# cos2weights: Cos-squared model weights In MuMIn: Multi-Model Inference

 cos2Weights R Documentation

## Cos-squared model weights

### Description

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

### Usage

```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

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.

`Weights`, `model.avg`

Other model weights: `BGWeights()`, `bootWeights()`, `jackknifeWeights()`, `stackingWeights()`

### Examples

```
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 18, 2022, 5:28 p.m.