https://github.com/cran/MuMIn
Tip revision: 3cc30366fac3889f2d8e5a534f86eaccb19cd020 authored by Kamil Bartoń on 01 March 2011, 11:42:45 UTC
version 1.0.0
version 1.0.0
Tip revision: 3cc3036
MuMIn-package.Rd
\name{MuMIn-package}
\alias{MuMIn-package}
\alias{MuMIn}
\docType{package}
\encoding{utf-8}
\title{
Multi-model inference
}
\description{
The package \code{MuMIn} contains functions for (automated) model selection and
model averaging based on information criteria (AIC alike).
}
\details{
User level functions include:
\describe{
\item{\code{model.avg}}{does model averaging.}
\item{\code{get.models}}{evaluates models from the table returned by dredge.}
\item{\code{dredge}}{runs models with combinations of terms of the supplied
\sQuote{global.model}.}
\item{\code{AICc}}{calculates second-order Akaike information criterion for
one or several fitted model objects.}
}
}
\author{ \enc{Kamil Bartoń}{Kamil Barton} \email{kamil.barton@go2.pl}}
\references{
Burnham, K. P. and Anderson, D. R (2002) \emph{Model selection and multimodel
inference: a practical information-theoretic approach}. 2nd ed.
}
\seealso{
\code{\link[stats]{AIC}}, \code{\link[stats]{step}}
}
\examples{
fm1 <- lm(Fertility ~ . , data = swiss)
dd <- dredge(fm1)
top.models.1 <- get.models(dd, subset = delta < 4)
model.avg(top.models.1) # get averaged coefficients
top.models.2 <- get.models(dd, cumsum(weight) <= .95)
model.avg(top.models.2) # get averaged coefficients
# Mixed models:
# modified example(lme)
data(Orthodont, package="nlme")
require(nlme)
fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject,
method="ML")
dredge(fm2)
}
\keyword{package}
\keyword{models}