Weights | R Documentation |
Calculate, extract or set normalized model likelihoods (‘Akaike weights’).
Weights(x)
Weights(x) <- value
x |
a numeric vector of information criterion values such as AIC, or
objects returned by functions like |
value |
numeric, the new weights for the |
The replacement function can assign new weights to an "averaging"
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 NULL
,
or use ordered weights.
Kamil Bartoń
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.
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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