Generates and returns the data required to plot the response curves of a model fitted with rf(), rf_repeat(), or rf_spatial().

get_response_curves(
model = NULL,
variables = NULL,
quantiles = c(0.1, 0.5, 0.9),
grid.resolution = 200,
verbose = TRUE
)

## Arguments

model A model fitted with rf(), rf_repeat(), or rf_spatial(). Character vector, names of predictors to plot. If NULL, the most important variables (importance higher than the median) in model are selected. Default: NULL. Numeric vector with values between 0 and 1, argument probs of quantile. Quantiles to set the other variables to. Default: c(0.1, 0.5, 0.9) Integer between 20 and 500. Resolution of the plotted curve Default: 100 Logical, if TRUE the plot is printed. Default: TRUE

## Value

A data frame with the following columns:

• response: Predicted values of the response, obtained with stats::predict().

• predictor: Values of the given predictor.

• quantile: Grouping column, values of the quantiles at which the other predictors are set to generate the response curve.

• model: Model number, only relevant if the model was produced with rf_repeat().

• predictor.name: Grouping variable, name of the predictor.

• response.name: Grouping variable, name of the response variable.

## Details

All variables that are not plotted in a particular response curve are set to the values of their respective quantiles, and the response curve for each one of these quantiles is shown in the plot.

plot_response_curves()

## Examples

if(interactive()){

data(plant_richness_df)

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

#getting data frame with response curves
p <- get_response_curves(out)
}