cos2weights: Cos-squared model weights

Description Usage Arguments Value Author(s) References See Also Examples

Description

Calculates cos-squared model weights, following the algorithm outlined in the appendix of Garthwaite & Mubwandarikwa (2010).

Usage

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cos2Weights(object, ..., data, eps = 1e-06, maxit = 100,
  predict.args = list())

Arguments

object, ...

two or more fitted glm objects, or a list of such, or an "averaging" object. Currently only lm and glm objects are accepted.

data

a test data frame in which to look for variables for use with prediction. If omitted, the fitted linear predictors are used.

eps

tolerance for determining convergence.

maxit

maximum number of iterations.

predict.args

optionally, a list of additional arguments to be passed to predict.

Value

The function returns a numeric vector of model weights.

Author(s)

Carsten Dormann, adapted by Kamil Bartoń

References

Garthwaite, P. H. and Mubwandarikwa, E. (2010) Selection of weights for weighted model averaging. Australian & New Zealand Journal of Statistics, 52: 363–382.

Dormann, C. et al. (2018) Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs, 88, 485–504.

See Also

Weights, model.avg

Other model.weights: BGWeights, bootWeights, jackknifeWeights, stackingWeights

Examples

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fm <- lm(y ~ X1 + X2 + X3 + X4, Cement, na.action = na.fail)
# most efficient way to produce a list of all-subsets models
models <- lapply(dredge(fm, evaluate = FALSE), eval)
ma <- model.avg(models)

test.data <- Cement
Weights(ma) <- cos2Weights(models, data = test.data)
predict(ma, data = test.data)

MuMIn documentation built on April 17, 2020, 1:14 a.m.