MuMIn (version 1.43.17)

MuMIn-package: Multi-model inference

Description

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

Details

The collection of functions includes:

dredge

performs an automated model selection with subsets of the supplied ‘global’ model, and optional choices of other model properties (such as different link functions). The set of models may be generated either with ‘all possible’ combinations or tailored according to the conditions specified.

pdredge does the same, but can parallelize model fitting process using a cluster.

model.sel

creates a model selection table from hand-picked models.

model.avg

calculates model-averaged parameters, with standard errors and confidence intervals. Furthermore, the predict method produces model-averaged predictions.

AICc

calculates second-order Akaike information criterion. Some other criteria are provided, see below.

stdize, stdizeFit, std.coef, partial.sd

can be used for standardization of data and model coefficients by Standard Deviation or Partial Standard Deviation.

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

By default, AIC\(_{c}\) is used to rank the models and to obtain model weights, 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 the list of supported models.

Apart from the “regular” information criteria, model averaging can be performed using various types of model weighting algorithms: Bates-Granger, bootstrapped, cos-squared, jackknife, stacking, or ARM. These weighting functions apply mostly to glms.

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

# NOT RUN {
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)

# Visualize the model selection table:
# }
# NOT RUN {
par(mar = c(3,5,6,4))
plot(ms1, labAsExpr = TRUE)
# }
# NOT RUN {
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)
# }