data_bpdp: Calculate data to construct partial dependence surface plots

In sjevelazco/flexsdm: Tools for Data Preparation, Fitting, Prediction, Evaluation, and Post-Processing of Species Distribution Models

Calculate data to construct partial dependence surface plots

Description

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.

See Also

data_pdp, p_bpdp, p_pdp

Examples

## Not run: 
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

## End(Not run)

sjevelazco/flexsdm documentation built on Feb. 28, 2025, 9:07 a.m.