splm(..., local)
and spglm(..., local)
.Matrix::rankMatrix(X, method = "tolNorm2")
to Matrix::rankMatrix(X, method = "qr")
when determining linear independence in X
, the design matrix of explanatory variables.X
has perfect collinearities (i.e., is not full rank). If this warning message occurs, it is possible that a subsequent error occurs while model fitting resulting from a covariance matrix that is not positive definite (i.e., a covariance matrix that is singular or computationally singular).splm()
when spcov_type
is "none"
and there are no random effects (#15).range_positive
argument to spautor()
and spgautor()
that when TRUE
(the new default), restricts the range parameter to be positive. When FALSE
(the prior default), the range parameter may be negative or positive.spautor()
and spgautor()
to include range parameter values near the lower and upper boundaries.local
in a call to predict(object, newdata, ...)
) when the model object (object
) was fit using splm(formula, ...)
or spglm(formula, ...)
and formula
contained at least one call to poly(..., raw = FALSE)
.splm(..., local)
and spglm(..., local)
to fail when a user-specified local index was passed to local
that was a factor variable and at least one factor level not was observed in the local index.splm(..., partition_factor)
and spglm(..., partition_factor)
to fail when the partition factor variable was a factor variable and at least one factor level was not observed in the data.spgautor()
that inflated the covariance matrix of the fixed effects (accessible via vcov()
).sp*(spcov_params, ...)
simulation functions that caused an error when spcov_params
had class "car"
or "sar"
and W
was provided by the user.newdata_size = 1
when newdata_size
was omitted while predicting type = "response"
for binomial families.loocv(object)
when object
was created using splm()
or spglm()
, spcov_type
was "none"
, and there were no random effects specified via random
.local
argument to splm()
or spglm()
).loocv(object)
. When object
was created using splm()
or spautor()
, loocv(object)
added the squared correlation between the observed data and leave-one-out predictions, regarded as a prediction r-squared.predict()
or augment()
) for splm()
objects when spcov_type
was "none"
and there were no random effects.loocv(object, local, ...)
if object
was created using splm(..., random)
or spglm(..., random)
(i.e., when random effects were specified via the random
argument to splm()
or spglm()
).loocv(object, local, ...)
if object
was created using splm(..., partition_factor)
or spglm(..., partition_factor)
(i.e., when a partition factor was specified via the partition_factor
argument to splm()
or spglm()
).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 POLYGON
geometries 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.