plot.cv_proportions function

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 class 'cv_proportions'
plot(x, text_angle = NULL, ...)

## S3 method for class '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.

Returns

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

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)

# 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"))

Author(s)

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.