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Calculate data to construct partial dependence plots

Source: R/data_pdp.R
data_pdp.Rd

Calculate data to construct partial dependence plots for a given predictor

Usage

data_pdp(
  model,
  predictors,
  resolution = 50,
  resid = FALSE,
  training_data = 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 a predictor name.

resolution

numeric. Number of equally spaced points at which to predict continuous predictors. Default 50

resid

logical. Calculate residuals based on training data. Default FALSE

training_data

data.frame. Database with response (0,1) and predictor values used to fit a model. Default NULL

projection_data

SpatRaster. Raster layer with environmental variables used for model projection. When this argument is used, function will calculate partial dependence curves distinguishing conditions used in training and projection conditions (i.e., projection data present in projection area but not training). Default NULL

clamping

logical. Perform clamping. Only for maxent models. Default FALSE

Value

A list with two tibbles "pdpdata" and "resid".

  • pdpdata: has data to construct partial dependence plots, the first column includes values of the selected environmental variable, the second column with predicted suitability, and the third column with range type, with two values Training and Projecting, referring to suitability calculated within and outside the range of training conditions. Third column is only returned if "projection_data" argument is used

  • resid: has data to plot residuals. The first column includes values of the selected environmental variable and the second column with predicted suitability.

See also

data_bpdp, p_bpdp, p_pdp

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)
)

svm_t1 <- fit_svm(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "cwd", "tmx", "tmn"),
  partition = ".part",
  thr = c("max_sens_spec")
)

df <- data_pdp(
  model = svm_t1$model,
  predictors = c("aet"),
  resolution = 100,
  resid = TRUE,
  projection_data = somevar,
  training_data = abies2,
  clamping = FALSE
)

df
names(df)
df$pdpdata
df$resid

plot(df$pdpdata[1:2], type = "l")
points(df$resid[1:2], cex = 0.5)

# see p_pdp to construct partial dependence plot with ggplot2
} # }