eacf() to compute the empirical autocovariance
function.size argument to the local argument
in predict(..., block = TRUE) and
augment(..., block = TRUE) from 1000 to 4000. This enhances
the block prediction (i.e., Kriging) approximation’s accuracy but can
slightly increase computational complexity.newdata objects.xcoord and
ycoord are ignored when data is an
sf object.predict(object, newdata) and
augment(object, newdata) that could cause NA
values when the spatial covariance was "matern" and a
location in newdata was the exact same as a location in
data (the data argument used to fit
object).splm() or
spglm() converts a non-POINT geometry to a
POINT geometry using sf::st_centroid().splm() or
spglm() compute distances using a geographic coordinate
system (and not a projected coordinate system).esv(..., cloud = TRUE)) that incorrectly doubled the
semivariance.\textbf, \textsf, and
\textttt LaTeX rendering issues in vignettes (#36).spmodel website
titled “Block Prediction (i.e., Block Kriging) in spmodel”.fc_borders data set which contains borders for
the Four Corners states in the United States.covmatrix(..., cov_type = "pred.pred") was called on
object with a non-NULL newdata
element.esv(); see the
robustargument to esv() (#28)."none" and "ie" spatial covariance
types (via spcov_type or spcov_initial) to
spautor(), spgautor(), and
spautorRF() (#27).range_constrain argument to
splm() and spglm() to constrain the range
parameter to enhance numerical stability. The default for
range_constrain is FALSE, implying the range
is not constrained.seal data with additional polygons and a
factor variable, stock, with two levels (8 and
10) that indicates seal stock (i.e., seal type).spglm() and
spgautor() model objects. See this
link for details."ie" spatial covariance type to
splm() and spglm() models. For
splm() models, "ie" is an alias for
"none". For spglm() models,
"none" now fixes both the de and
ie covariance parameters at zero, while "ie"
fixes the de covariance parameter at zero but allows the
ie covariance parameter to vary. Thus, "none"
from spmodel $\le$ v0.8.0 matches "ie" from
spmodel v0.9.0 and but is different from
"none" from spmodel v0.9.0.na.action argument to
predict.spmodel() functions to clarify that missing values
in newdata return an error.anova(model1, model2)) when
estmethod is "ml" for both models.anova(object1, object2) when the name of
object1 had special characters (e.g., $).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
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 standardized 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.
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