model.selection.object: Description of Model Selection Objects
Description
An object of class "model.selection"
holds a table of model
coefficients and ranking statistics. It is a produced by dredge
or model.sel
.Value
- The object is a
data.frame
with additional attributes. Each row
represents one model. The models are ordered by the information criterion
value specified by rank
(lowest on top).
Data frame columns: - model termsFor numeric covariates these columns hold coeficent value,
for factors their presence in the model. If the term is not present in a
model, value is
NA
. - varying argumentsoptional. If any arguments differ between the
modelling function calls (except for formulas and some other arguments),
these will be held in additional columns (of class
"factor"
). - "df"Number of model parameters
- "logLik"Log-likelihood (or quasi-likelihood for GEE)
- rankInformation criterion value
- "delta"latex{$\Delta_{IC}$}{Δ{Delta}_IC}
- "weight"Akaike weights.
- Attributes:
- model.callsA list containing model calls (arranged in
the same order as in the table). A model call can be retrieved with
getCall(*, i)
where i is a vector of model index or name
(if given as character string). - globalThe
global.model
object - global.callCall to the
global.model
- termsA character string holding all term names. Attribute
"interceptLabel"
gives the name of intercept term. - rankThe
rank
function used - betaA character string, representing the coefficient standardizing
method used. Either
"none"
, "sd"
or "partial.sd"
- coefTablesList of matrices of class
"coefTable"
containing
each model's coefficents with std. errors and associated dfs - nobsNumber of observations
- warningsoptional (
pdredge
only). A list of errors and
warnings issued by the modelling function during the fitting, with model
number appended to each. - Most attributes does not need (and should not) be accessed directly, use of extractor
functions is preferred. These functions include
getCall
for
retrieving model calls, coefTable
and coef
for coefficiens,
and nobs
. logLik
extracts list of model log-likelihoods (as
"logLik"
objects), and Weights
extracts Akaike
weights.
The object has class c("model.selection", "data.frame")
.