Compute model weights using bootstrap.
bootWeights(object, ..., R, rank = c("AICc", "AIC", "BIC"))A numeric vector of model weights.
two or more fitted glm objects, or a
list of such, or an "averaging" object.
the number of replicates.
a character string, specifying the information criterion to use
for model ranking. Defaults to AICc.
Kamil Bartoń, Carsten Dormann
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.
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.
Weights, model.avg
Other model weights:
BGWeights(),
cos2Weights(),
jackknifeWeights(),
stackingWeights()
# 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)
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