# model.sel

##### model selection table

Build a model selection table.

- Keywords
- models

##### Usage

`model.sel(object, ...)`# S3 method for default
model.sel(object, ..., rank = NULL, rank.args = NULL,
beta = c("none", "sd", "partial.sd"), extra)
# S3 method for model.selection
model.sel(object, rank = NULL, rank.args = NULL, fit = NA,
..., beta = c("none", "sd", "partial.sd"), extra)

##### 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 (returning an information criterion) to use instead of the default

`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.- fit
logical, stating whether the model objects should be re-fitted if they are not stored in the

`"model.selection"`

object. Set to`NA`

to re-fit the models only if this is needed. See ‘Details’.- beta
indicates whether and how the component models' coefficients should be standardized. See the argument's description in

`dredge`

.- extra
optional additional statistics to include in the result, provided as functions, function names or a list of such (best if named or quoted). See

`dredge`

for details.

##### Details

`model.sel`

used with `"model.selection"`

object will re-fit model
objects, unless they are stored in `object`

(in attribute `"modelList"`

),
if argument `extra`

is provided, or the requested `beta`

is different
than object's `"beta"`

attribute, or the new `rank`

function
cannot be applied directly to `logLik`

objects, or new `rank.args`

are given (unless argument `fit = FALSE`

).

##### Value

An object of class `c("model.selection", "data.frame")`

, being a
`data.frame`

, where each row represents one model and columns contain
useful information about each model: the coefficients, *df*, log-likelihood, the
value of the information criterion used,
<U+0394>_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).

See `model.selection.object`

for its structure.

##### See Also

`dredge`

, `AICc`

, list of supported
models.

Possible alternatives: `ICtab`

(in package bbmle), or
`aictab`

(AICcmodavg).

##### Examples

```
# NOT RUN {
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))))
# }
```

*Documentation reproduced from package MuMIn, version 1.43.6, License: GPL-2*