Generate or extract a list of fitted model objects from a
"model.selection" table, optionally using parallel computation in a
get.models(object, subset, cluster = NA, ...)
object returned by
subset of models, an expression evaluated within the model selection table (see ‘Details’).
additional arguments to update the models. For example, in
subset must be explicitely provided. This is to assure that
a potentially long list of models is not fitted unintentionally. To evaluate all
subset is a character vector, it is interpreted as names of rows to be
list of fitted model objects.
eval can be used to compute a model out of the
"model.selection" table (e.g.
eval(getCall(<model.selection>, i)), where
i is the model index or name).
dredge is not efficient as the requested models
have to be fitted again. If the number of generated models is reasonable, consider using
lapply(dredge(..., evaluate = FALSE), eval), which generates a list of all model
calls and evaluates them into a list of model objects. This avoids fitting the
pget.models is still available, but is deprecated.
makeCluster in packages parallel and snow
# Mixed models: fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject, method = "ML") ms2 <- dredge(fm2) # Get top-most models, but fitted by REML: (confset.d4 <- get.models(ms2, subset = delta < 4, method = "REML")) ## Not run: # Get the top model: get.models(ms2, subset = 1)[] ## End(Not run)
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