sumofweights: Per-variable sum of model weights

In MuMIn: Multi-Model Inference

Per-variable sum of model weights

Description

Sum of model weights over all models including each explanatory variable.

Usage

sw(x)
importance(x)

Arguments

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

Value

a named numeric vector of so called relative importance values, for each predictor variable.

Author(s)

Kamil Bartoń

See Also

Weights

dredge, model.avg, model.sel

Examples

# 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))

MuMIn documentation built on June 22, 2024, 6:44 p.m.