summary.vsel  R Documentation 
varsel()
or cv_varsel()
runThis is the summary()
method for vsel
objects (returned by varsel()
or
cv_varsel()
). Apart from some general information about the varsel()
or
cv_varsel()
run, it shows the fulldata predictor ranking, basic
information about the (CV) variability in the ranking of the predictors (if
available; inferred from cv_proportions()
), and estimates for
userspecified predictive performance statistics. For a graphical
representation, see plot.vsel()
.
## S3 method for class 'vsel'
summary(
object,
nterms_max = NULL,
stats = "elpd",
type = c("mean", "se", "diff", "diff.se"),
deltas = FALSE,
alpha = 2 * pnorm(1),
baseline = if (!inherits(object$refmodel, "datafit")) "ref" else "best",
resp_oscale = TRUE,
cumulate = FALSE,
...
)
object 
An object of class 
nterms_max 
Maximum submodel size (number of predictor terms) for which
the performance statistics are calculated. Using 
stats 
One or more character strings determining which performance
statistics (i.e., utilities or losses) to estimate based on the
observations in the evaluation (or "test") set (in case of
crossvalidation, these are all observations because they are partitioned
into multiple test sets; in case of

type 
One or more items from 
deltas 
If 
alpha 
A number determining the (nominal) coverage 
baseline 
For 
resp_oscale 
Only relevant for the latent projection. A single logical
value indicating whether to calculate the performance statistics on the
original response scale ( 
cumulate 
Passed to argument 
... 
Arguments passed to the internal function which is used for
bootstrapping (if applicable; see argument 
The stats
options "mse"
and "rmse"
are only available for:
the traditional projection,
the latent projection with resp_oscale = FALSE
,
the latent projection with resp_oscale = TRUE
in combination with
<refmodel>$family$cats
being NULL
.
The stats
option "acc"
(= "pctcorr"
) is only available for:
the binomial()
family in case of the traditional projection,
all families in case of the augmenteddata projection,
the binomial()
family (on the original response scale) in case of the
latent projection with resp_oscale = TRUE
in combination with
<refmodel>$family$cats
being NULL
,
all families (on the original response scale) in case of the latent
projection with resp_oscale = TRUE
in combination with
<refmodel>$family$cats
being not NULL
.
The stats
option "auc"
is only available for:
the binomial()
family in case of the traditional projection,
the binomial()
family (on the original response scale) in case of the
latent projection with resp_oscale = TRUE
in combination with
<refmodel>$family$cats
being NULL
.
An object of class vselsummary
.
print.vselsummary()
# 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
)
# Run varsel() (here without crossvalidation, with L1 search, and with small
# values for `nterms_max` and `nclusters_pred`, but only for the sake of
# speed in this example; this is not recommended in general):
vs < varsel(fit, method = "L1", nterms_max = 3, nclusters_pred = 10,
seed = 5555)
print(summary(vs), digits = 1)
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