MuMIn: get.models – R documentation

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get.models

Retrieve models from selection table

Description

Generate or extract a list of fitted model objects from a "model.selection" table, optionally using parallel computation in a cluster.

Usage

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

Arguments

`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.

Details

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.

Value

list of fitted model objects.

Note

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.

Author(s)

Kamil Bartoń

Examples

# 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

Multi-Model Inference

v1.43.17

GPL-2

Authors