MuMIn (version 1.43.17)

Weights: Akaike weights

Description

Calculate, extract or set normalized model likelihoods (‘Akaike weights’).

Usage

Weights(x)
Weights(x) <- value

Arguments

x

a numeric vector of information criterion values such as AIC, or objects returned by functions like AIC. There are also methods for extracting ‘Akaike weights’ from "model.selection" or "averaging" objects.

value

numeric, the new weights for the "averaging" object or NULL to reset the weights based on the original IC used.

Value

For the extractor, a numeric vector of normalized likelihoods.

Details

The replacement function can assign new weights to an "averaging" object, affecting coefficient values and order of component models.

See Also

sw, weighted.mean

armWeights, bootWeights, BGWeights, cos2Weights, jackknifeWeights and stackingWeights can be used to produce model weights.

weights, which extracts fitting weights from model objects.

Examples

# NOT RUN {
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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