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\name{bayesresiduals}
\alias{bayesresiduals}
\title{Computation of posterior residual outlying probabilities for a linear regression model}
\description{
Computes the posterior probabilities that Bayesian residuals exceed a cutoff value for a
linear regression model with a noninformative prior
}
\usage{
bayesresiduals(lmfit,post,k)
}
\arguments{
\item{lmfit}{output of the regression function lm}
\item{post}{list with components beta, matrix of simulated draws of regression parameter, and
sigma, vector of simulated draws of sampling standard deviation}
\item{k}{cut-off value that defines an outlier}
}
\value{
vector of posterior outlying probabilities
}
\author{Jim Albert}
\examples{
chirps=c(20,16.0,19.8,18.4,17.1,15.5,14.7,17.1,15.4,16.2,15,17.2,16,17,14.1)
temp=c(88.6,71.6,93.3,84.3,80.6,75.2,69.7,82,69.4,83.3,78.6,82.6,80.6,83.5,76.3)
X=cbind(1,chirps)
lmfit=lm(temp~X)
m=1000
post=blinreg(temp,X,m)
k=2
bayesresiduals(lmfit,post,k)
}
\keyword{models}
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