Calculate, extract or set normalized model likelihoods (‘Akaike weights’).
a numeric vector of information criterion values such as AIC, or
objects returned by functions like
numeric, the new weights for the
The replacement function can assign new weights to an
object, affecting coefficient values and order of component models.
For the extractor, a numeric vector of normalized likelihoods.
On assigning new weights, the model order changes accordingly, so assigning
the same weights again will cause incorrect re-calculation of averaged
coefficients. To avoid that, either re-set model weights by assigning
or use ordered weights.
weights, which extracts fitting weights from model objects.
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fm1 <- glm(Prop ~ dose, data = Beetle, family = binomial) fm2 <- update(fm1, . ~ . + I(dose^2)) fm3 <- update(fm1, . ~ log(dose)) fm4 <- update(fm3, . ~ . + I(log(dose)^2)) round(Weights(AICc(fm1, fm2, fm3, fm4)), 3) am <- model.avg(fm1, fm2, fm3, fm4, rank = AICc) coef(am) # Assign equal weights to all models: Weights(am) <- rep(1, 4) # assigned weights are rescaled to sum to 1 Weights(am) coef(am) # Assign dummy weights: wts <- c(2,1,4,3) Weights(am) <- wts coef(am) # Component models are now sorted according to the new weights. # The same weights assigned again produce incorrect results! Weights(am) <- wts coef(am) # wrong! # Weights(am) <- NULL # reset to original model weights Weights(am) <- wts coef(am) # correct
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