\name{par.avg}
\alias{par.avg}
\encoding{utf-8}

\title{Parameter averaging}
\description{
Average a coefficient with standard errors based on provided weights. 
    This function is intended chiefly for internal use.
}

\usage{
par.avg(x, se, weight, df = NULL, level = 1 - alpha, alpha = 0.05,
  revised.var = TRUE, adjusted = TRUE)
}

\arguments{
  \item{x}{vector of parameters. }
  \item{se}{vector of standard errors. }
  \item{weight}{vector of weights. }
  \item{df}{optional vector of degrees of freedom. }
  \item{alpha, level}{significance level for calculating confidence intervals. }
  \item{revised.var}{logical, should the revised formula for standard errors be
	used? See \sQuote{Details}. }
  \item{adjusted}{logical, should the inflated standard errors be calculated? See
  \sQuote{Details}. }

}

\value{
  \code{par.avg} returns a vector with named elements:
  \item{Coefficient}{model coefficients}
  \item{SE}{unconditional standard error}
  \item{Adjusted SE}{adjusted standard error}
  \item{Lower CI, Upper CI}{unconditional confidence intervals.}
}

\details{
Unconditional standard errors are square root of the variance estimator,
calculated either according to the original equation in Burnham and Anderson
(2002, equation 4.7),
or a newer, revised formula from Burnham and Anderson (2004, equation 4)
(if \code{revised.var = TRUE}, this is the default).
If \code{adjusted = TRUE} (the default) and degrees of freedom are given, the
adjusted standard error estimator and confidence intervals with improved 
coverage are returned (see Burnham and Anderson 2002, section 4.3.3).

}

\references{
Burnham, K. P. and Anderson, D. R. 2002 \emph{Model selection and multimodel
inference: a practical information-theoretic approach}. 2nd ed.

Burnham, K. P. and Anderson, D. R. 2004 Multimodel inference -
understanding AIC and BIC in model selection. \emph{Sociological Methods & Research}
\bold{33}, 261--304.
}

\author{Kamil Barto\enc{ń}{n}}

\seealso{
\code{\link{model.avg}} for model averaging.
}

%% \examples{}


\keyword{models}