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AICcmodavg (version 1.01)

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

5,782

Version

1.01

License

GPL (>= 2 )

Maintainer

Marc J Mazerolle

Last Published

September 4th, 2009

Functions in AICcmodavg (1.01)

aictab

Function Creating Model Selection Tables
confset

Computing Confidence Set for the Kullback-Leibler Best Model
dry.frog

Frog dehydration experiment on three different substrate types.
c_hat

Compute Estimate of Dispersion for Poisson and Binomial GLM's
cement

Heat expended following hardening of Portland cement.
evidence

Compute Evidence Ratio Between Two Models
AICc

Computing AIC, AICc, QAIC, and QAICc
modavg

Model-average Parameter Estimate (Multimodel Inference)
min.trap

Anuran larvae counts in minnow traps across pond type.
beetle

Flour beetle data.
pine

Strength of pine wood based on the density adjusted for resin content.
AICcmodavg-package

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

Compute Importance Values of Variable
modavgpred

Computing Model-averaged Predictions