MuMIn (version 1.10.5)

MuMIn-package: Multi-model inference

Description

The package MuMIn contains functions to streamline information-theoretic model selection and carry out model averaging based on the information criteria.

encoding

utf-8

Details

The collection of functions includes: [object Object],[object Object],[object Object],[object Object]

For a complete list of functions, use library(help = "MuMIn").

By default, AIC$_{c}$ is used to rank the models and to obtain model selection probabilities, though any other information criteria can be utilised. At least the following ones are currently implemented in R: AIC and BIC in package stats, and QAIC, QAICc, ICOMP, CAICF, and Mallows' Cp in MuMIn. There is also DIC extractor for MCMC models, and QIC for GEE.

Most of R's common modelling functions are supported, for a full inventory see list of supported models.

References

Burnham, K. P. and Anderson, D. R (2002) Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, Springer-Verlag.

See Also

AIC, step or stepAIC for stepwise model selection by AIC.

Examples

data(Cement)

options(na.action = "na.fail") #  change the default "na.omit" to prevent models 
                               #  from being fitted to different datasets in 
                               #  case of missing values.

fm1 <- lm(y ~ ., data = Cement)
ms1 <- dredge(fm1)
plot(ms1)

model.avg(ms1, subset = delta < 4)

confset.95p <- get.models(ms1, cumsum(weight) <= .95)
avgmod.95p <- model.avg(confset.95p)
summary(avgmod.95p)
confint(avgmod.95p)