Per-variable sum of model weights
Sum of model weights over all models including each explanatory variable.
sw(x) importance(x)
x |
either a list of fitted model objects, or a |
a named numeric vector of so called relative importance values, for each predictor variable.
Kamil Bartoń
# Generate some models
fm1 <- lm(y ~ ., data = Cement, na.action = na.fail)
ms1 <- dredge(fm1)
# Sum of weights can be calculated/extracted from various objects:
sw(ms1)
## Not run:
sw(subset(model.sel(ms1), delta <= 4))
sw(model.avg(ms1, subset = delta <= 4))
sw(subset(ms1, delta <= 4))
sw(get.models(ms1, delta <= 4))
## End(Not run)
# Re-evaluate SW according to BIC
# note that re-ranking involves fitting the models again
# 'nobs' is not used here for backwards compatibility
lognobs <- log(length(resid(fm1)))
sw(subset(model.sel(ms1, rank = AIC, rank.args = list(k = lognobs)),
cumsum(weight) <= .95))
# This gives a different result than previous command, because 'subset' is
# applied to the original selection table that is ranked with 'AICc'
sw(model.avg(ms1, rank = AIC, rank.args = list(k = lognobs),
subset = cumsum(weight) <= .95))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.