Function reference • spmodel

All functions

AIC(<splm>) AIC(<spautor>) AIC(<spglm>) AIC(<spgautor>) AICc(): Compute AIC and AICc of fitted model objects

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

augment(<splm>) augment(<spautor>) augment(<spglm>) augment(<spgautor>): Augment data with information from fitted model objects

caribou: A caribou forage experiment

coef(<splm>) coefficients(<splm>) coef(<spautor>) coefficients(<spautor>) coef(<spglm>) coefficients(<spglm>) coef(<spgautor>) coefficients(<spgautor>): Extract fitted model coefficients

confint(<splm>) confint(<spautor>) confint(<spglm>) confint(<spgautor>): Confidence intervals for fitted model parameters

cooks.distance(<splm>) cooks.distance(<spautor>) cooks.distance(<spglm>) cooks.distance(<spgautor>): Compute Cook's distance

covmatrix(): Create a covariance matrix

deviance(<splm>) deviance(<spautor>) deviance(<spglm>) deviance(<spgautor>): Fitted model deviance

dispersion_initial(): Create a dispersion parameter initial object

dispersion_params(): Create a dispersion parameter object

esv(): Compute the empirical semivariogram

fitted(<splm>) fitted.values(<splm>) fitted(<spautor>) fitted.values(<spautor>) fitted(<spglm>) fitted.values(<spglm>) fitted(<spgautor>) fitted.values(<spgautor>): Extract model fitted values

formula(<splm>) formula(<spautor>) formula(<spglm>) formula(<spgautor>): Model formulae

glance(<splm>) glance(<spautor>) glance(<spglm>) glance(<spgautor>): Glance at a fitted model object

glances(): Glance at many fitted model objects

hatvalues(<splm>) hatvalues(<spautor>) hatvalues(<spglm>) hatvalues(<spgautor>): Compute leverage (hat) values

influence(<splm>) influence(<spautor>) influence(<spglm>) influence(<spgautor>): Regression diagnostics

labels(<splm>) labels(<spautor>) labels(<spglm>) labels(<spgautor>): Find labels from object

logLik(<splm>) logLik(<spautor>) logLik(<spglm>) logLik(<spgautor>): Extract log-likelihood

loocv(): Perform leave-one-out cross validation

model.frame(<splm>) model.frame(<spautor>) model.frame(<spglm>) model.frame(<spgautor>): Extract the model frame from a fitted model object

model.matrix(<splm>) model.matrix(<spautor>) model.matrix(<spglm>) model.matrix(<spgautor>): Extract the model matrix from a fitted model object

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

plot(<splm>) plot(<spautor>) plot(<spglm>) plot(<spgautor>): Plot fitted model diagnostics

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)

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

pseudoR2(): Compute a pseudo r-squared

randcov_initial(): Create a random effects covariance parameter initial object

randcov_params(): Create a random effects covariance parameter object

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

seal: Estimated harbor-seal trends from abundance data in southeast Alaska, USA

spautor(): Fit spatial autoregressive models

spautorRF(): Fit random forest spatial residual models

spcov_initial(): Create a spatial covariance parameter initial object

spcov_params(): Create a spatial covariance parameter object

spgautor(): Fit spatial generalized autoregressive models

spglm(): Fit spatial generalized linear models

splm(): Fit spatial linear models

splmRF(): Fit random forest spatial residual models

sprbeta(): Simulate a spatial beta random variable

sprbinom(): Simulate a spatial binomial random variable

sprgamma(): Simulate a spatial gamma random variable

sprinvgauss(): Simulate a spatial inverse gaussian random variable

sprnbinom(): Simulate a spatial negative binomial random variable

sprnorm(): Simulate a spatial normal (Gaussian) random variable

sprpois(): Simulate a spatial Poisson random variable

sulfate: Sulfate atmospheric deposition in the conterminous USA

sulfate_preds: Locations at which to predict sulfate atmospheric deposition in the conterminous USA

summary(<splm>) summary(<spautor>) summary(<spglm>) summary(<spgautor>): Summarize a fitted model object

tidy(<splm>) tidy(<spautor>) tidy(<spglm>) tidy(<spgautor>): Tidy a fitted model object

varcomp(): Variability component comparison

vcov(<splm>) vcov(<spautor>) vcov(<spglm>) vcov(<spgautor>): Calculate variance-covariance matrix for a fitted model object