MuMIn-package | R Documentation |
The package MuMIn contains functions to streamline the information-theoretic model selection and carry out model averaging based on information criteria.
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 can be generated with ‘all possible’ combinations or tailored according to the conditions specified.
model.sel
creates a model selection table from selected models.
model.avg
calculates model-averaged parameters,
along with standard errors and confidence intervals.
Furthermore, 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, AICc is used to rank the models and obtain model
weights, although any other information criteria can be used. 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 a
DIC
extractor for MCMC models, and a QIC
for
GEE.
Most of common modelling functions in R are supported. For a full listing, see the list of supported models.
In addition to 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 are mainly applicable to glm
s.
Kamil Bartoń
Burnham, K. P. and Anderson, D. R (2002) Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, Springer-Verlag.
AIC
, step
or stepAIC
for stepwise
model selection by AIC.
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)
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