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AICcmodavg (version 1.0)

Model selection and multimodel inference based on (Q)AIC(c)

Description

This package includes functions to create model selection tables based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc). Tables are printed with delta AIC and Akaike weights. The package also includes functions to conduct model averaging (multimodel inference) for a given parameter of interest or predicted values. Other handy functions enable the computation of relative variable importance, evidence ratios, and confidence sets for the best model. The present version works with lm, glm, and lme object classes.

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Version

Install

install.packages('AICcmodavg')

Monthly Downloads

8,098

Version

1.0

License

GPL (>= 2 )

Last Published

September 3rd, 2009

Functions in AICcmodavg (1.0)

min.trap

Anuran larvae counts in minnow traps across pond type.
confset

Computing Confidence Set for the Kullback-Leibler Best Model
cement

Heat expended following hardening of Portland cement.
AICcmodavg-package

Model Selection and Multimodel Inference Based on (Q)AIC(c)
modavgpred

Computing Model-averaged Predictions
modavg

Compute Model-averaged Parameter Estimate (Multimodel Inference)
beetle

Flour beetle data.
pine

Strength of pine wood based on the density adjusted for resin content.
aictab

Create Model Selection Tables
evidence

Compute Evidence Ratio Between Two Models
dry.frog

Frog dehydration experiment on three different substrate types.
importance

Compute Importance Values of Variable
predictSE.lme

Computing Predicted Values and Standard Errors
AICc

Computing AIC, AICc, QAIC, and QAICc
c_hat

Compute Estimate of Dispersion for Poisson and Binomial GLM's
extractSE.mer

Extract SE of Fixed Effects of 'glmer' Fit
fam.link.mer

Extract Distribution Family and Link Function
predictSE.mer

Computing Predicted Values and Standard Errors