Plot ranking proportions from fold-wise predictor rankings

Plots the ranking proportions (see cv_proportions()) from the fold-wise predictor rankings in a cross-validation with fold-wise searches. This is a visualization of the transposed matrix returned by cv_proportions(). The proportions printed as text inside of the colored tiles are rounded to whole percentage points (the plotted proportions themselves are not rounded).

# S3 method for cv_proportions
plot(x, text_angle = NULL, ...)

# S3 method for ranking
plot(x, ...)

Arguments

x: For plot.cv_proportions(): an object of class cv_proportions (returned by cv_proportions(), possibly with cumulate = TRUE). For plot.ranking(): an object of class ranking (returned by ranking()) that cv_proportions() will be applied to internally before then calling plot.cv_proportions().
text_angle: Passed to argument angle of ggplot2::element_text() for the y-axis tick labels. In case of long predictor names, text_angle = 45 might be helpful (for example).
...: For plot.ranking(): arguments passed to cv_proportions.ranking() and plot.cv_proportions(). For plot.cv_proportions(): currently ignored.

Value

A ggplot2 plotting object (of class gg and ggplot).

Author

Idea and original code by Aki Vehtari. Slight modifications of the original code by Frank Weber, Yann McLatchie, and Sölvi Rögnvaldsson. Final implementation in projpred by Frank Weber.

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 = 1000, refresh = 0, seed = 9876
)

# Run cv_varsel() (with L1 search and small values for `K`, `nterms_max`, and
# `nclusters_pred`, but only for the sake of speed in this example; this is
# not recommended in general):
cvvs <- cv_varsel(fit, method = "L1", cv_method = "kfold", K = 2,
                  nterms_max = 3, nclusters_pred = 10, seed = 5555,
                  verbose = FALSE)
#> Fitting model 1 out of 2
#> Fitting model 2 out of 2

# Extract predictor rankings:
rk <- ranking(cvvs)

# Compute ranking proportions:
pr_rk <- cv_proportions(rk)

# Visualize the ranking proportions:
gg_pr_rk <- plot(pr_rk)
print(gg_pr_rk)


# Since the object returned by plot.cv_proportions() is a standard ggplot2
# plotting object, you can modify the plot easily, e.g., to remove the
# legend:
print(gg_pr_rk + ggplot2::theme(legend.position = "none"))