Generate or extract a list of fitted model objects from a
`"model.selection"`

table, optionally using parallel computation in a
cluster.

`get.models(object, subset, cluster = NA, ...)`

object

object returned by `dredge`

.

subset

subset of models, an expression evaluated within the model selection table (see ‘Details’).

cluster

optionally, a `"cluster"`

object. If it is a valid
cluster, models are evaluated using parallel computation.

…

additional arguments to update the models. For example, in
`lme`

one may want to use `method = "REML"`

while using `"ML"`

for model selection.

`list`

of fitted model objects.

The argument `subset`

must be explicitely provided. This is to assure that
a potentially long list of models is not fitted unintentionally. To evaluate all
models, set `subset`

to `NA`

or `TRUE`

.

If `subset`

is a character vector, it is interpreted as names of rows to be
selected.

`makeCluster`

in packages parrallel and snow

# NOT RUN { # Mixed models: # } # NOT RUN { 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)[[1]] # } # NOT RUN { # }

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