AICcmodavg-package: Model Selection and Multimodel Inference Based on (Q)AIC(c)

Description

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 features functions to conduct classic model averaging (multimodel inference) for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates. Other handy functions enable the computation of relative variable importance, evidence ratios, and confidence sets for the best model. The present version works with linear models ('lm' class), generalized linear models ('glm' class), linear models fit by generalized least squares ('gls' class), linear mixed models ('lme' class), generalized linear mixed models ('mer' class), multinomial and ordinal logistic regressions ('multinom' and 'polr' classes).

Arguments

Details

ll{ Package: AICcmodavg Type: Package Version: 1.15 Date: 2011-04-19 License: GPL (>=2 ) LazyLoad: yes } This package contains several useful functions for model selection and multimodel inference:

AICc

{Computes AIC, AICc, and their quasi-likelihood counterparts (QAIC, QAICc).} aictab {Constructs model selection tables with number of parameters, AIC, delta AIC, Akaike weights or variants based on other AICc, QAIC, and QAICc for a set of candidate models.} confset {Determines the confidence set for the best model based on one of three criteria.} evidence {Computes the evidence ratio between the highest-ranked model based on the information criteria selected and a lower-ranked model.} extractSE.mer {Extracts standard errors of the fixed effects of a generalized linear mixed model of class 'mer' and adds the labels.} fam.link.mer {Extracts the distribution family and link function from a generalized linear mixed model of class 'mer'.} importance {Computes importance values (w+) for the support of a given parameter among set of candidate models.} modavg {Computes model-averaged estimate, unconditional standard error, and unconditional confidence interval of a parameter of interest among a set of candidate models.} modavg.shrink {Computes shrinkage version of model-averaged estimate, unconditional standard error, and unconditional confidence interval of a parameter of interest among a set of candidate models.} modavgpred {Computes model-average predictions and unconditional SE's among entire set of candidate models.} c_hat {Computes an estimate of variance inflation factor for binomial or Poisson GLM's based on Pearson's chi-square.} predictSE.lme {Computes predictions and associated standard errors for 'lme' object based on fixed effects (i.e., population predictions).} predictSE.mer {Computes predictions and associated standard errors for 'mer' object based on fixed effects (i.e., population predictions).}

References

Anderson, D. R. (2008) Model-based inference in the life sciences: a primer on evidence. Springer: New York.

Burnham, K. P., and Anderson, D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer: New York.

Burnham, K. P., Anderson, D. R. (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research 33, 261--304.

Mazerolle, M. J. (2006) Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. Amphibia-Reptilia 27, 169--180.