R/plot_response_surface.R
plot_response_surface.Rd
Plots response surfaces for any given pair of predictors in a rf()
, rf_repeat()
, or rf_spatial()
model.
A model fitted with rf()
, rf_repeat()
, or rf_spatial()
. Default NULL
Character string, name of a model predictor. If NULL
, the most important variable in model
is selected. Default: NULL
Character string, name of a model predictor. If NULL
, the second most important variable in model
is selected. Default: NULL
Numeric vector between 0 and 1. Argument probs
of the function quantile. Quantiles to set the other variables to. Default: 0.5
Integer between 20 and 500. Resolution of the plotted surface Default: 100
Numeric vector of length 2 with the range of point sizes used by geom_point. Using c(-1, -1)
removes the points. Default: c(0.5, 2.5)
Numeric between 0 and 1, transparency of the points. Setting it to 0
removes all points. Default: 1
.
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 = 0.9)
Character vector with a color name (e.g. "red4"). Default: gray30
Logical, if TRUE the plot is printed. Default: TRUE
A list with slots named after the selected quantiles
, each one with a ggplot.
All variables that are not a
or b
in a response curve are set to the values of their respective quantiles to plot the response surfaces. The output list can be plotted all at once with patchwork::wrap_plots(p)
or cowplot::plot_grid(plotlist = p)
, or one by one by extracting each plot from the list.
if(interactive()){
#load example data
data(plant_richness_df)
#fit 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
)
#plot interactions between most important predictors
plot_response_surfaces(x = out)
}