Calculate Akaike weights

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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/aw.R
+\name{aw}
+\alias{aw}
+\alias{aw.mkinfit}
+\alias{aw.mmkin}
+\title{Calculate Akaike weights for model averaging}
+\usage{
+aw(object, ...)
+
+\method{aw}{mkinfit}(object, ...)
+
+\method{aw}{mmkin}(object, ...)
+}
+\arguments{
+\item{object}{An mmkin column object, containing two or more
+\code{\link{mkinfit}} models that have been fitted to the same data,
+or an mkinfit object. In the latter case, further mkinfit
+objects fitted to the same data should be specified
+as dots arguments.}
+
+\item{\dots}{Not used in the method for mmkin column objects,
+further mkinfit objects in the method for mkinfit objects.}
+}
+\description{
+Akaike weights are calculated based on the relative
+expected Kullback-Leibler information as specified
+by Burnham and Anderson (2004).
+}
+\examples{
+\dontrun{
+f_sfo <- mkinfit("SFO", FOCUS_2006_D, quiet = TRUE)
+f_dfop <- mkinfit("DFOP", FOCUS_2006_D, quiet = TRUE)
+aw_sfo_dfop <- aw(f_sfo, f_dfop)
+sum(aw_sfo_dfop)
+aw_sfo_dfop # SFO gets more weight as it has less parameters and a similar fit
+f <- mmkin(c("SFO", "FOMC", "DFOP"), list("FOCUS D" = FOCUS_2006_D), cores = 1, quiet = TRUE)
+aw(f)
+sum(aw(f))
+aw(f[c("SFO", "DFOP")])
+}
+}
+\references{
+Burnham KP and Anderson DR (2004) Multimodel
+  Inference: Understanding AIC and BIC in Model Selection
+  Sociological Methods & Research 33(2) 261-304
+}