plot.vsel: Plot predictive performance

In projpred: Projection Predictive Feature Selection

Plot predictive performance

Description

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().

Usage

## S3 method for class '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",
  ...
)

Arguments

x	An object of class vsel (returned by varsel() or cv_varsel()).
nterms_max	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.
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 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.
deltas	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).
alpha	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.
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.
thres_elpd	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.
resp_oscale	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).
point_size	Passed to argument size of ggplot2::geom_point() and controls the size of the points.
bar_thickness	Passed to argument linewidth of ggplot2::geom_linerange() and controls the thickness of the uncertainty bars.
ranking_nterms_max	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.
ranking_abbreviate	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".
ranking_abbreviate_args	A list of arguments (except for names.arg) to be passed to abbreviate() in case of ranking_abbreviate = TRUE.
ranking_repel	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.
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.
ranking_colored	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).
show_cv_proportions	A single logical value indicating whether the CV ranking proportions (see cv_proportions()) should be displayed (TRUE) or not (FALSE).
cumulate	Passed to argument cumulate of cv_proportions(). Affects the ranking proportions given on the x-axis (below the full-data predictor ranking).
text_angle	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.
size_position	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).

Details

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.

Value

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).

Horizontal lines

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.

Examples

# 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))

projpred documentation built on May 29, 2024, 5:31 a.m.