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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/select_parameters.R
\name{select_parameters}
\alias{select_parameters}
\alias{select_parameters.lm}
\alias{select_parameters.merMod}
\title{Automated selection of model parameters}
\usage{
select_parameters(model, ...)
\method{select_parameters}{lm}(model, direction = "both", steps = 1000, k = 2, ...)
\method{select_parameters}{merMod}(model, direction = "backward", steps = 1000, ...)
}
\arguments{
\item{model}{A statistical model (of class \code{lm}, \code{glm}, or \code{merMod}).}
\item{...}{Arguments passed to or from other methods.}
\item{direction}{
the mode of stepwise search, can be one of \code{"both"},
\code{"backward"}, or \code{"forward"}, with a default of \code{"both"}.
If the \code{scope} argument is missing the default for
\code{direction} is \code{"backward"}. Values can be abbreviated.
}
\item{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.
}
\item{k}{The multiple of the number of degrees of freedom used for the penalty.
Only \code{k = 2} gives the genuine AIC: \code{k = log(n)} is sometimes referred to as
BIC or SBC.}
}
\value{
The model refitted with optimal number of parameters.
}
\description{
This function performs an automated selection of the 'best' parameters,
updating and returning the "best" model.
}
\section{Classical lm and glm}{
For frequentist GLMs, \code{select_parameters()} performs an AIC-based stepwise
selection.
}
\section{Mixed models}{
For mixed-effects models of class \code{merMod}, stepwise selection is based on
\code{\link[cAIC4:stepcAIC]{cAIC4::stepcAIC()}}. This step function only searches the "best" model
based on the random-effects structure, i.e. \code{select_parameters()} adds or
excludes random-effects until the cAIC can't be improved further.
}
\examples{
\dontshow{if (requireNamespace("lme4")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
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)
}
\dontshow{\}) # examplesIf}
}
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