Sum of model weights over all models including each explanatory variable.
either a list of fitted model objects, or a
a named numeric vector of so called relative importance values, for each predictor variable.
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# 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))
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