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

Computes empirical weights based on out of sample forecast variances, following Bates and Granger (1969).

1 |

`object, ...` |
two or more fitted |

`data` |
a data frame containing the variables in the model. |

`force.update` |
if |

Bates-Granger model weights are calculated using prediction covariance. To
get the estimate of prediction covariance, the models are fitted to
randomly selected half of `data`

and prediction is done on the
remaining half.
These predictions are then used to compute the variance-covariance between
models, *Σ*. Model weights are then calculated as
*w_BG = (1' Σ⁻¹ 1)⁻¹ 1 Σ⁻¹
*,
where *1* a vector of 1-s.

Bates-Granger model weights may be outside of the *[0,1]* range, which
may cause the averaged variances to be negative. Apparently this method
works best when data is large.

The function returns a numeric vector of model weights.

For matrix inversion, `ginv`

from package
MASS is more stable near singularities than `solve`

. It
will be used as a fallback if `solve`

fails and MASS is
available.

Carsten Dormann, Kamil Bartoń

Bates, J. M. & Granger, C. W. J. (1969) The combination of forecasts.
*Journal of the Operational Research Society*, 20: 451-468.

Other model.weights: `bootWeights`

,
`cos2Weights`

,
`jackknifeWeights`

,
`stackingWeights`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
fm <- glm(Prop ~ mortality + dose, family = binomial, Beetle, na.action = na.fail)
models <- lapply(dredge(fm, evaluate = FALSE), eval)
ma <- model.avg(models)
# this produces warnings because of negative variances:
set.seed(78)
Weights(ma) <- BGWeights(ma, data = Beetle)
coefTable(ma, full = TRUE)
# SE for prediction is not reliable if some or none of coefficient's SE
# are available
predict(ma, data = test.data, se.fit = TRUE)
coefTable(ma, full = TRUE)
``` |

MuMIn documentation built on Jan. 31, 2018, 1:03 a.m.

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