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.