NNDM is a variant of leave-one-out cross-validation which assigns each observation to a single assessment fold, and then attempts to remove data from each analysis fold until the nearest neighbor distance distribution between assessment and analysis folds matches the nearest neighbor distance distribution between training data and the locations a model will be used to predict. Proposed by Milà et al. (2022), this method aims to provide accurate estimates of how well models will perform in the locations they will actually be predicting. This method was originally implemented in the CAST package.
spatial_nndm_cv(
data,
prediction_sites,
...,
autocorrelation_range = NULL,
prediction_sample_size = 1000,
min_analysis_proportion = 0.5
)A tibble with classes spatial_nndm_cv, spatial_rset, rset,
tbl_df, tbl, and data.frame. The results include a column for the
data split objects and an identification variable id.
An object of class sf or sfc.
An sf or sfc object describing the areas to be
predicted. If prediction_sites are all points, then those points are
treated as the intended prediction points when calculating target nearest
neighbor distances. If prediction_sites is a single (multi-)polygon, then
points are sampled from within the boundaries of that polygon. Otherwise,
if prediction_sites is of length > 1 and not made up of points,
then points are sampled from within the bounding box of prediction_sites
and used as the intended prediction points.
Additional arguments passed to sf::st_sample(). Note that the
number of points to sample is controlled by prediction_sample_size; trying
to pass size via ... will cause an error.
A numeric of length 1 representing the landscape
autocorrelation range ("phi" in the terminology of Milà et al. (2022)). If
NULL, the default, the autocorrelation range is assumed to be the distance
between the opposite corners of the bounding box of prediction_sites.
A numeric of length 1: the number of points to
sample when prediction_sites is not only composed of points. Note that this
argument is passed to size in sf::st_sample(), meaning that no elements
of ... can be named size.
The minimum proportion of data that must
remain after removing points to match nearest neighbor distances. This
function will stop removing data from analysis sets once only
min_analysis_proportion of the original data remains in analysis sets, even
if the nearest neighbor distances between analysis and assessment sets are
still lower than those between training and prediction locations.
Note that, as a form of leave-one-out cross-validation, this method can be rather slow for larger data (and fitting models to these resamples will be even slower).
C. Milà, J. Mateu, E. Pebesma, and H. Meyer. 2022. "Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation." Methods in Ecology and Evolution 2022:13, pp 1304– 1316. doi: 10.1111/2041-210X.13851.
H. Meyer and E. Pebesma. 2022. "Machine learning-based global maps of ecological variables and the challenge of assessing them." Nature Communications 13, pp 2208. doi: 10.1038/s41467-022-29838-9.
data(ames, package = "modeldata")
ames_sf <- sf::st_as_sf(ames, coords = c("Longitude", "Latitude"), crs = 4326)
# Using a small subset of the data, to make the example run faster:
spatial_nndm_cv(ames_sf[1:100, ], ames_sf[2001:2100, ])
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