local = TRUE in splm() and
spglm() now uses the kmeans assignment method
with group sizes approximately equal to 100.
random assignment method was used with
group sizes approximately equal to 50.local = TRUE in predict()
and augment() now uses 100 local neighbors.
spmodel” and “Technical
Details” vignettes to the package website.spmodel”
vignette to the package website.spmodel” vignette to “An Introduction to
spmodel” and changed output type from PDF to HTML.local in
predict() was TRUE.sprbinom() when the size argument was
different from 1."sv-wls" estimation method.tidy() when conf.level was less than zero or
greater than one.spglm() function to fit spatial generalized
linear models for point-referenced data (i.e., generalized
geostatistical models).
spglm() syntax is very similar to splm()
syntax.spglm() fitted model objects use the same generics as
splm() fitted model objects.spgautor() function to fit spatial generalized
linear models for areal data (i.e., spatial generalized autoregressive
models).
spgautor() syntax is very similar to
spautor() syntax.spgautor() fitted model objects use the same generics
as spautor() fitted model objects.augment(), made the level and
local arguments explicit (rather than being passed to
predict() via ...).offset support for relevant modeling
functions.spcov_params() that yielded output with
improper names when a named vector was used as an argument.spautor() that did not properly coerce
M if given as a matrix (instead of a vector).esv() that prevented coercion of
POLYGONgeometries to POINT geometries if
data was an sf object.esv() that did not remove
NA values from the response.splm() and spautor() that
caused an error when random effects or partition factors were ordered
factors.spautor() that prevented an error from
occurring when a partition factor was not categorical or not a
factorcovmatrix(object, newdata) that returned
a matrix with improper dimensions when spcov_type was
"none".predict() that caused an error when at
least one level of a fixed effect factor was not observed within a local
neighborhood (when the local method was
"covariance" or "distance").cooks.distance() that used the Pearson
residuals instead of the standarized residuals.varcomp function to compare variance
components.NA values in
predictors.which argument to plot()
contains 8.residuals() type raw to
response to match stats::lm().splm() output to "splm"
from "spmod" or "splm_list" from
"spmod_list".spautor() output to
"spautor" from "spmod" or
"spautor_list" from "spautor_list".splmRF() output to
"splmRF" from "spmodRF" or
"splmRF_list" from "spmodRF_list".spautorRF() output to
"spautorRF" from "spmodRF" or
"spautorRF_list" from "spmodRF_list".spmodel are now all documented using an
.spmodel suffix, making it easier to find documentation of
a particular spmodel method for the generic function of
interest.newdata are not also in data.spcov_initial().predict() with interval = "confidence".spmodel v0.3.0 changed the names of spmod,
spmodRF, spmod_list, and
spmodRF_list objects.splm() and spautor() allow multiple models
to be fit when the spcov_type argument is a vector of
length greater than one or the spcov_initial argument is a
list (with length greater than one) of spcov_initial
objects.
spmod_list.
Each element of the list holds a different model fit.glances() is used on an spmod_list object
to glance at each model fit.predict() is used on an spmod_list object
to predict at the locations in newdata for each model
fit.splmRF() and spautorRF()
functions to fit random forest spatial residual models.
spmodRF (one spatial
covariance) or spmodRF_list (multiple spatial
covariances)predict() to
perform prediction.covmatrix() function to extract covariance
matrices from an spmod object fit using splm()
or spautor().spmod objects.newdata.Matrix.This is the initial release of spmodel.