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emmeans
R
package for estimating marginal means of splm()
,
spautor()
, spglm()
, and
spgautor()
models.spmodel
website
titled “Using emmeans to Estimate Marginal Means of spmodel
Objects”.spautor()
and spgautor()
models via the
cutoff
argument, required when data
are an
sf
object with POINT
geometry and
W
is not specified.texas
data set, which contains voter turnout
data from eligible voters in Texas, USA, during the 1980 Presidential
election.lake
and lake_preds
data sets,
which contain data from the United States Environmental Protection
Agency’s National Lakes Assessment and LakeCat.type
argument in augment()
for
spglm()
and spgautor()
models to
type.predict
to match
broom::augment.glm()
.augment()
for spglm()
and
spgautor()
models now returns fitted values on the link
scale by default to match broom::augment.glm()
.type.residuals
argument for
spglm()
and spgautor()
models to match
broom::augment.glm()
.logLik()
to match lm()
and
glm()
behavior. logLik()
now returns a vector
with class logLik
and attributes nobs
and
df
.AIC()
and BIC()
from stats
and removed spmodel
-specific
AIC()
and BIC()
methods."terms"
prediction for
splm()
, spautor()
, spglm()
, and
spgautor()
models.scale
and df
arguments to
predict()
for splm()
and
spautor()
models.dispersion
argument to predict()
for
spglm()
and spgautor()
models.spglm()
or spgautor()
models when
family = "beta"
.cov_type
argument to covmatrix()
to return observed by observed, prediction by observed, observed by
prediction, and prediction by prediction covariance matrices.warning
argument to glances()
that
determines whether relevant warnings should be displayed or not.glances()
about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when a one model has
estmethod = "ml"
and another model has
estmethod = "reml"
.glances()
about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when two models with
estmethod = "reml"
have distinct formula
arguments.glances()
about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have
different sample sizes.glances()
about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have
different family supports (which can happen with spglm()
and spgautor()
models).tbl_df
and tbl
classes (i.e., are tibbles).cloud
argument to esv()
to return
a cloud semivariogram.esv()
output now has tbl_df
and
tbl
classes (i.e., are tibbles) and an esv
class.plot()
method for esv
objects.AUROC()
functions to compute the area under the
receiver operating characteristic (AUROC) curve for spglm()
and spgautor()
models when family
is
"binomial"
and the response is binary (i.e., represents a
single success or failure).BIC()
function to compute the Bayesian
Information Criterion (BIC) for splm()
,
spautor()
, spglm()
, and
spgautor()
models when estmethod
is
"reml"
(restricted maximum likelihood; the default) or
"ml"
(maximum likelihood).type
argument to loocv()
when
cv_predict = TRUE
and using spglm()
or
spgautor()
models so that predictions may be obtained on
the link or response scale.data
is an sf
object and a geographic (i.e., degrees) coordinate system is used
instead of a projected coordinate system.local
in
predict.spmodel
so that it depends only on the observed
data sample size. Now, when the observed data sample size exceeds 10,000
local
is set to TRUE
by default. This change
was made because prediction for big data depends almost exclusively on
the observed data sample size, not the number of predictions
desired.predict()
with the local
method
"distance"
on a model object fit with a random effect or
partition factor.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.
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.