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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.