Cos-squared model weights
Calculates cos-squared model weights, following the algorithm outlined in the appendix of Garthwaite & Mubwandarikwa (2010).
cos2Weights(object, ..., data, eps = 1e-06, maxit = 100, predict.args = list())
object, ... |
two or more fitted |
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 |
The function returns a numeric vector of model weights.
Carsten Dormann, adapted by Kamil Bartoń
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.
Other model.weights: BGWeights,
bootWeights,
jackknifeWeights,
stackingWeights
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)
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