Plots the variable importance of a model

Plots variable importance scores of rf(), rf_repeat(), and rf_spatial() models. Distributions of importance scores produced with rf_repeat() are plotted using ggplot2::geom_violin, which shows the median of the density estimate rather than the actual median of the data. However, the violin plots are ordered from top to bottom by the real median of the data to make small differences in median importance easier to spot. Ths function does not plot the result of rf_importance() yet, but you can find it under model$importance$cv.per.variable.plot.

Usage

plot_importance(
  model,
  fill.color = viridis::viridis(
    100,
    option = "F",
    direction = -1,
    alpha = 1,
    end = 0.9
   ),
  line.color = "white",
  verbose = TRUE
)

Arguments

model

A model fitted with rf(), rf_repeat(), or rf_spatial(), or a data frame with variable importance scores (only for internal use within the package functions).

fill.color

Character vector with hexadecimal codes (e.g. "#440154FF" "#21908CFF" "#FDE725FF"), or function generating a palette (e.g. viridis::viridis(100)). Default: viridis::viridis(100, option = "F", direction = -1, alpha = 0.8, end = 0.9)

line.color

Character string, color of the line produced by ggplot2::geom_smooth(). Default: "white"

verbose

Logical, if TRUE, the plot is printed. Default: TRUE

Value

A ggplot.

See also

print_importance(), get_importance()

Examples

if(interactive()){

#loading example data
data(plant_richness_df)
data(distance_matrix)

#fitting a random forest model
rf.model <- rf(
 data = plant_richness_df,
 dependent.variable.name = "richness_species_vascular",
 predictor.variable.names = colnames(plant_richness_df)[5:21],
 distance.matrix = distance_matrix,
 distance.thresholds = 0,
 n.cores = 1,
 verbose = FALSE
)

#plotting variable importance scores
plot_importance(model = rf.model)

}