plot.vsel  R Documentation 
This is the plot()
method for vsel
objects (returned by varsel()
or
cv_varsel()
).
## S3 method for class 'vsel' plot( x, nterms_max = NULL, stats = "elpd", deltas = FALSE, alpha = 0.32, baseline = if (!inherits(x$refmodel, "datafit")) "ref" else "best", thres_elpd = NA, ... )
x 
An object of class 
nterms_max 
Maximum submodel size for which the statistics are
calculated. Using 
stats 
One or more character strings determining which performance statistics (i.e., utilities or losses) to calculate. Available statistics are:

deltas 
If 
alpha 
A number determining the (nominal) coverage 
baseline 
For 
thres_elpd 
Only relevant if 
... 
Arguments passed to the internal function which is used for
bootstrapping (if applicable; see argument 
As long as the reference model's performance is computable, it is
always shown in the plot as a dashed red horizontal line. If baseline = "best"
, the baseline model's performance is shown as a dotted black
horizontal line. If !is.na(thres_elpd)
and any(stats %in% c("elpd", "mlpd"))
, the value supplied to thres_elpd
(which is automatically
adapted internally in case of the MLPD or deltas = FALSE
) is shown as a
dotdashed gray horizontal line for the reference model and, if baseline = "best"
, as a longdashed green horizontal line for the baseline model.
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(plot(vs)) }
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