Plot Random Forest Results

Plot Random Forest Results

Usage

# S3 method for class 'cjdiag_forest'
plot(
  x,
  type = "importance",
  top_n = NULL,
  base_size = NULL,
  colors = NULL,
  palette = NULL,
  theme = NULL,
  label_wrap = NULL,
  attribute.names = NULL,
  level.names = NULL,
  group_by_attribute = FALSE,
  ...
)

Arguments

x

A cjdiag_forest object from cj_fit()

type

Plot type: "importance" (default), "combined", "rank", "cumulative" (MDA rank-decay with labels), or "cumulative_pct" (cumulative % of total MDA, area chart like NMM cumulative)

top_n

Number of levels to display (default 25; NULL = all levels). For "cumulative", defaults to all levels if not specified.

base_size

Font size (default from global options or 12)

colors

Named character vector overriding specific palette colors

palette

Palette name: "default", "colorblind", or "grey"

theme

A complete ggplot2::theme() object (overrides all theme defaults)

label_wrap

Character width for label wrapping (default 35)

attribute.names

Named character vector renaming attributes in display

level.names

Named list for renaming levels

group_by_attribute

Group levels by attribute with visual separators (default FALSE)

...

Additional arguments passed to primary ggplot2 geom

Value

A ggplot object

See also

Other plotting: plot.cjdiag_crt(), plot.cjdiag_importance(), plot.cjdiag_nmm(), plot.cjdiag_tree()

Examples

# \donttest{
df <- data.frame(
  y = sample(0:1, 200, TRUE),
  a = factor(sample(c("x","y"), 200, TRUE)),
  b = factor(sample(c("p","q","r"), 200, TRUE))
)
rf <- cj_fit(y ~ a + b, data = df, method = "forest")
plot(rf)

plot(rf, palette = "colorblind", base_size = 14)

plot(rf, type = "combined")

# }