Package: spmodel 0.8.0
Michael Dumelle
spmodel: Spatial Statistical Modeling and Prediction
Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
Authors:
spmodel_0.8.0.tar.gz
spmodel_0.8.0.tar.gz(r-4.5-noble)spmodel_0.8.0.tar.gz(r-4.4-noble)
spmodel_0.8.0.tgz(r-4.4-emscripten)spmodel_0.8.0.tgz(r-4.3-emscripten)
spmodel.pdf |spmodel.html✨
spmodel/json (API)
NEWS
| # Install 'spmodel' in R: |
| install.packages('spmodel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/usepa/spmodel/issues
Datasets:
- caribou - A caribou forage experiment
- lake - National Lakes Assessment Data
- lake_preds - Lakes Prediction Data
- moose - Moose counts and presence in Alaska, USA
- moose_preds - Locations at which to predict moose counts and presence in Alaska, USA
- moss - Heavy metals in mosses near a mining road in Alaska, USA
- seal - Estimated harbor-seal trends from abundance data in southeast Alaska, USA
- sulfate - Sulfate atmospheric deposition in the conterminous USA
- sulfate_preds - Locations at which to predict sulfate atmospheric deposition in the conterminous USA
- texas - Texas Turnout Data
Last updated 20 days agofrom:26c241ed45. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 28 2024 |
| R-4.5-linux | OK | Aug 28 2024 |
Exports:AICcaugmentAUROCcovmatrixdispersion_initialdispersion_paramsesvglanceglancesloocvpseudoR2randcov_initialrandcov_paramsspautorspautorRFspcov_initialspcov_paramsspgautorspglmsplmsplmRFsprbetasprbinomsprgammasprinvgausssprnbinomsprnormsprpoistidyvarcomp
Dependencies:classclassIntcliDBIe1071fansigenericsglueKernSmoothlatticelifecyclemagrittrMASSMatrixpillarpkgconfigproxyRcpprlangs2sftibbleunitsutf8vctrswk
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute AICc of fitted model objects | AICc |
| Compute analysis of variance and likelihood ratio tests of fitted model objects | anova.spautor anova.spgautor anova.spglm anova.splm anova.spmodel tidy.anova.spautor tidy.anova.spgautor tidy.anova.spglm tidy.anova.splm |
| Augment data with information from fitted model objects | augment.spautor augment.spgautor augment.spglm augment.splm augment.spmodel |
| Area Under Receiver Operating Characteristic Curve | AUROC AUROC.spgautor AUROC.spglm |
| A caribou forage experiment | caribou |
| Extract fitted model coefficients | coef.spautor coef.spgautor coef.spglm coef.splm coef.spmodel coefficients.spautor coefficients.spgautor coefficients.spglm coefficients.splm |
| Confidence intervals for fitted model parameters | confint.spautor confint.spgautor confint.spglm confint.splm confint.spmodel |
| Compute Cook's distance | cooks.distance.spautor cooks.distance.spgautor cooks.distance.spglm cooks.distance.splm cooks.distance.spmodel |
| Create a covariance matrix | covmatrix covmatrix.spautor covmatrix.spgautor covmatrix.spglm covmatrix.splm |
| Fitted model deviance | deviance.spautor deviance.spgautor deviance.spglm deviance.splm deviance.spmodel |
| Create a dispersion parameter initial object | dispersion_initial |
| Create a dispersion parameter object | dispersion_params |
| Compute the empirical semivariogram | esv plot.esv |
| Extract model fitted values | fitted.spautor fitted.spgautor fitted.spglm fitted.splm fitted.spmodel fitted.values.spautor fitted.values.spgautor fitted.values.spglm fitted.values.splm |
| Model formulae | formula.spautor formula.spgautor formula.spglm formula.splm formula.spmodel |
| Glance at a fitted model object | glance.spautor glance.spgautor glance.spglm glance.splm glance.spmodel |
| Glance at many fitted model objects | glances glances.spautor glances.spautor_list glances.spgautor glances.spgautor_list glances.spglm glances.spglm_list glances.splm glances.splm_list |
| Compute leverage (hat) values | hatvalues.spautor hatvalues.spgautor hatvalues.spglm hatvalues.splm hatvalues.spmodel |
| Regression diagnostics | influence.spautor influence.spgautor influence.spglm influence.splm influence.spmodel |
| Find labels from object | labels.spautor labels.spgautor labels.spglm labels.splm labels.spmodel |
| National Lakes Assessment Data | lake |
| Lakes Prediction Data | lake_preds |
| Extract log-likelihood | logLik.spautor logLik.spgautor logLik.spglm logLik.splm logLik.spmodel |
| Perform leave-one-out cross validation | loocv loocv.spautor loocv.spgautor loocv.spglm loocv.splm |
| Extract the model frame from a fitted model object | model.frame.spautor model.frame.spgautor model.frame.spglm model.frame.splm model.frame.spmodel |
| Extract the model matrix from a fitted model object | model.matrix.spautor model.matrix.spgautor model.matrix.spglm model.matrix.splm model.matrix.spmodel |
| Moose counts and presence in Alaska, USA | moose |
| Locations at which to predict moose counts and presence in Alaska, USA | moose_preds |
| Heavy metals in mosses near a mining road in Alaska, USA | moss |
| Plot fitted model diagnostics | plot.spautor plot.spgautor plot.spglm plot.splm plot.spmodel |
| Model predictions (Kriging) | predict.spautor predict.spautorRF predict.spautorRF_list predict.spautor_list predict.spgautor predict.spgautor_list predict.spglm predict.spglm_list predict.splm predict.splmRF predict.splmRF_list predict.splm_list predict.spmodel |
| Print values | print.anova.spautor print.anova.spgautor print.anova.spglm print.anova.splm print.spautor print.spgautor print.spglm print.splm print.spmodel print.summary.spautor print.summary.spgautor print.summary.spglm print.summary.splm |
| Compute a pseudo r-squared | pseudoR2 pseudoR2.spautor pseudoR2.spgautor pseudoR2.spglm pseudoR2.splm |
| Create a random effects covariance parameter initial object | randcov_initial |
| Create a random effects covariance parameter object | randcov_params |
| Extract fitted model residuals | resid.spautor resid.spgautor resid.spglm resid.splm residuals.spautor residuals.spgautor residuals.spglm residuals.splm residuals.spmodel rstandard.spautor rstandard.spgautor rstandard.spglm rstandard.splm |
| Estimated harbor-seal trends from abundance data in southeast Alaska, USA | seal |
| Fit spatial autoregressive models | spautor |
| Fit random forest spatial residual models | spautorRF |
| Create a spatial covariance parameter initial object | spcov_initial |
| Create a spatial covariance parameter object | spcov_params |
| Fit spatial generalized autoregressive models | spgautor |
| Fit spatial generalized linear models | spglm |
| Fit spatial linear models | splm |
| Fit random forest spatial residual models | splmRF |
| Simulate a spatial beta random variable | sprbeta |
| Simulate a spatial binomial random variable | sprbinom |
| Simulate a spatial gamma random variable | sprgamma |
| Simulate a spatial inverse gaussian random variable | sprinvgauss |
| Simulate a spatial negative binomial random variable | sprnbinom |
| Simulate a spatial normal (Gaussian) random variable | sprnorm sprnorm.car sprnorm.exponential sprnorm.none |
| Simulate a spatial Poisson random variable | sprpois |
| Sulfate atmospheric deposition in the conterminous USA | sulfate |
| Locations at which to predict sulfate atmospheric deposition in the conterminous USA | sulfate_preds |
| Summarize a fitted model object | summary.spautor summary.spgautor summary.spglm summary.splm summary.spmodel |
| Texas Turnout Data | texas |
| Tidy a fitted model object | tidy.spautor tidy.spgautor tidy.spglm tidy.splm tidy.spmodel |
| Variability component comparison | varcomp varcomp.spautor varcomp.spgautor varcomp.spglm varcomp.splm |
| Calculate variance-covariance matrix for a fitted model object | vcov.spautor vcov.spgautor vcov.spglm vcov.splm vcov.spmodel |