View source: R/plot_response_surface.R
plot_response_surface | R Documentation |
Plots response surfaces for any given pair of predictors in a rf()
, rf_repeat()
, or rf_spatial()
model.
plot_response_surface( model = NULL, a = NULL, b = NULL, quantiles = 0.5, grid.resolution = 100, point.size.range = c(0.5, 2.5), point.alpha = 1, fill.color = viridis::viridis(100, option = "F", direction = -1, alpha = 0.9), point.color = "gray30", verbose = TRUE )
model |
A model fitted with |
a |
Character string, name of a model predictor. If |
b |
Character string, name of a model predictor. If |
quantiles |
Numeric vector between 0 and 1. Argument |
grid.resolution |
Integer between 20 and 500. Resolution of the plotted surface Default: |
point.size.range |
Numeric vector of length 2 with the range of point sizes used by geom_point. Using |
point.alpha |
Numeric between 0 and 1, transparency of the points. Setting it to |
fill.color |
Character vector with hexadecimal codes (e.g. "#440154FF" "#21908CFF" "#FDE725FF"), or function generating a palette (e.g. |
point.color |
Character vector with a color name (e.g. "red4"). Default: |
verbose |
Logical, if TRUE the plot is printed. Default: |
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.
A list with slots named after the selected quantiles
, each one with a ggplot.
plot_response_curves()
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) }
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