https://github.com/cran/MuMIn
Tip revision: 1834bb90bade3912317a15c9b7c19771f19cf6dc authored by Kamil Bartoń on 22 June 2024, 14:10:02 UTC
version 1.48.4
version 1.48.4
Tip revision: 1834bb9
arm.glm.Rd
\name{arm.glm}
\alias{arm.glm}
\alias{armWeights}
\encoding{utf-8}
\title{Adaptive Regression by Mixing}
\description{
Combine all-subsets GLMs using the ARM algorithm.
Calculate ARM weights for a set of models.
}
\usage{
arm.glm(object, R = 250, weight.by = c("aic", "loglik"), trace = FALSE)
armWeights(object, ..., data, weight.by = c("aic", "loglik"), R = 1000)
}
\arguments{
\item{object}{for \code{arm.glm}, a fitted \dQuote{global} \code{glm} object.
For \code{armWeights}, a fitted \code{\link{glm}} object, or a
\code{list} of such, or an \code{\link[=model.avg]{"averaging"}} object. }
\item{\dots}{more fitted model objects. }
\item{R}{number of permutations. }
\item{weight.by}{indicates whether model weights should be calculated with AIC
or log-likelihood. }
\item{trace}{if \code{TRUE}, information is printed during the running of
\code{arm.glm}. }
\item{data}{a data frame in which to look for variables for use with
\link[=predict]{prediction}. If omitted, the fitted linear predictors are used.}
%% \item{seed}{optionally, the random seed. See \code{\link{set.seed}}.}
}
\details{
For each of all-subsets of the \dQuote{global} model, parameters are estimated
using randomly sampled half of the data. Log-likelihood given the remaining half
of the data is used to calculate AIC weights. This is repeated \code{R}
times and mean of the weights is used to average all-subsets parameters
estimated using complete data.
}
\note{
Number of parameters is limited to \code{floor(nobs(object) / 2) - 1}.
All-subsets respect marginality constraints.
}
\value{
\code{arm.glm} returns an object of class \code{"averaging"} contaning only
\dQuote{full} averaged coefficients. See \code{\link{model.avg}} for object
description.
\code{armWeights} returns a numeric vector of model weights.
}
\references{
Yang, Y. 2001 Adaptive Regression by Mixing.
\emph{Journal of the American Statistical Association} \bold{96}, 574–588.
Yang, Y. 2003 Regression with multiple candidate models: selecting or mixing?
\emph{Statistica Sinica} \bold{13}, 783–810.
}
\author{Kamil Barto\enc{ń}{n}}
\seealso{
\code{\link{model.avg}}, \code{\link{par.avg}}
\code{\link{Weights}} for assigning new model weights to an \code{"averaging"}
object.
Other implementation of ARM algorithm: \code{arms} in (archived) package
\bold{MMIX}.
Other kinds of model weights: \code{\link{BGWeights}},
\code{\link{bootWeights}},
\code{\link{cos2Weights}}, \code{\link{jackknifeWeights}},
\code{\link{stackingWeights}}.
}
\examples{
fm <- glm(y ~ X1 + X2 + X3 + X4, data = Cement)
summary(am1 <- arm.glm(fm, R = 15))
mst <- dredge(fm)
am2 <- model.avg(mst, fit = TRUE)
Weights(am2) <- armWeights(am2, data = Cement, R = 15)
# differences are due to small R:
coef(am1, full = TRUE)
coef(am2, full = TRUE)
}
\keyword{models}