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spmodel 0.6.0

CRAN release: 2024-04-16

Minor Updates

  • Improved efficiency of handling random effects in big data models fit using splm(..., local) and spglm(..., local).
  • Changed Matrix::rankMatrix(X, method = "tolNorm2") to Matrix::rankMatrix(X, method = "qr") when determining linear independence in X, the design matrix of explanatory variables.
  • Replaced an error message with a warning message when 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).
  • Improved efficiency of splm() when spcov_type is "none" and there are no random effects (#15).
  • Added a 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.
  • Updated the initial parameter grid search for spautor() and spgautor() to include range parameter values near the lower and upper boundaries.
  • Minor documentation updates

Bug Fixes

  • Fixed a bug that yielded improper predictions when performing local prediction (specifying 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).
  • Fixed a bug that caused big data models fit using 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.
  • Fixed a bug that caused models fit using 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.
  • Fixed a bug in spgautor() that inflated the covariance matrix of the fixed effects (accessible via vcov()).
  • Fixed a bug in sp*(spcov_params, ...) simulation functions that caused an error when spcov_params had class "car" or "sar" and W was provided by the user.

spmodel 0.5.1

CRAN release: 2024-01-09

Minor Updates

  • Set a default value of newdata_size = 1 when newdata_size was omitted while predicting type = "response" for binomial families.
  • Improved computational efficiency of loocv(object) when object was created using splm() or spglm(), spcov_type was "none", and there were no random effects specified via random.
  • Changed the number of k-means iterations from 10 to 30 (when fitting models using the local argument to splm() or spglm()).
  • Added bias and root-mean-squared-prediction error to 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.
  • Improved prediction efficiency (using predict() or augment()) for splm() objects when spcov_type was "none" and there were no random effects.
  • Minor error message updates.

Bug Fixes

  • Fixed a bug that caused local prediction to fail when the fitted model used a partition factor (#13).
  • Fixed a bug that caused significant increases in computational and memory demands when calling 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()).
  • Fixed a bug that caused significant increases in computational and memory demands when calling 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()).

spmodel 0.5.0

CRAN release: 2023-10-25

Minor updates

  • Predictions can now be made for prediction locations whose random effect levels are not present in the observed data
    • When this occurs, the random-effect covariance between the observed data and these prediction locations is assumed to be zero.
  • The default for local = TRUE in splm() and spglm() now uses the kmeans assignment method with group sizes approximately equal to 100.
    • Previously, the random assignment method was used with group sizes approximately equal to 50.
  • The default for local = TRUE in predict() and augment() now uses 100 local neighbors.
    • Previously, 50 local neighbors were used.
  • Moved the “A Detailed Guide to spmodel” and “Technical Details” vignettes to the package website.
  • Added a “Spatial Generalized Linear Models in spmodel” vignette to the package website.
  • Changed name of “An Overview of Basic Features in spmodel” vignette to “An Introduction to spmodel” and changed output type from PDF to HTML.
  • Other minor vignette updates.
  • Minor documentation updates.

Bug fixes

  • Fixed a bug that occurred with prediction for success/failure binomial data (e.g., Bernoulli data) when local in predict() was TRUE.
  • Fixed a bug that could affect simulating data using sprbinom() when the size argument was different from 1.
  • Fixed a bug that could cause local prediction to fail when only one level of a random effect was present in the prediction site’s local neighborhood.
  • Fixed a bug that could cause an error when local estimation was used for the "sv-wls" estimation method.
  • Fixed a bug that caused undesirable behavior from tidy() when conf.level was less than zero or greater than one.

spmodel 0.4.0

CRAN release: 2023-05-26

Major updates

  • Added an 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.
    • Poisson, negative binomial, binomial, beta, gamma, and inverse Gaussian families are accommodated.
    • spglm() fitted model objects use the same generics as splm() fitted model objects.
  • Added an 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.
    • Poisson, negative binomial, binomial, beta, gamma, and inverse Gaussian families are accommodated.
    • spgautor() fitted model objects use the same generics as spautor() fitted model objects.

Minor updates

  • In augment(), made the level and local arguments explicit (rather than being passed to predict() via ...).
  • Added offset support for relevant modeling functions.
  • Minor documentation updates.
  • Minor vignette updates.

Bug fixes

  • Fixed a bug in spcov_params() that yielded output with improper names when a named vector was used as an argument.
  • Fixed a bug in spautor() that did not properly coerce M if given as a matrix (instead of a vector).
  • Fixed a bug in esv() that prevented coercion of POLYGONgeometries to POINT geometries if data was an sf object.
  • Fixed a bug in esv() that did not remove NA values from the response.
  • Fixed a bug in splm() and spautor() that caused an error when random effects or partition factors were ordered factors.
  • Fixed a bug in spautor() that prevented an error from occurring when a partition factor was not categorical or not a factor
  • Fixed a bug in covmatrix(object, newdata) that returned a matrix with improper dimensions when spcov_type was "none".
  • Fixed a bug in 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").
  • Fixed a bug in cooks.distance() that used the Pearson residuals instead of the standarized residuals.

spmodel 0.3.0

CRAN release: 2023-03-10

Minor updates

  • Added the varcomp function to compare variance components.
  • Added an error message when there are NA values in predictors.
  • Added an error message when the design (model) matrix is not invertible (i.e., perfect collinearities are detected).
  • Added support for plotting anisotropic level curves of equal correlation when the which argument to plot() contains 8.
  • Renamed residuals() type raw to response to match stats::lm().
  • Changed class of splm() output to "splm" from "spmod" or "splm_list" from "spmod_list".
  • Changed class of spautor() output to "spautor" from "spmod" or "spautor_list" from "spautor_list".
  • Changed class of splmRF() output to "splmRF" from "spmodRF" or "splmRF_list" from "spmodRF_list".
  • Changed class of spautorRF() output to "spautorRF" from "spmodRF" or "spautorRF_list" from "spmodRF_list".
  • Methods corresponding to a generic function defined outside of 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.
  • Added an error when random effect grouping variables or partition factors are numeric.
  • Added an error when random effect or partition factor levels in newdata are not also in data.
  • Updated citation information.

Bug fixes

  • Fixed a bug that produced irregular spacing in an error message for spcov_initial().
  • Fixed a bug that prevented proper display of row names when calling predict() with interval = "confidence".
  • Fixed a bug that sometimes caused miscalculations in model-fitting and prediction when random effect or partition factor variables were improperly coerced to a different type.
  • Fixed bugs that sometimes caused miscalculations in certain model diagnostics.
  • Fixed inconsistencies in several non-exported generic functions.
  • Fixed a bug that prevented names from appearing with output from certain model diagnostics.

spmodel 0.2.0

CRAN release: 2022-11-11

  • spmodel v0.3.0 changed the names of spmod, spmodRF, spmod_list, and spmodRF_list objects.

Minor updates

  • 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.
    • The resulting object is a list with class 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.
  • Added the splmRF() and spautorRF() functions to fit random forest spatial residual models.
    • The resulting object has class spmodRF (one spatial covariance) or spmodRF_list (multiple spatial covariances)
    • These objects are built for use with predict() to perform prediction.
  • Added the covmatrix() function to extract covariance matrices from an spmod object fit using splm() or spautor().
  • Minor vignette updates.
  • Minor documentation updates.

Bug fixes

  • Fixed a bug that prevents display of spatial covariance type in summary of spmod objects.
  • Fixed a bug that prevented prediction of factor variables when all levels of all factor variables did not appear in newdata.

spmodel 0.1.1

CRAN release: 2022-10-20

Minor updates

  • Updated unit tests so that they are compatible with an upcoming version of Matrix.

spmodel 0.1.0

CRAN release: 2022-08-12

This is the initial release of spmodel.