MuMIn (version 1.15.6)

arm.glm: Adaptive Regression by Mixing

Description

Combine all-subsets GLMs using the ARM algorithm.

Usage

arm.glm(object, R = 250, weight.by = c("aic", "loglik"), trace = FALSE)

Arguments

object

a fitted “global” glm object.

R

number of permutations.

weight.by

indicates whether model weights should be calculated with AIC or log-likelihood.

trace

if TRUE, information is printed during the running of arm.glm.

Value

An object of class "averaging" contaning only “full” averaged coefficients. See model.avg for object description.

Details

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.

References

Yang Y. (2001) Adaptive Regression by Mixing. Journal of the American Statistical Association 96: 574<U+2013>588.

Yang Y. (2003) Regression with multiple candidate models: selecting or mixing? Statistica Sinica 13: 783<U+2013>810.

See Also

model.avg, par.avg

Other implementation: arms in (archived) package MMIX.

Examples

# NOT RUN {
fm <- glm(y ~ X1 + X2 + X3 + X4, data = Cement)

summary(arm.glm(fm, R = 25))
# }