Function reference
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AIC(<splm>)AIC(<spautor>)AIC(<spglm>)AIC(<spgautor>)AICc() - Compute AIC and AICc of fitted model objects
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anova(<splm>)anova(<spautor>)anova(<spglm>)anova(<spgautor>)tidy(<anova.splm>)tidy(<anova.spautor>)tidy(<anova.spglm>)tidy(<anova.spgautor>) - Compute analysis of variance and likelihood ratio tests of fitted model objects
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augment(<splm>)augment(<spautor>)augment(<spglm>)augment(<spgautor>) - Augment data with information from fitted model objects
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caribou - A caribou forage experiment
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coef(<splm>)coefficients(<splm>)coef(<spautor>)coefficients(<spautor>)coef(<spglm>)coefficients(<spglm>)coef(<spgautor>)coefficients(<spgautor>) - Extract fitted model coefficients
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confint(<splm>)confint(<spautor>)confint(<spglm>)confint(<spgautor>) - Confidence intervals for fitted model parameters
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cooks.distance(<splm>)cooks.distance(<spautor>)cooks.distance(<spglm>)cooks.distance(<spgautor>) - Compute Cook's distance
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covmatrix() - Create a covariance matrix
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deviance(<splm>)deviance(<spautor>)deviance(<spglm>)deviance(<spgautor>) - Fitted model deviance
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dispersion_initial() - Create a dispersion parameter initial object
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dispersion_params() - Create a dispersion parameter object
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esv() - Compute the empirical semivariogram
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fitted(<splm>)fitted.values(<splm>)fitted(<spautor>)fitted.values(<spautor>)fitted(<spglm>)fitted.values(<spglm>)fitted(<spgautor>)fitted.values(<spgautor>) - Extract model fitted values
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formula(<splm>)formula(<spautor>)formula(<spglm>)formula(<spgautor>) - Model formulae
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glance(<splm>)glance(<spautor>)glance(<spglm>)glance(<spgautor>) - Glance at a fitted model object
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glances() - Glance at many fitted model objects
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hatvalues(<splm>)hatvalues(<spautor>)hatvalues(<spglm>)hatvalues(<spgautor>) - Compute leverage (hat) values
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influence(<splm>)influence(<spautor>)influence(<spglm>)influence(<spgautor>) - Regression diagnostics
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labels(<splm>)labels(<spautor>)labels(<spglm>)labels(<spgautor>) - Find labels from object
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logLik(<splm>)logLik(<spautor>)logLik(<spglm>)logLik(<spgautor>) - Extract log-likelihood
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loocv() - Perform leave-one-out cross validation
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model.frame(<splm>)model.frame(<spautor>)model.frame(<spglm>)model.frame(<spgautor>) - Extract the model frame from a fitted model object
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model.matrix(<splm>)model.matrix(<spautor>)model.matrix(<spglm>)model.matrix(<spgautor>) - Extract the model matrix from a fitted model object
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moose - Moose counts and presence in Alaska, USA
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moose_preds - Locations at which to predict moose counts and presence in Alaska, USA
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moss - Heavy metals in mosses near a mining road in Alaska, USA
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plot(<splm>)plot(<spautor>)plot(<spglm>)plot(<spgautor>) - Plot fitted model diagnostics
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predict(<splm>)predict(<spautor>)predict(<splm_list>)predict(<spautor_list>)predict(<splmRF>)predict(<spautorRF>)predict(<splmRF_list>)predict(<spautorRF_list>)predict(<spglm>)predict(<spgautor>)predict(<spglm_list>)predict(<spgautor_list>) - Model predictions (Kriging)
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print(<splm>)print(<spautor>)print(<summary.splm>)print(<summary.spautor>)print(<anova.splm>)print(<anova.spautor>)print(<spglm>)print(<spgautor>)print(<summary.spglm>)print(<summary.spgautor>)print(<anova.spglm>)print(<anova.spgautor>) - Print values
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pseudoR2() - Compute a pseudo r-squared
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randcov_initial() - Create a random effects covariance parameter initial object
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randcov_params() - Create a random effects covariance parameter object
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residuals(<splm>)resid(<splm>)rstandard(<splm>)residuals(<spautor>)resid(<spautor>)rstandard(<spautor>)residuals(<spglm>)resid(<spglm>)rstandard(<spglm>)residuals(<spgautor>)resid(<spgautor>)rstandard(<spgautor>) - Extract fitted model residuals
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seal - Estimated harbor-seal trends from abundance data in southeast Alaska, USA
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spautor() - Fit spatial autoregressive models
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spautorRF() - Fit random forest spatial residual models
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spcov_initial() - Create a spatial covariance parameter initial object
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spcov_params() - Create a spatial covariance parameter object
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spgautor() - Fit spatial generalized autoregressive models
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spglm() - Fit spatial generalized linear models
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splm() - Fit spatial linear models
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splmRF() - Fit random forest spatial residual models
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sprbeta() - Simulate a spatial beta random variable
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sprbinom() - Simulate a spatial binomial random variable
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sprgamma() - Simulate a spatial gamma random variable
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sprinvgauss() - Simulate a spatial inverse gaussian random variable
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sprnbinom() - Simulate a spatial negative binomial random variable
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sprnorm() - Simulate a spatial normal (Gaussian) random variable
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sprpois() - Simulate a spatial Poisson random variable
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sulfate - Sulfate atmospheric deposition in the conterminous USA
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sulfate_preds - Locations at which to predict sulfate atmospheric deposition in the conterminous USA
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summary(<splm>)summary(<spautor>)summary(<spglm>)summary(<spgautor>) - Summarize a fitted model object
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tidy(<splm>)tidy(<spautor>)tidy(<spglm>)tidy(<spgautor>) - Tidy a fitted model object
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varcomp() - Variability component comparison
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vcov(<splm>)vcov(<spautor>)vcov(<spglm>)vcov(<spgautor>) - Calculate variance-covariance matrix for a fitted model object