summary.vsel  R Documentation 
Summary statistics of a variable selection
Description
This is the summary()
method for vsel
objects (returned by varsel()
or
cv_varsel()
).
Usage
## S3 method for class 'vsel'
summary(
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
object 
An object of class vsel (returned by varsel() or
cv_varsel() ).

nterms_max 
Maximum submodel size for which the statistics are
calculated. Note that nterms_max does not count the intercept, so use
nterms_max = 0 for the interceptonly model. For plot.vsel() ,
nterms_max must be at least 1 .

stats 
One or more character strings determining which statistics to
calculate. Available statistics are:

"elpd" : (expected) sum of log predictive densities.

"mlpd" : mean log predictive density, that is, "elpd" divided by the
number of observations.

"mse" : mean squared error.

"rmse" : root mean squared error. For the corresponding standard error,
bootstrapping is used.

"acc" (or its alias, "pctcorr" ): classification accuracy
(binomial() family only).

"auc" : area under the ROC curve (binomial() family only). For the
corresponding standard error, bootstrapping is used.

type 
One or more items from "mean" , "se" , "lower" , "upper" ,
"diff" , and "diff.se" indicating which of these to compute for each
item from 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 normalapproximation confidence
intervals with (nominal) coverage 1  alpha . Items "diff" and
"diff.se" are only supported if deltas is FALSE .

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

alpha 
A number determining the (nominal) coverage 1  alpha of the
normalapproximation confidence intervals. For example, alpha = 0.32
corresponds to a coverage of 68%, i.e., onestandarderror intervals
(because of the normal approximation).

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

... 
Arguments passed to the internal function which is used for
bootstrapping (if applicable; see argument stats ). Currently, relevant
arguments are B (the number of bootstrap samples, defaulting to 2000 )
and seed (see set.seed() , defaulting to
sample.int(.Machine$integer.max, 1) ).

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 crossvalidation 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))
}