spatial_nndm_cv() forced leave-one-out
CV, rather than the intended sampling of prediction points from the polygon.spatial_block_cv() now adds an expand_bbox attribute to the resulting rset
for compatibility with rsample::reshuffle_rset()
autoplot.spatial_block_cv() now plots the proper grid (using the new
expand_bbox attribute).
spatial_block_cv() gains an argument, expand_bbox, which represents the
proportion a bounding box should be expanded by (each corner of the bounding
box is expanded by bbox_corner_value * expand_bbox).
spatial_block_cv() will now throw an error if observations are in multiple
assessment folds (caused by observations, or observation centroids, falling
precisely along grid polygon boundaries).
In spatial_nndm_cv(), passing a single polygon (or multipolygon) to the
prediction_sites argument will result in prediction sites being sampled from
that polygon, rather than from its bounding box.
get_rsplit() is now re-exported from the rsample package. This provides a
more natural, pipe-able interface for accessing individual splits;
get_rsplit(rset, 1) is identical to rset$splits[[1]].
spatial_nndm_cv() is a new function for nearest neighbor distance matching
cross-validation, as described in Milà et al. 2022
(doi: 10.1111/2041-210X.13851). NNDM was first implemented in CAST
(https://cran.r-project.org/package=CAST).spatial_clustering_cv() no longer accepts non-sf objects. Use
rsample::clustering_cv() for these instead (#126).
spatial_clustering_cv() now uses edge-to-edge distances, like the rest of
the package, rather than centroids (#126).
All functions now have a repeats argument, defaulting to 1, allowing for
repeated cross-validation (#122, #125, #126).
spatial_clustering_cv() now has a distance_function argument, set by
default to as.dist(sf::st_distance(x)) (#126).
Outputs from spatial_buffer_vfold_cv() should now have the correct radius and buffer attributes (#110).
spatial_buffer_vfold_cv() now has the correct id values when using repeats (#116).
spatial_buffer_vfold_cv() now throws an error when repeats > 1 && v >= nrow(data) (#116).
The minimum sf version required is now >= 1.0-9, so that unit objects can be passed to cellsize in spatial_block_cv() (#113; #124).
autoplot() now handles repeated cross-validation properly (#123).
Mike Mahoney is taking over as package maintainer, as Julia Silge (who remains a package author) moves to focus on ModelOps work.
Functions will now return rsplits without out_id, like most rsample
functions, whenever buffer is NULL.
spatial_block_cv(), spatial_buffer_vfold_cv(), and buffering now support
using sf or sfc objects with a missing CRS. The assumption is that data in an
NA CRS is projected, with all distance values in the same unit as the
projection. Trying to use alternative units will fail. Set a CRS if these
assumptions aren't correct.
spatial_buffer_vfold_cv() and buffering no longer support tibble or
data.frame inputs (they now require sf or sfc objects). It was not easy to
use these to begin with, but should have always caused an error: use
rsample::vfold_cv() instead or transform your data into an sf object.
spatial_buffer_vfold_cv() has had some attribute changes to match rsample:
strata attribute is now the name of the column used for stratification,
or not set if there was no stratification.pool and breaks have been added as attributesradius and buffer are now set to 0 if they were passed as NULL.spatial_buffer_vfold_cv() is a new function which wraps
rsample::vfold_cv(), allowing users to add inclusion radii and exclusion
buffers to their vfold resamples. This is the supported way to perform
spatially buffered leave-one-out cross validation (set v to nrow(data)).
spatial_leave_location_out_cv() is a new function with wraps
rsample::group_vfold_cv(), allowing users to add inclusion radii and
exclusion buffers to their vfold resamples.
spatial_block_cv() is a new function for performing spatial block
cross-validation. It currently supports randomly assigning blocks to folds.
spatial_clustering_cv() gains an argument, cluster_function, which
specifies what type of clustering to perform. cluster_function = "kmeans",
the default, uses stats::kmeans() for k-means clustering, while
cluster_function = "hclust" uses stats::hclust() for hierarchical
clustering. Users can also provide their own clustering function.
spatial_clustering_cv() now supports sf objects! Coordinates are inferred
automatically when using sf objects, and anything passed to coords will
be ignored with a warning. Clusters made using sf objects will take
coordinate reference systems into account (using sf::st_distance()),
unlike those made using data frames.
All resampling functions now support spatial buffering using two arguments.
radius lets you specify an inclusion radius for your test set, where any
data within radius of the original assessment set will be added to the
assessment set. buffer specifies an exclusion buffer around the test set,
where any data within buffer of the assessment set (after radius is
applied) will be excluded from both sets.
autoplot() now has a method for spatial resamples built from sf objects.
It works both on rset objects and on rsplit objects, and has a special
method for outputs from spatial_block_cv().
boston_canopy is a new dataset with data on tree canopy change over time in
Boston, Massachusetts, USA. It uses a projected coordinate reference system
and US customary units; see ?boston_canopy for instructions on how to
install these into your PROJ installation if needed.
The "Getting Started" vignette has been revised to demonstrate the new features and clustering methods.
A new vignette has been added walking through the spatial buffering process.
R versions before 3.4 are no longer supported.
glue, sf, and units have been added to Imports.
ggplot2 has been moved to Imports. It had been in Suggests.
covr, gifski, lwgeom, and vdiffr are now in Suggests.
rlang now has a minimum version of 1.0.0 (was previously unversioned).
NEWS.md file to track changes to the package.