cv_similarity: Compute similarity measures to evaluate possible...

In blockCV: Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation

Compute similarity measures to evaluate possible extrapolation in testing folds

Description

This function computes multivariate environmental similarity surface (MESS) as described in Elith et al. (2010). MESS represents how similar a point in a testing fold is to a training fold (as a reference set of points), with respect to a set of predictor variables in r. The negative values are the sites where at least one variable has a value that is outside the range of environments over the reference set, so these are novel environments.

Usage

cv_similarity(
  cv,
  x,
  r,
  num_plot = seq_along(cv$folds_list),
  jitter_width = 0.1,
  points_size = 2,
  points_alpha = 0.7,
  points_colors = NULL,
  progress = TRUE
)

Arguments

cv	a blockCV cv_* object; a cv_spatial, cv_cluster, cv_buffer or cv_nndm
x	a simple features (sf) or SpatialPoints object of the spatial sample data used for creating the cv object.
r	a terra SpatRaster object of environmental predictor that are going to be used for modelling. This is used to calculate similarity between the training and testing points.
num_plot	a vector of indices of folds.
jitter_width	numeric; the width of jitter points.
points_size	numeric; the size of points.
points_alpha	numeric; the opacity of points
points_colors	character; a character vector of colours for points
progress	logical; whether to shows a progress bar for random fold selection.

Value

a ggplot object

Examples

library(blockCV)

# import presence-absence species data
points <- read.csv(system.file("extdata/", "species.csv", package = "blockCV"))
# make an sf object from data.frame
pa_data <- sf::st_as_sf(points, coords = c("x", "y"), crs = 7845)

# load raster data
path <- system.file("extdata/au/", package = "blockCV")
files <- list.files(path, full.names = TRUE)
covars <- terra::rast(files)

# hexagonal spatial blocking by specified size and random assignment
sb <- cv_spatial(x = pa_data,
                 column = "occ",
                 size = 450000,
                 k = 5,
                 iteration = 1)

# compute extrapolation
cv_similarity(cv = sb, r = covars, x = pa_data)

blockCV documentation built on Nov. 1, 2024, 9:09 a.m.