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$refmodel, "datafit")) "ref" else "best",
thres_elpd = NA,
resp_oscale = TRUE,
point_size = getOption("projpred.plot_vsel_point_size", 3),
bar_thickness = getOption("projpred.plot_vsel_bar_thickness", 1),
ranking_nterms_max = getOption("projpred.plot_vsel_ranking_nterms_max", NULL),
ranking_abbreviate = getOption("projpred.plot_vsel_ranking_abbreviate", FALSE),
ranking_abbreviate_args = getOption("projpred.plot_vsel_ranking_abbreviate_args",
list()),
ranking_repel = getOption("projpred.plot_vsel_ranking_repel", NULL),
ranking_repel_args = getOption("projpred.plot_vsel_ranking_repel_args", list()),
ranking_colored = getOption("projpred.plot_vsel_ranking_colored", FALSE),
show_cv_proportions = getOption("projpred.plot_vsel_show_cv_proportions", FALSE),
cumulate = FALSE,
text_angle = getOption("projpred.plot_vsel_text_angle", 45),
size_position = getOption("projpred.plot_vsel_size_position", "primary_x_top"),
...
)A ggplot2 plotting object (of class gg and ggplot). If
ranking_abbreviate is TRUE, the output of abbreviate() is stored in
an attribute called projpred_ranking_abbreviated (to allow the
abbreviations to be easily mapped back to the original predictor names).
An object of class vsel (returned by varsel() or cv_varsel()).
Maximum submodel size (number of predictor terms) for which
the performance statistics are calculated. Using NULL is effectively the
same as length(ranking(object)$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) (ELPD). 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). For the
corresponding uncertainty interval, a normal approximation is used.
"mlpd": mean log predictive density (MLPD), that is, the ELPD divided
by the number of observations. For the corresponding uncertainty interval,
a normal approximation is used.
"gmpd": geometric mean predictive density (GMPD), that is, exp() of
the 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 uncertainty
interval type is "exponentiated normal approximation" because the
uncertainty interval bounds are the exponentiated uncertainty interval
bounds of the MLPD.
"mse": mean squared error (only available in the situations mentioned
in section "Details" below). For the corresponding uncertainty interval, a
log-normal approximation is used if deltas is FALSE and a normal
approximation is used if deltas is TRUE (or "mixed", in case of
plot.vsel()).
"rmse": root mean squared error (only available in the situations
mentioned in section "Details" below). For the corresponding standard
error, the delta method is used. For the corresponding uncertainty
interval, a log-normal approximation is used if deltas is FALSE and a
normal approximation is used if deltas is TRUE (or "mixed", in case
of plot.vsel()).
"R2": R-squared, i.e., coefficient of determination (only available in
the situations mentioned in section "Details" below). For the corresponding
standard error, the delta method is used. For the corresponding uncertainty
interval, a normal approximation 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. For the corresponding uncertainty
interval, a normal approximation is used.
"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 uncertainty interval bounds, bootstrapping is used. Not
supported in case of subsampled LOO-CV (see argument nloo of
cv_varsel()).
May be set to FALSE, TRUE, or "mixed". If FALSE, the
submodel performance statistics are plotted on their actual scale and the
uncertainty bars match this scale. If TRUE, the submodel statistics are
plotted relatively to the baseline model (see argument baseline) and the
uncertainty bars match this scale. 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). If set to
"mixed", the deltas = FALSE point estimates are combined with the
uncertainty bars from the deltas = TRUE plot.
A number determining the (nominal) coverage 1 - alpha of the
uncertainty intervals. For example, in case of a normal-approximation
uncertainty interval, alpha = 2 * pnorm(-1) corresponds to a uncertainty
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. In case of subsampled LOO-CV, baseline = "best" is not supported.
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, which requires size_position = "primary_x_bottom". 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. Note that the default of argument angle in
ggplot2::element_text() is NULL (which implies no rotation) whereas we
use a default of text_angle = 45 here. 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. When controlling text_angle via global option
projpred.plot_vsel_text_angle, keep in mind that a global option set to
NULL is treated like an unset global option, so
options(projpred.plot_vsel_text_angle = NULL) would result in text_angle = 45, not text_angle = 0.
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
(the current default), or "secondary_x" for putting them into a secondary
x-axis. Currently, "primary_x_top" and "secondary_x" may not be
combined with ranking_nterms_max = NA (i.e., only "primary_x_bottom"
works 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 or deltas = "mixed") 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", "rmse", and "R2" 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.
Note that the stats option "auc" is not supported in case of subsampled
LOO-CV (see argument nloo of cv_varsel()).
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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