get.models: Retrieve models from selection table

get.modelsR Documentation

Retrieve models from selection table


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 returned by dredge.


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


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.


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.


list of fitted model objects.


Alternatively, getCall and 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).

Using get.models following 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 models twice.

pget.models is still available, but is deprecated.


Kamil Bartoń

See Also

dredge and pdredge, model.avg

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)[[1]]

## End(Not run)

MuMIn documentation built on March 31, 2023, 8:33 p.m.