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Package index
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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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AUROC()
- Area Under Receiver Operating Characteristic Curve
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BIC(<splm>)
BIC(<spautor>)
BIC(<spglm>)
BIC(<spgautor>)
- Compute BIC of 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