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Bivariate partial dependence plots for abundance-based distribution models

Source: R/p_abund_bpdp.R
p_abund_bpdp.Rd

Create bivariate partial dependence plots to explore the marginal effect of predictors on modeled abundance

Usage

p_abund_bpdp(
  model,
  predictors = NULL,
  resolution = 50,
  training_data = NULL,
  projection_data = NULL,
  training_boundaries = NULL,
  invert_transform = NULL,
  response_name = NULL,
  color_gradient = c("#000004", "#1B0A40", "#4A0C69", "#781B6C", "#A42C5F", "#CD4345",
    "#EC6824", "#FA990B", "#F7CF3D", "#FCFFA4"),
  color_training_boundaries = "white",
  set_max = NULL,
  set_min = NULL,
  theme = ggplot2::theme_classic(),
  sample_size = NULL,
  training_raster = NULL,
  x_coord = NULL,
  y_coord = NULL
)

Arguments

model

A model object found in the first element of the list returned by any function from the fit_abund_ or tune_abund_ function families

predictors

character. Vector of predictor name(s) to calculate partial dependence plots. If NULL all predictors will be used. Default NULL

resolution

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

training_data

data.frame or tibble. Database with response 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

training_boundaries

character. Plot training conditions boundaries based on training data. 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.

invert_transform

logical. Invert transformation of response variable. Useful for those cases that the response variable was transformed with one of the method in adm_transform. Default NULL

response_name

character. Name of the response variable. Default NULL

color_gradient

character. Vector with gradient colors. Default c("#000004", "#1B0A40", "#4A0C69", "#781B6C", "#A42C5F", "#CD4345", "#EC6824", "#FA990B", "#F7CF3D", "#FCFFA4")

color_training_boundaries

character. A vector with one color used to color points of residuals, Default "white"

set_max

numeric. Set a maximum abundance value to plot

set_min

numeric. Set a minimum abundance value to plot

theme

ggplot2 theme. Default ggplot2::theme_classic()

sample_size

vector. For CNN only. A vector containing the dimensions, in pixels, of raster samples. See cnn_make_samples beforehand. Default c(11,11)

training_raster

a terra SpatRaster object. For CNN only. A raster containing the predictor variables used in tune_abund_cnn or fit_abund_cnn.

x_coord

character. For CNN only. The name of the column containing longitude information for each observation.

y_coord

character. For CNN only. The name of the column containing latitude information for each observation.

Value

A ggplot object

See also

data_abund_pdp, data_abund_bpdp, p_abund_pdp

Examples

if (FALSE) {
require(terra)
require(dplyr)

# Load data
envar <- system.file("external/envar.tif", package = "adm") %>%
  rast()
data("sppabund")
some_sp <- sppabund %>%
  filter(species == "Species one")

# Fit some models
mglm <- fit_abund_glm(
  data = some_sp,
  response = "ind_ha",
  predictors = c("bio12", "elevation", "sand"),
  predictors_f = c("eco"),
  partition = ".part",
  distribution = "ZAIG",
  poly = 3,
  inter_order = 0,
  predict_part = TRUE
)

# Bivariate Dependence Plots:
# In different resolutions
p_abund_bpdp(
  model = mglm,
  predictors = c("bio12", "sand"),
  training_data = some_sp,
  resolution = 50
)

p_abund_bpdp(
  model = mglm,
  predictors = c("bio12", "sand"),
  training_data = some_sp,
  resolution = 25
)

# With projection and training boundaries
p_abund_bpdp(
  model = mglm,
  predictors = c("bio12", "elevation", "sand"),
  training_data = some_sp,
  projection_data = envar,
  training_boundaries = "rectangle"
)

p_abund_bpdp(
  model = mglm,
  predictors = c("bio12", "elevation", "sand"),
  training_data = some_sp,
  projection_data = envar,
  training_boundaries = "convexh"
)

# Customize colors and theme
p_abund_bpdp(
  model = mglm,
  predictors = c("bio12", "sand"),
  training_data = some_sp,
  projection_data = envar,
  training_boundaries = "convexh",
  color_gradient =
    c(
      "#122414", "#183C26", "#185437", "#106D43", "#0F874C",
      "#2D9F54", "#61B463", "#8DC982", "#B3E0A7", "#D7F9D0"
    ),
  color_training_boundaries = "purple",
  theme = ggplot2::theme_dark()
)
}