cos2Weights | R Documentation |

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 |

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.

`Weights`

, `model.avg`

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)
```

MuMIn documentation built on March 31, 2023, 8:33 p.m.

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