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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{plot.vsel}
\alias{plot.vsel}
\title{Plot summary statistics related to variable selection}
\usage{
\method{plot}{vsel}(
x,
nterms_max = NULL,
stats = "elpd",
deltas = FALSE,
alpha = 0.32,
baseline = NULL,
...
)
}
\arguments{
\item{x}{The object returned by \link[=varsel]{varsel} or
\link[=cv_varsel]{cv_varsel}.}
\item{nterms_max}{Maximum submodel size for which the statistics are
calculated. For \code{plot.vsel} it must be at least 1.}
\item{stats}{One or several strings determining which statistics to
calculate. Available statistics are:
\itemize{
\item{elpd:} {(Expected) sum of log predictive densities}
\item{mlpd:} {Mean log predictive density, that is, elpd divided by the
number of datapoints.} \item{mse:} {Mean squared error (gaussian family
only)}
\item{rmse:} {Root mean squared error (gaussian family only)}
\item{acc/pctcorr:} {Classification accuracy (binomial family only)}
\item{auc:} {Area under the ROC curve (binomial family only)}
}
Default is \code{"elpd"}.}
\item{deltas}{If \code{TRUE}, the submodel statistics are estimated relative
to the baseline model (see argument \code{baseline}) instead of estimating
the actual values of the statistics. Defaults to \code{FALSE}.}
\item{alpha}{A number indicating the desired coverage of the credible
intervals. For example \code{alpha=0.32} corresponds to 68\% probability
mass within the intervals, that is, one standard error intervals.}
\item{baseline}{Either 'ref' or 'best' indicating whether the baseline is the
reference model or the best submodel found. Default is 'ref' when the
reference model exists, and 'best' otherwise.}
\item{...}{Currently ignored.}
}
\description{
Plot summary statistics related to variable selection
}
\examples{
\donttest{
### Usage with stanreg objects
if (requireNamespace('rstanarm', quietly=TRUE)) {
n <- 30
d <- 5
x <- matrix(rnorm(n*d), nrow=n)
y <- x[,1] + 0.5*rnorm(n)
data <- data.frame(x,y)
fit <- rstanarm::stan_glm(y ~ X1 + X2 + X3 + X4 + X5, gaussian(),
data=data, chains=2, iter=500)
vs <- cv_varsel(fit)
plot(vs)
}
}
}
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