Description Usage Arguments Value Author(s) References See Also Examples
GLMBoost
a convenience wrapper around GAMBoost
, for fitting generalized linear models by likelihood based boosting.
1 
x 

y 
response vector of length 
penalty 
penalty value (scalar or vector of length q) for update of individual linear components in each boosting step. If this is set to 
standardize 
logical value indicating whether linear covariates should be standardized for estimation. 
... 
arguments that should be passed to 
Object returned by call to GAMBoost
(see documentation there), with additional class GLMBoost
.
Harald Binder binderh@unimainz.de
Tutz, G. and Binder, H. (2007) Boosting ridge regression. Computational Statistics \& Data Analysis, 51(12), 6044–6059.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## Generate some data
x < matrix(runif(100*8,min=1,max=1),100,8)
eta < 0.5 + 2*x[,1] + 4*x[,3]
y < rbinom(100,1,binomial()$linkinv(eta))
## Fit a model with only linear components
gb1 < GLMBoost(x,y,penalty=100,stepno=100,trace=TRUE,family=binomial())
# Inspect the AIC for a minimum
plot(gb1$AIC)
# print the selected covariates, i.e., covariates with nonzero estimates
getGAMBoostSelected(gb1)
## Make the first two covariates mandatory
gb2 < GLMBoost(x,y,penalty=c(0,0,rep(100,ncol(x)2)),
stepno=100,family=binomial(),trace=TRUE)

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