MuMIn-package {MuMIn} | R Documentation |
Multi-model inference
Description
The package MuMIn contains functions to streamline information-theoretic model selection and carry out model averaging based on information criteria.
Details
The suite of functions includes:
dredge
performs automated model selection by generating subsets of the supplied ‘global’ model and optional choices of other model properties (such as different link functions). The set of models can be generated with ‘all possible’ combinations or tailored according to specified conditions.
model.sel
creates a model selection table from selected models.
model.avg
calculates model-averaged parameters, along with standard errors and confidence intervals. The
predict
method produces model-averaged predictions.AICc
calculates the second-order Akaike information criterion. Some other criteria are provided, see below.
stdize
,stdizeFit
,std.coef
,partial.sd
can be used to standardise data and model coefficients by standard deviation or partial standard deviation.
For a complete list of functions, use library(help = "MuMIn")
.
By default, AIC is used to rank models and obtain model
weights, although any information criterion can be used. At least the
following are currently implemented in R:
AIC
and BIC
in package stats, and
QAIC
, QAICc
, ICOMP
,
CAICF
, and Mallows' Cp in MuMIn. There is also a
DIC
extractor for MCMC models and a QIC
for
GEE.
Many common modelling functions in R are supported. For a complete list, see the list of supported models.
In addition to “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 are mainly applicable to glm
s.
Author(s)
Kamil Bartoń
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
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:
par(mar = c(3,5,6,4))
plot(ms1, labAsExpr = TRUE)
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)