Optimization plot of a selection of spatial predictors

Source: R/plot_optimization.R

Plots optimization data frames produced by select_spatial_predictors_sequential() and select_spatial_predictors_recursive().

plot_optimization(
  model,
  point.color = viridis::viridis(
    100,
    option = "F",
    direction = -1
  ),
  verbose = TRUE
)

Arguments

model

A model produced by rf_spatial(), or an optimization data frame produced by select_spatial_predictors_sequential() or select_spatial_predictors_recursive().

point.color

Colors of the plotted points. Can be a single color name (e.g. "red4"), a 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)

verbose

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

Value

A ggplot.

Details

If the method used to fit a model with rf_spatial() is "hengl", the function returns nothing, as this method does not require optimization.

Examples

if(interactive()){

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

 #names of the response and predictors
 dependent.variable.name <- "richness_species_vascular"
 predictor.variable.names <- colnames(plant_richness_df)[5:21]

 #spatial model
 model <- rf_spatial(
   data = plant_richness_df,
   dependent.variable.name = dependent.variable.name,
   predictor.variable.names = predictor.variable.names,
   distance.matrix = distance_matrix,
   distance.thresholds = 0,
   method = "mem.moran.sequential",
   n.cores = 1,
   seed = 1
 )

 #plotting selection of spatial predictors
 plot_optimization(model = model)


}