MuMIn (version 1.5.0)

MuMIn-package: Multi-model inference

Description

The package MuMIn contains functions to streamline model selection and perform model averaging based on information criteria (AIC, AICc and alike).

encoding

utf-8

Details

User level functions include: [object Object],[object Object],[object Object],[object Object]

Model selection can be done according to any information criterion, such as AIC, AICc, BIC, QAIC, ICOMP or Mallows' Cp.

References

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

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)