MuMIn (version 1.42.1)

importance: Relative variable importance

Description

Sum of ‘Akaike weights’ over all models including the explanatory variable.

Usage

importance(x)

Arguments

x

either a list of fitted model objects, or a "model.selection" or "averaging" object.

Value

a numeric vector of so called relative importance values, named as the predictor variables.

See Also

Weights

dredge, model.avg, model.sel

Examples

# NOT RUN {
# Generate some models
fm1 <- lm(y ~ ., data = Cement, na.action = na.fail)
ms1 <- dredge(fm1)

# Importance can be calculated/extracted from various objects:
importance(ms1)
# }
# NOT RUN {
importance(subset(model.sel(ms1), delta <= 4))
importance(model.avg(ms1, subset = delta <= 4))
importance(subset(ms1, delta <= 4))
importance(get.models(ms1, delta <= 4))
# }
# NOT RUN {
# Re-evaluate the importances 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)))

importance(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'
importance(model.avg(ms1, rank = AIC, rank.args = list(k = lognobs),
    subset = cumsum(weight) <= .95))

# }