Metric functions now return NA in all cases where
they previously returned NaN. This improves cross-platform
consistency; in particular, MacOS often returned NA when
every other platform would return NaN. (#63)
ww_multi_scale() now handles classification and
class probability metrics better when called with raster arguments
(either to data or to truth and
estimate):
ww_multi_scale() will now convert truth and
estimate to factors before passing them to the metric set.
Thanks to @nowosad
for the report in #60 (#61).ww_multi_scale() will convert truth to a
factor and will pass estimate as an unnamed argument.
(#62)ww_multi_scale() will error. (#62)ww_multi_scale() now warns if you provide
crs as an argument to sf::st_make_grid() via
.... Grids created by this function will always take their
CRS from data.
ww_multi_scale() now throws an error if you pass
arguments via ... while also providing a list of grids
(because those arguments would be ignored).
ww_multi_scale() is now faster when
data is an sf object, particularly when grids are created
by passing arguments to sf::st_make_grid() (rather than
passing grids via grids).
ww_multi_scale() did not correctly handle grid
cellsizes with units. Units (set using the units package)
are now respected.
ww_multi_scale() using sf data always returned “1”
for truth and estimate counts. This was because counts were calculated
post-aggregation by mistake. Calculation order has been fixed, and these
counts should now be correct.
ww_build_neighbors() (and by extent, every spatial
dependence metric) will no longer calculate neighbors for non-point or
non-polygon geometries.
Functions dealing with local Moran’s I and related p-values (both data frame and vector variants) now return unnamed vectors.
Added a new method to support passing a SpatRaster
to data in ww_multi_scale(), with
truth and estimate being indices used to
subset data. This is a bit faster than passing
SpatRaster objects to truth and
estimate, as extraction is only done once per grid rather
than twice, but does not easily support passing R functions to
aggregation_function.
Metric functions now have better error messages including the name of the function the user called that errored, not the internal function that errored. Huge thanks to @EmilHvitfeldt for their PR (#40).
Data frame metric functions now guarantee that
.estimate will be an unnamed vector.
The sf method for ww_multi_scale() is
now much faster (and more memory efficient).
Fixed the warning when ww_area_of_applicability()
calculates an AOA threshold of 0. It now includes “Did you accidentally
pass the same data as testing and training?” as a bullet.
Removed combination functions – ww_global_geary,
ww_global_moran, ww_local_geary,
ww_local_moran, ww_local_getis_ord. Use
metric_set() to combine functions instead.
Renamed ww_local_getis_ord_pvalue_vec() and variants
to ww_local_getis_ord_g_pvalue_vec(); this change allows
internal functions to work properly, and makes it easier for the output
to indicate if the p-value is associated with a g or g* value.
Yardstick metrics will no longer include geometry columns in their returns.
The na_action argument to
ww_area_of_applicability() has been replaced by
na_rm, with a default value of FALSE.
na_rm is now TRUE by default for
non-spatial-autocorrelation functions. NA values will cause
spatial-autocorrelation functions to fail with an error.
Added functions (primarily ww_multi_scale()) and a
vignette for multi-scale assessment of model predictions.
Added functions to calculate metrics from Ji and Gallo (2006) and
Willmott (1981, 1982, 2012): ww_agreement_coefficient(),
ww_systematic_agreement_coefficient(),
ww_unsystematic_agreement_coefficient(),
ww_unsystematic_mpd(),, ww_systematic_mpd(),
ww_unsystematic_rmpd(), ww_systematic_rmpd(),
ww_willmott_d(), ww_willmott_dr(),
ww_willmott_d1(), ww_systematic_mse(),
ww_unsystematic_mse(), ww_systematic_rmse(),
ww_unsystematic_rmse(). Note that
ww_willmott_dr() uses the version from Willmott (2012);
other implementations (sometimes called “d1r”) seem to use an unbounded
variant that I haven’t found a reference to support.
Changed a call in ww_area_of_applicability() to use
FNN for nearest neighbors, rather than fields. This sped up prediction
by a lot.
Rewrote README and moved the old content to a new vignette on assessing the spatial dependency in model residuals.
Added a dependency on FNN.
Minimum version for dplyr has been bumped to 1.1.0
Added functions for automatically constructing nb
and listw objects
Added functions for global and local Geary’s C values.
Added functions for local Getis-Ord G and G* values.
Added functions for global and local Moran’s I values.
Added a NEWS.md file to track changes to the
package.