Automated selection of model parameters — select

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

if (FALSE) { # requireNamespace("lme4")
model <- lm(mpg ~ ., data = mtcars)
select_parameters(model)

model <- lm(mpg ~ cyl * disp * hp * wt, data = mtcars)
select_parameters(model)
# \donttest{
# lme4 -------------------------------------------
model <- lme4::lmer(
  Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
  data = iris
)
select_parameters(model)
# }
}