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) }