plot_evaluation: Plots the results of a spatial cross-validation

In BlasBenito/spatialRF: Easy Spatial Modeling with Random Forest

Plots the results of a spatial cross-validation

Description

Plots the results of an spatial cross-validation performed with rf_evaluate().

Usage

plot_evaluation(
  model,
  fill.color = viridis::viridis(
    3,
    option = "F",
    alpha = 0.8,
    direction = -1
    ),
  line.color = "gray30",
  verbose = TRUE,
  notch = TRUE
)

Arguments

model	A model resulting from rf_evaluate().
fill.color	Character vector with three hexadecimal codes (e.g. "#440154FF" "#21908CFF" "#FDE725FF"), or function generating a palette (e.g. viridis::viridis(3)). Default: viridis::viridis(3, option = "F", alpha = 0.8, direction = -1)
line.color	Character string, color of the line produced by ggplot2::geom_smooth(). Default: "gray30"
verbose	Logical, if TRUE the plot is printed. Default: TRUE
notch	Logical, if TRUE, boxplot notches are plotted. Default: TRUE

Value

A ggplot.

See Also

rf_evaluate(), get_evaluation(), print_evaluation().

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
)

#evaluating the model with spatial cross-validation
rf.model <- rf_evaluate(
  model = rf.model,
  xy = plant_richness_df[, c("x", "y")],
  n.cores = 1
)

#plotting the evaluation results
plot_evaluation(rf.model)

}

BlasBenito/spatialRF documentation built on Sept. 1, 2022, 1:42 p.m.