% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{summary.vsel}
\alias{summary.vsel}
\title{Summary statistics of a variable selection}
\usage{
\method{summary}{vsel}(
  object,
  nterms_max = NULL,
  stats = "elpd",
  type = c("mean", "se", "diff", "diff.se"),
  deltas = FALSE,
  alpha = 0.32,
  baseline = if (!inherits(object$refmodel, "datafit")) "ref" else "best",
  ...
)
}
\arguments{
\item{object}{An object of class \code{vsel} (returned by \code{\link[=varsel]{varsel()}} or
\code{\link[=cv_varsel]{cv_varsel()}}).}

\item{nterms_max}{Maximum submodel size for which the statistics are
calculated. Note that \code{nterms_max} does not count the intercept, so use
\code{nterms_max = 0} for the intercept-only model. For \code{\link[=plot.vsel]{plot.vsel()}},
\code{nterms_max} must be at least \code{1}.}

\item{stats}{One or more character strings determining which statistics to
calculate. Available statistics are:
\itemize{
\item \code{"elpd"}: (expected) sum of log predictive densities.
\item \code{"mlpd"}: mean log predictive density, that is, \code{"elpd"} divided by the
number of observations.
\item \code{"mse"}: mean squared error.
\item \code{"rmse"}: root mean squared error. For the corresponding standard error,
bootstrapping is used.
\item \code{"acc"} (or its alias, \code{"pctcorr"}): classification accuracy
(\code{\link[=binomial]{binomial()}} family only).
\item \code{"auc"}: area under the ROC curve (\code{\link[=binomial]{binomial()}} family only). For the
corresponding standard error, bootstrapping is used.
}}

\item{type}{One or more items from \code{"mean"}, \code{"se"}, \code{"lower"}, \code{"upper"},
\code{"diff"}, and \code{"diff.se"} indicating which of these to compute for each
item from \code{stats} (mean, standard error, lower and upper confidence
interval bounds, mean difference to the corresponding statistic of the
reference model, and standard error of this difference, respectively). The
confidence interval bounds belong to normal-approximation confidence
intervals with (nominal) coverage \code{1 - alpha}. Items \code{"diff"} and
\code{"diff.se"} are only supported if \code{deltas} is \code{FALSE}.}

\item{deltas}{If \code{TRUE}, the submodel statistics are estimated as differences
from the baseline model (see argument \code{baseline}) instead of estimating the
actual values of the statistics.}

\item{alpha}{A number determining the (nominal) coverage \code{1 - alpha} of the
normal-approximation confidence intervals. For example, \code{alpha = 0.32}
corresponds to a coverage of 68\%, i.e., one-standard-error intervals
(because of the normal approximation).}

\item{baseline}{For \code{\link[=summary.vsel]{summary.vsel()}}: Only relevant if \code{deltas} is \code{TRUE}.
For \code{\link[=plot.vsel]{plot.vsel()}}: Always relevant. Either \code{"ref"} or \code{"best"}, indicating
whether the baseline is the reference model or the best submodel found (in
terms of \code{stats[1]}), respectively.}

\item{...}{Arguments passed to the internal function which is used for
bootstrapping (if applicable; see argument \code{stats}). Currently, relevant
arguments are \code{B} (the number of bootstrap samples, defaulting to \code{2000})
and \code{seed} (see \code{\link[=set.seed]{set.seed()}}, defaulting to
\code{sample.int(.Machine$integer.max, 1)}).}
}
\description{
This is the \code{\link[=summary]{summary()}} method for \code{vsel} objects (returned by \code{\link[=varsel]{varsel()}} or
\code{\link[=cv_varsel]{cv_varsel()}}).
}
\examples{
if (requireNamespace("rstanarm", quietly = TRUE)) {
  # Data:
  dat_gauss <- data.frame(y = df_gaussian$y, df_gaussian$x)

  # The "stanreg" fit which will be used as the reference model (with small
  # values for `chains` and `iter`, but only for technical reasons in this
  # example; this is not recommended in general):
  fit <- rstanarm::stan_glm(
    y ~ X1 + X2 + X3 + X4 + X5, family = gaussian(), data = dat_gauss,
    QR = TRUE, chains = 2, iter = 500, refresh = 0, seed = 9876
  )

  # Variable selection (here without cross-validation and with small values
  # for `nterms_max`, `nclusters`, and `nclusters_pred`, but only for the
  # sake of speed in this example; this is not recommended in general):
  vs <- varsel(fit, nterms_max = 3, nclusters = 5, nclusters_pred = 10,
               seed = 5555)
  print(summary(vs))
}

}
