MuMIn (version 1.7.0)

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.

Most of R's common modelling functions are supported, for a full list refer to the help pages for dredge and model.avg.

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)

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

ms1 <- dredge(fm1)
confset.d4 <- get.models(ms1, subset = delta < 4)
model.avg(confset.d4)

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