Calculate data to construct Partial dependence surface plot (i.e., bivariate dependence plot) for two predictor set
Usage
data_bpdp(
model,
predictors,
resolution = 50,
training_data = NULL,
training_boundaries = NULL,
projection_data = NULL,
clamping = FALSE
)Arguments
- model
A model object of class "gam", "gbm", "glm", "graf", "ksvm", "ksvm", "maxnet”, “nnet", and "randomForest" This model can be found in the first element of the list returned by any function from the fit_, tune_, or esm_ function families
- predictors
character. Vector with two predictor name(s) to plot. If NULL all predictors will be plotted. Default NULL
- resolution
numeric. Number of equally spaced points at which to predict continuous predictors. Default 50
- training_data
data.frame. Database with response (0,1) and predictor values used to fit a model. Default NULL
- training_boundaries
character. Plot training conditions boundaries based on training data (i.e., presences, presences and absences, etc). If training_boundaries = "convexh", function will delimit training environmental region based on a convex-hull. If training_boundaries = "rectangle", function will delimit training environmental region based on four straight lines. If used any methods it is necessary provide data in training_data argument. If NULL all predictors will be used. Default NULL.
- projection_data
SpatRaster. Raster layer with environmental variables used for model projection. Default NULL
- clamping
logical. Perform clamping. Only for maxent models. Default FALSE
Value
A list with two tibbles "pdpdata" and "resid".
pspdata: has data to construct partial dependence surface plot, the first two column includes values of the selected environmental variables, the third column with predicted suitability.
training_boundaries: has data to plot boundaries of training data.
Examples
if (FALSE) { # \dontrun{
library(terra)
library(dplyr)
somevar <- system.file("external/somevar.tif", package = "flexsdm")
somevar <- terra::rast(somevar) # environmental data
names(somevar) <- c("aet", "cwd", "tmx", "tmn")
data(abies)
abies2 <- abies %>%
select(x, y, pr_ab)
abies2 <- sdm_extract(abies2,
x = "x",
y = "y",
env_layer = somevar
)
abies2 <- part_random(abies2,
pr_ab = "pr_ab",
method = c(method = "kfold", folds = 5)
)
m <- fit_svm(
data = abies2,
response = "pr_ab",
predictors = c("aet", "cwd", "tmx", "tmn"),
partition = ".part",
thr = c("max_sens_spec")
)
df <- data_bpdp(
model = m$model,
predictors = c("aet", "cwd"),
resolution = 50,
projection_data = somevar,
training_boundaries = "rectangle",
training_data = abies2,
clamping = TRUE
)
df
names(df)
df$pspdata
df$training_boundaries
# see p_bpdp to construct partial dependence plot with ggplot2
} # }