pdp_sdm {caretSDM} | R Documentation |
Model Response to Variables
Description
Obtain the Partial Dependence Plots (PDP) to each variable.
Usage
pdp_sdm(i, spp = NULL, algo = NULL, variables_selected = NULL, mean.only = FALSE)
get_pdp_sdm(i, spp = NULL, algo = NULL, variables_selected = NULL)
Arguments
i |
A |
spp |
A |
algo |
A |
variables_selected |
A |
mean.only |
Boolean. Should only the mean curve be plotted or a curve to each run should be included? Standard is FALSE. |
Value
A plot (for pdp_sdm
) or a data.frame (for get_pdp_sdm
) with PDP values.
Author(s)
Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com
See Also
Examples
# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 100000, crs = 6933)
# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio12"))
# Include scenarios:
sa <- add_scenarios(sa)
# Create occurrences:
oc <- occurrences_sdm(occ, crs = 6933) |> join_area(sa)
# Create input_sdm:
i <- input_sdm(oc, sa)
# Pseudoabsence generation:
i <- pseudoabsences(i, method="bioclim", n_set=3)
# Custom trainControl:
ctrl_sdm <- caret::trainControl(method = "repeatedcv",
number = 2,
repeats = 1,
classProbs = TRUE,
returnResamp = "all",
summaryFunction = summary_sdm,
savePredictions = "all")
# Train models:
i <- train_sdm(i, algo = c("naive_bayes"), ctrl=ctrl_sdm)
# PDP plots:
pdp_sdm(i)
get_pdp_sdm(i)
[Package caretSDM version 1.1.0.1 Index]