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 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.