plotImpPreds: Plot Important Predictor Distribution

In rfPermute: Estimate Permutation p-Values for Random Forest Importance Metrics

Plot Important Predictor Distribution

Description

For classification models, plot distribution of predictor variables on classes sorted by order of importance in model.

Usage

plotImpPreds(
  x,
  df,
  class.col,
  imp.type = NULL,
  max.vars = 16,
  scale = TRUE,
  size = 1,
  point.alpha = 0.2,
  violin.alpha = 0.5,
  plot = TRUE
)

Arguments

x	a rfPermute or randomForest model object.
df	data.frame with predictors in rf model.
class.col	response column name in df.
imp.type	character string representing importance type to use for sorting predictors.
max.vars	number of variables to plot (from most important to least).
scale	For permutation based importance measures, should they be divided their "standard errors"?
size, point.alpha, violin.alpha	controls size of points and alpha values (transparency) for points and violin plots.
plot	display the plot?

Value

the ggplot2 object is invisibly returned.

Note

If the model in x is from randomForest and was run with importance = TRUE, then 'MeanDecreaseAccuracy' is used as the default importance measure for sorting. Otherwise, 'MeanDecreaseGini' is used.

Author(s)

Eric Archer [e-mail]

Examples

library(randomForest)
data(mtcars)

df <- mtcars
df$am <- factor(df$am)

rf <- randomForest(am ~ ., df, importance = TRUE)
plotImpPreds(rf, df, "am")

rfPermute documentation built on Aug. 24, 2023, 1:08 a.m.