coef.glmulti {glmulti} | R Documentation |
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.
# S3 coef method for class 'glmulti' coef.glmulti(object, select="all", varweighting="Buckland", ...) # S3 predict method for class 'glmulti' predict.glmulti(object, select="all", ...)
object |
an object of calss 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 smaller estimates of the unconditional variance of model coefficients. |
... |
Further arguments. |
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.
coef
returns a data.frame with model-averaged estimates of the different parameters in the models, as well as their unconditional variance and their importance.
predict
returns a list of three data.frames: the multi-model predictions, their variability across models, and the predictions from each model separetely.
Vincent Calcagno, McGill University
Buckland et al. 1997. Model selection: an integral part of inference. Biometrics. Johnson & Omland. 2004. Model selection in ecology and evolution. TREE.