blockCV (version 3.1-6)

cv_similarity: 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
)

Value

a ggplot object

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.

Examples

# \donttest{
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)

# }