cv_nndm: Use the Nearest Neighbour Distance Matching (NNDM) to...

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

Use the Nearest Neighbour Distance Matching (NNDM) to separate train and test folds

Description

A fast implementation of the Nearest Neighbour Distance Matching (NNDM) algorithm (Milà et al., 2022) in C++. Similar to cv_buffer, this is a variation of leave-one-out (LOO) cross-validation. It tries to match the nearest neighbour distance distribution function between the test and training data to the nearest neighbour distance distribution function between the target prediction and training points (Milà et al., 2022).

Usage

cv_nndm(
  x,
  column = NULL,
  r,
  size,
  num_sample = 10000,
  sampling = "random",
  min_train = 0.05,
  presence_bg = FALSE,
  add_bg = FALSE,
  plot = TRUE,
  report = TRUE
)

Arguments

x	a simple features (sf) or SpatialPoints object of spatial sample data (e.g., species data or ground truth sample for image classification).
column	character; indicating the name of the column in which response variable (e.g. species data as a binary response i.e. 0s and 1s) is stored. This is required when presence_bg = TRUE, otherwise optional.
r	a terra SpatRaster object of a predictor variable. This defines the area that model is going to predict.
size	numeric value of the range of spatial autocorrelation (the phi parameter). This distance should be in metres. The range could be explored by cv_spatial_autocor.
num_sample	integer; the number of sample points from predictor (r) to be used for calculating the G function of prediction points.
sampling	either "random" or "regular" for sampling prediction points. When sampling = "regular", the actual number of samples might be less than num_sample for non-rectangular rasters (points falling on no-value areas are removed).
min_train	numeric; between 0 and 1. A constraint on the minimum proportion of train points in each fold.
presence_bg	logical; whether to treat data as species presence-background data. For all other data types (presence-absence, continuous, count or multi-class responses), this option should be FALSE.
add_bg	logical; add background points to the test set when presence_bg = TRUE. We do not recommend this according to Radosavljevic & Anderson (2014). Keep it FALSE, unless you mean to add the background pints to testing points.
plot	logical; whether to plot the G functions.
report	logical; whether to generate print summary of records in each fold; for very big datasets, set to FALSE for slightly faster calculation.

Details

When working with presence-background (presence and pseudo-absence) species distribution data (should be specified by presence_bg = TRUE argument), only presence records are used for specifying the folds (recommended). The testing fold comprises only the target presence point (optionally, all background points within the distance are also included when add_bg = TRUE; this is the distance that matches the nearest neighbour distance distribution function of training-testing presences and training-presences and prediction points; often lower than size). Any non-target presence points inside the distance are excluded. All points (presence and background) outside of distance are used for the training set. The methods cycles through all the presence data, so the number of folds is equal to the number of presence points in the dataset.

For all other types of data (including presence-absence, count, continuous, and multi-class) set presence_bg = FALE, and the function behaves similar to the methods explained by Milà and colleagues (2022).

Value

An object of class S3. A list of objects including:

• folds_list - a list containing the folds. Each fold has two vectors with the training (first) and testing (second) indices

• k - number of the folds

• size - the distance band to separated trainig and testing folds)

• column - the name of the column if provided

• presence_bg - whether this was treated as presence-background data

• records - a table with the number of points in each category of training and testing

References

C. Milà, J. Mateu, E. Pebesma, and H. Meyer, Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation, Methods in Ecology and Evolution (2022).

See Also

cv_buffer and cv_spatial_autocor

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/bio_5.tif", package = "blockCV")
covar <- terra::rast(path)

nndm <- cv_nndm(x = pa_data,
                column = "occ", # optional
                r = covar,
                size = 350000, # size in metres no matter the CRS
                num_sample = 10000,
                sampling = "regular",
                min_train = 0.1)

rvalavi/blockCV documentation built on Feb. 3, 2024, 7:26 a.m.