Model averaging based on an information criterion.
model.avg(object, ..., revised.var = TRUE)# S3 method for default
model.avg(object, ..., beta = c("none", "sd", "partial.sd"),
rank = NULL, rank.args = NULL, revised.var = TRUE,
dispersion = NULL, ct.args = NULL)
# S3 method for model.selection
model.avg(object, subset, fit = FALSE, ..., revised.var = TRUE)
a fitted model object or a list of such objects, or a
"model.selection" object. See ‘Details’.
for default method, more fitted model objects. Otherwise, arguments that are passed to the default method.
indicates whether and how the component models' coefficients
should be standardized. See the argument's description in
dredge.
optionally, a rank function (returning an information criterion) to
use instead of AICc, e.g. BIC or QAIC, may be
omitted if object is a model list returned by get.models
or a "model.selection" object. See ‘Details’.
optional list of arguments for the rank
function. If one is an expression, an x within it is substituted
with a current model.
logical, indicating whether to use revised formula for
standard errors. See par.avg.
the dispersion parameter for the family used. See
summary.glm. This is used currently only with glm,
is silently ignored otherwise.
optional list of arguments to be passed to
coefTable (besides dispersion).
see subset method for
"model.selection" object.
if TRUE, the component models are fitted using
get.models. See ‘Details’.
An object of class "averaging" is a list with components:
a data.frame with log-likelihood, IC,
<U+0394>_IC and
‘Akaike weights’ for the component models.
Its attribute "term.codes" is a named vector with numerical
representation of the terms in the row names of msTable.
a matrix of model-averaged coefficients.
“full” coefficients in first row, “subset” coefficients in
second row. See ‘Note’
a 3-dimensional array of component models' coefficients,
their standard errors and degrees of freedom.
object of class importance containing relative
importance values of each term (including interactions), calculated as a sum
of the Akaike weights over all of the models in which the term
appears.
a formula corresponding to the one that would be used in a single model. The formula contains only the averaged (fixed) coefficients.
the matched call.
The object has following attributes:
the rank function used.
optionally, a list of all component model objects. Only if the object was created with model objects (and not model selection table).
Corresponds to the function argument.
number of observations.
Corresponds to the function argument.
model.avg may be used either with a list of models, or directly with a
model.selection object (e.g. returned by dredge). In the
latter case, the models from the model selection table are not evaluated
unless the argument fit is set to TRUE or some additional
arguments are present (such as rank or dispersion). This
results in much faster calculation, but has certain drawbacks, because the
fitted component model objects are not stored, and some methods (e.g.
predict, fitted, model.matrix or vcov) would not
be available with the returned object. Otherwise, get.models is
called prior to averaging, and … are passed to it.
For a list of model types that are accepted see list of supported models.
rank is found by a call to match.fun and typically is
specified as a function or a symbol or a character string specifying a
function to be searched for from the environment of the call to lapply.
rank must be a function able to accept model as a first argument and
must always return a numeric scalar.
Several standard methods for fitted model objects exist for class
averaging, including summary,
predict, coef, confint,
formula, and vcov.
coef, vcov, confint and coefTable accept argument
full that if set to TRUE, the full model-averaged coefficients
are returned, rather than subset-averaged ones (when full = FALSE,
being the default).
logLik returns a list of logLik objects
for the component models.
Burnham, K. P. and Anderson, D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, Springer-Verlag.
Lukacs, P. M., Burnham K. P. and Anderson, D. R. (2009) Model selection bias and Freedman<U+2019>s paradox. Annals of the Institute of Statistical Mathematics 62(1): 117<U+2013>125.
See par.avg for more details of model averaged parameter
calculation.
dredge, get.models
AICc has examples of averaging models fitted by REML.
modavg in package AICcmodavg, and
coef.glmulti in package glmulti also perform model
averaging.
# NOT RUN {
# Example from Burnham and Anderson (2002), page 100:
fm1 <- lm(y ~ ., data = Cement, na.action = na.fail)
(ms1 <- dredge(fm1))
#models with delta.aicc < 4
summary(model.avg(ms1, subset = delta < 4))
#or as a 95% confidence set:
avgmod.95p <- model.avg(ms1, cumsum(weight) <= .95)
confint(avgmod.95p)
# }
# NOT RUN {
# The same result, but re-fitting the models via 'get.models'
confset.95p <- get.models(ms1, cumsum(weight) <= .95)
model.avg(confset.95p)
# Force re-fitting the component models
model.avg(ms1, cumsum(weight) <= .95, fit = TRUE)
# Models are also fitted if additional arguments are given
model.avg(ms1, cumsum(weight) <= .95, rank = "AIC")
# }
# NOT RUN {
# }
# NOT RUN {
# using BIC (Schwarz's Bayesian criterion) to rank the models
BIC <- function(x) AIC(x, k = log(length(residuals(x))))
model.avg(confset.95p, rank = BIC)
# the same result, using AIC directly, with argument k
# 'x' in a quoted 'rank' argument is substituted with a model object
# (in this case it does not make much sense as the number of observations is
# common to all models)
model.avg(confset.95p, rank = AIC, rank.args = alist(k = log(length(residuals(x)))))
# }
# NOT RUN {
# }
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