bootWeights: Bootstrap model weights

In MuMIn: Multi-Model Inference

Bootstrap model weights

Description

Compute model weights using bootstrap.

Usage

bootWeights(object, ..., R, rank = c("AICc", "AIC", "BIC"))

Arguments

object, ...	two or more fitted glm objects, or a list of such, or an "averaging" object.
R	the number of replicates.
rank	a character string, specifying the information criterion to use for model ranking. Defaults to AICc.

Details

The models are fitted repeatedly to a resampled data set and ranked using AIC-type criterion. The model weights represent the proportion of replicates when a model has the lowest IC value.

Value

A numeric vector of model weights.

Author(s)

Kamil Bartoń, Carsten Dormann

References

Dormann, C. et al. 2018 Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs 88, 485–504.

See Also

Weights, model.avg

Other model weights: BGWeights(), cos2Weights(), jackknifeWeights(), stackingWeights()

Examples

# To speed up the bootstrap, use 'x = TRUE' so that model matrix is included
#     in the returned object
fm <- glm(Prop ~ mortality + dose, family = binomial, data = Beetle, 
   na.action = na.fail, x = TRUE)

fml <- lapply(dredge(fm, eval = FALSE), eval)
am <- model.avg(fml)

Weights(am) <- bootWeights(am, data = Beetle, R = 25)

summary(am)

MuMIn documentation built on Aug. 7, 2023, 3 p.m.