disp + cyl:hp + 
#>     disp:hp + cyl:wt + disp:wt + hp:wt + cyl:disp:hp + cyl:hp:wt, 
#>     data = mtcars)
#> 
#> Coefficients:
#> (Intercept)          cyl         disp           hp           wt     cyl:disp  
#>  49.1436077   -3.6167276   -1.2955318   -0.0004854   58.8328841    0.1704703  
#>      cyl:hp      disp:hp       cyl:wt      disp:wt        hp:wt  cyl:disp:hp  
#>  -0.0134573    0.0132124   -7.4915051   -0.0167172   -0.6524341   -0.0016542  
#>   cyl:hp:wt  
#>   0.0850798  
#> 
# \donttest{
# lme4 -------------------------------------------
model <- lme4::lmer(
  Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
  data = iris
)
select_parameters(model)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 |  
#>     Species)
#>    Data: iris
#> REML criterion at convergence: 50.9896
#> Random effects:
#>  Groups   Name        Std.Dev.
#>  Species  (Intercept) 0.8259  
#>  Residual             0.2536  
#> Number of obs: 150, groups:  Species, 3
#> Fixed Effects:
#>                           (Intercept)                           Sepal.Length  
#>                             -2.000229                               0.936730  
#>                           Petal.Width                           Petal.Length  
#>                              1.575526                               0.265556  
#>              Sepal.Length:Petal.Width              Sepal.Length:Petal.Length  
#>                             -0.282960                              -0.088409  
#>              Petal.Width:Petal.Length  Sepal.Length:Petal.Width:Petal.Length  
#>                              0.001866                               0.023319  
# }