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

Combine all-subsets GLMs using the ARM algorithm. Calculate ARM weights for a set of models.

1 2 3 |

`object` |
for |

`...` |
more fitted model objects. |

`R` |
number of permutations. |

`weight.by` |
indicates whether model weights should be calculated with AIC or log-likelihood. |

`trace` |
if |

`data` |
a data frame in which to look for variables for use with prediction. If omitted, the fitted linear predictors are used. |

For each of all-subsets of the “global” model, parameters are estimated
using randomly sampled half of the data. Log-likelihood given the remaining half
of the data is used to calculate AIC weights. This is repeated `R`

times and mean of the weights is used to average all-subsets parameters
estimated using complete data.

`arm.glm`

returns an object of class `"averaging"`

contaning only
“full” averaged coefficients. See `model.avg`

for object
description.

`armWeights`

returns a numeric vector of model weights.

Number of parameters is limited to `floor(nobs(object) / 2) - 1`

.
All-subsets respect marginality constraints.

Kamil Bartoń

Yang Y. (2001) Adaptive Regression by Mixing.
*Journal of the American Statistical Association* 96: 574–588.

Yang Y. (2003) Regression with multiple candidate models: selecting or mixing?
*Statistica Sinica* 13: 783–810.

`Weights`

for assigning new model weights to an `"averaging"`

object.

Other implementation of ARM algorithm: `arms`

in (archived) package
**MMIX**.

Other kinds of model weights: `BGWeights`

,
`bootWeights`

,
`cos2Weights`

, `jackknifeWeights`

,
`stackingWeights`

.

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MuMIn documentation built on July 25, 2018, 1:04 a.m.

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