MuMIn (version 1.9.5)

model.sel: model selection table

Description

Build a model selection table.

Usage

model.sel(object, ...)
## S3 method for class 'model.selection':
model.sel(object, rank = NULL, rank.args = NULL, ...)
## S3 method for class 'default':
model.sel(object, ..., rank = NULL, rank.args = NULL)

Arguments

object

A fitted model object, a list of such objects, or a "model.selection" object.

...

More fitted model objects.

rank

Optional, custom rank function (information criterion) to use instead of AICc, e.g. QAIC or BIC, may be omitted if object is a model list returned by get.models.

rank.args

Optional list of arguments for the rank function. If one is an expression, an x within it is substituted with a current model.

Value

• An object of class "model.selection" with columns containing useful information about each model: the coefficients, df, log-likelihood, the value of the information criterion used, Delta(IC) and Akaike weight. If any arguments differ between the modelling function calls, the result will include additional columns showing them (except for formulas and some other arguments).

encoding

utf-8

See Also

dredge, AICc, list of supported models.

Possible alternatives: ICtab (in package bbmle), or aictab (AICcmodavg).

Examples

data(Cement)
Cement$X1 <- cut(Cement$X1, 3)
Cement$X2 <- cut(Cement$X2, 2)

fm1 <- glm(formula = y ~ X1 + X2 * X3, data = Cement)
fm2 <- update(fm1, . ~ . - X1 - X2)
fm3 <- update(fm1, . ~ . - X2 - X3)

## ranked with AICc by default
(msAICc <- model.sel(fm1, fm2, fm3))

## ranked with BIC
model.sel(fm1, fm2, fm3, rank = AIC, rank.args = alist(k = log(nobs(x))))
# or
# model.sel(msAICc, rank = AIC, rank.args = alist(k = log(nobs(x))))
# or
# update(msAICc, rank = AIC, rank.args = alist(k = log(nobs(x))))