coef.glmulti: Multimodel inference with glmulti

Description

These functions, applied on a glmulti object, produce model-averaged coefficients and predictions from the multiple models in the confidence set (or a subset of them). This allows easy multi-model inference.

Usage

# S3 coef method for class 'glmulti'
coef.glmulti(object, select="all", varweighting="Buckland", alphaIC=0.05,  ...)
# S3 predict method for class 'glmulti'
predict.glmulti(object, select="all", newdata=NA, ...)

Arguments

object

an object of class glmulti

select

A specification of which models should be used for inference. By default all models are used, see below.

varweighting

The method to be used to compute the unconditional variance. "Buckland" implements the approach presented in Buckland et al. 1997. "Johnson" implements a slightly different approach suggested in Johnson & Omland 2004. The latter results in slightly smal

newdata

New data for which to predict values

alphaIC

The alpha risk when building the confidence interval for model averaged estimates

...

Further arguments.

Value

coef returns a data.frame with model-averaged estimates of the different parameters in the models, as well as their unconditional variance, importance, and a sophisticated confidence interval proposed by Burnham & Anderson 2002.
predict returns a list of three data.frames: the multi-model predictions, their variability across models, and the predictions from each model separetely.

Details

select can be used to specify which models should be used for inference. By default all are used. If specifying an integer value x, only the x best models are used. If a numeric value is provided, if it less than one, models that sum up to x% of evidence weight are used. If it more than one, models within x IC units from the best model are used.

References