This is the plot() method for vsel objects (returned by varsel() or
cv_varsel()). It visualizes the predictive performance of the reference
model (possibly also that of some other "baseline" model) and that of the
submodels along the full-data predictor ranking. Basic information about the
(CV) variability in the ranking of the predictors is included as well (if
available; inferred from cv_proportions()). For a tabular representation,
see summary.vsel() and performances().
# S3 method for vsel
plot(
x,
nterms_max = NULL,
stats = "elpd",
deltas = FALSE,
alpha = 2 * pnorm(-1),
baseline = if (!inherits(x$refmodel, "datafit")) "ref" else "best",
thres_elpd = NA,
resp_oscale = TRUE,
point_size = 3,
bar_thickness = 1,
ranking_nterms_max = NULL,
ranking_abbreviate = FALSE,
ranking_abbreviate_args = list(),
ranking_repel = NULL,
ranking_repel_args = list(),
ranking_colored = FALSE,
show_cv_proportions = TRUE,
cumulate = FALSE,
text_angle = NULL,
size_position = "primary_x_bottom",
...
)A ggplot2 plotting object (of class gg and ggplot). If
ranking_abbreviate is TRUE, the output of abbreviate() is stored in
an attribute called projpred_ranking_abbreviated (to allow the
abbreviations to be easily mapped back to the original predictor names).
An object of class vsel (returned by varsel() or cv_varsel()).
Maximum submodel size (number of predictor terms) for which
the performance statistics are calculated. Using NULL is effectively the
same as length(ranking(object)$fulldata). Note that nterms_max does not
count the intercept, so use nterms_max = 0 for the intercept-only model.
For plot.vsel(), nterms_max must be at least 1.
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
cross-validation, these are all observations because they are partitioned
into multiple test sets; in case of varsel() with d_test = NULL, these
are again all observations because the test set is the same as the training
set). Available statistics are:
"elpd": expected log (pointwise) predictive density (for a new
dataset). Estimated by the sum of the observation-specific log predictive
density values (with each of these predictive density values being
a---possibly weighted---average across the parameter draws).
"mlpd": mean log predictive density, that is, "elpd" divided by the
number of observations.
"gmpd": geometric mean predictive density (GMPD), that is, exp() of
"mlpd". The GMPD is especially helpful for discrete response families
(because there, the GMPD is bounded by zero and one). For the corresponding
standard error, the delta method is used. The corresponding confidence
interval type is "exponentiated normal approximation" because the
confidence interval bounds are the exponentiated confidence interval bounds
of the "mlpd".
"mse": mean squared error (only available in the situations mentioned
in section "Details" below).
"rmse": root mean squared error (only available in the situations
mentioned in section "Details" below). For the corresponding standard error
and lower and upper confidence interval bounds, bootstrapping is used.
"acc" (or its alias, "pctcorr"): classification accuracy (only
available in the situations mentioned in section "Details" below). By
"classification accuracy", we mean the proportion of correctly classified
observations. For this, the response category ("class") with highest
probability (the probabilities are model-based) is taken as the prediction
("classification") for an observation.
"auc": area under the ROC curve (only available in the situations
mentioned in section "Details" below). For the corresponding standard error
and lower and upper confidence interval bounds, bootstrapping is used.
If TRUE, the submodel statistics are estimated relatively to
the baseline model (see argument baseline). For the GMPD, the term
"relatively" refers to the ratio vs. the baseline model (i.e., the submodel
statistic divided by the baseline model statistic). For all other stats,
"relatively" refers to the difference from the baseline model (i.e., the
submodel statistic minus the baseline model statistic).
A number determining the (nominal) coverage 1 - alpha of the
normal-approximation (or bootstrap or exponentiated normal-approximation;
see argument stats) confidence intervals. For example, in case of the
normal approximation, alpha = 2 * pnorm(-1) corresponds to a confidence
interval stretching by one standard error on either side of the point
estimate.
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.
Only relevant if any(stats %in% c("elpd", "mlpd", "gmpd")). The threshold for the ELPD difference (taking the submodel's
ELPD minus the baseline model's ELPD) above which the submodel's ELPD is
considered to be close enough to the baseline model's ELPD. An equivalent
rule is applied in case of the MLPD and the GMPD. See suggest_size() for
a formalization. Supplying NA deactivates this.
Only relevant for the latent projection. A single logical
value indicating whether to calculate the performance statistics on the
original response scale (TRUE) or on latent scale (FALSE).
Passed to argument size of ggplot2::geom_point() and
controls the size of the points.
Passed to argument linewidth of
ggplot2::geom_linerange() and controls the thickness of the uncertainty
bars.
Maximum submodel size (number of predictor terms)
for which the predictor names and the corresponding ranking proportions are
added on the x-axis. Using NULL is effectively the same as using
nterms_max. Using NA causes the predictor names and the corresponding
ranking proportions to be omitted. Note that ranking_nterms_max does not
count the intercept, so ranking_nterms_max = 1 corresponds to the
submodel consisting of the first (non-intercept) predictor term.
A single logical value indicating whether the
predictor names in the full-data predictor ranking should be abbreviated by
abbreviate() (TRUE) or not (FALSE). See also argument
ranking_abbreviate_args and section "Value".
A list of arguments (except for names.arg)
to be passed to abbreviate() in case of ranking_abbreviate = TRUE.
Either NULL, "text", or "label". By NULL, the
full-data predictor ranking and the corresponding ranking proportions are
placed below the x-axis. By "text" or "label", they are placed within
the plotting area, using ggrepel::geom_text_repel() or
ggrepel::geom_label_repel(), respectively. See also argument
ranking_repel_args.
A list of arguments (except for mapping) to be
passed to ggrepel::geom_text_repel() or ggrepel::geom_label_repel() in
case of ranking_repel = "text" or ranking_repel = "label",
respectively.
A single logical value indicating whether the points
and the uncertainty bars should be gradient-colored according to the CV
ranking proportions (TRUE, currently only works if show_cv_proportions
is TRUE as well) or not (FALSE). The CV ranking proportions may be
cumulated (see argument cumulate). Note that the point and the
uncertainty bar at submodel size 0 (i.e., at the intercept-only model) are
always colored in gray because the intercept is forced to be selected
before any predictors are selected (in other words, the reason is that for
submodel size 0, the question of variability across CV folds is not
appropriate in the first place).
A single logical value indicating whether the CV
ranking proportions (see cv_proportions()) should be displayed (TRUE)
or not (FALSE).
Passed to argument cumulate of cv_proportions(). Affects
the ranking proportions given on the x-axis (below the full-data predictor
ranking).
Passed to argument angle of ggplot2::element_text() for
the x-axis tick labels. In case of long predictor names (and/or large
nterms_max), text_angle = 45 might be helpful (for example). If
text_angle > 0 (< 0), the x-axis text is automatically right-aligned
(left-aligned). If -90 < text_angle && text_angle < 90 && text_angle != 0, the x-axis text is also top-aligned.
A single character string specifying the position of the
submodel sizes. Either "primary_x_bottom" for including them in the
x-axis tick labels, "primary_x_top" for putting them above the x-axis, or
"secondary_x" for putting them into a secondary x-axis. Currently, both
of the non-default options may not be combined with ranking_nterms_max = NA.
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(), but defaulting to NA so that set.seed()
is not called within that function at all).
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", "gmpd")), the
value supplied to thres_elpd (which is automatically adapted internally in
case of the MLPD or the GMPD or deltas = FALSE) is shown as a dot-dashed
gray horizontal line for the reference model and, if baseline = "best", as
a long-dashed green horizontal line for the baseline model.
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 augmented-data 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.
if (FALSE) { # 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
)
# Run varsel() (here without cross-validation, 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(plot(vs))
}
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