Package ‘MuMIn’
Package 'MuMIn'
Package 'MuMIn'
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6 arm.glm<br />
Arguments<br />
object<br />
R<br />
weight.by<br />
trace<br />
a fitted “global” glm object.<br />
number of permutations.<br />
indicates whether model weights should be calculated with AIC or log-likelihood.<br />
if TRUE, information is printed during the running of arm.glm.<br />
Details<br />
For each of all-subsets of the “global” model, parameters are estimated using randomly sampled half<br />
of the data. Log-likelihood given the remaining half of the data is used to calculate AIC weights.<br />
This is repeated R times and mean of the weights is used to average all-subsets parameters estimated<br />
using complete data.<br />
Value<br />
An object of class "averaging" contaning only “full” averaged coefficients. See model.avg for<br />
object description.<br />
Note<br />
Number of parameters is limited to floor(nobs(object) / 2) - 1. All-subsets respect marginality<br />
constraints.<br />
Author(s)<br />
Kamil Bartoń<br />
References<br />
Yang Y. (2001) Adaptive Regression by Mixing. Journal of the American Statistical Association<br />
96: 574–588.<br />
Yang Y. (2003) Regression with multiple candidate models: selecting or mixing? Statistica Sinica<br />
13: 783–810.<br />
See Also<br />
model.avg, par.avg<br />
Other implementation: arms in (archived) package MMIX.<br />
Examples<br />
fm
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