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

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

1 2 | ```
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`

1 2 3 4 5 6 7 8 | ```
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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.