GAMBoost (version 1.2-3)

optimGLMBoostPenalty: Coarse line search for adequate GLMBoost penalty parameter

Description

This routine is a convenience wrapper around optimGAMBoostPenalty for finding a penalty value that leads to an ``optimal'' number of boosting steps for GLMBoost (determined by AIC or cross-validation) that is not too small/in a specified range.

Usage

optimGLMBoostPenalty(x,y,start.penalty=length(y),just.penalty=FALSE,...)

Arguments

x

n * q matrix of covariates with linear influence.

y

response vector of length n.

start.penalty

start value for the search for the appropriate penalty.

just.penalty

logical value indicating whether just the optimal penalty value should be returned or a GLMBoost fit performed with this penalty.

...

miscellaneous parameters for optimGAMBoostPenalty.

Value

GLMBoost fit with the optimal penalty (with an additional component optimGAMBoost.criterion giving the values of the criterion (AIC or cross-validation) corresponding to the final penalty) or just the optimal penalty value itself.

Details

The penalty parameter(s) for GLMBoost have to be chosen only very coarsely. In Tutz and Binder (2006) it is suggested just to make sure, that the optimal number of boosting steps (according to AIC or cross-validation) is larger or equal to 50. With a smaller number of steps boosting may become too ``greedy'' and show sub-optimal performance. This procedure uses very a coarse line search and so one should specify a rather large range of boosting steps.

Penalty optimization based on AIC should work fine most of the time, but for a large number of covariates (e.g. 500 with 100 observations) problems arise and (more costly) cross-validation should be employed.

References

Tutz, G. and Binder, H. (2006) Generalized additive modelling with implicit variable selection by likelihood based boosting. Biometrics, 51, 961--971.

See Also

GLMBoost, optimGAMBoostPenalty, GAMBoost

Examples

## Not run: 
# ##  Generate some data 
# 
# ##  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()$linkinv(eta))
# 
# ##  Find a penalty (starting from a large value, here: 5000) 
# ##  that leads to an optimal number of boosting steps (based in AIC) 
# ##  in the range [50,200] and return a GLMBoost fit with
# ##  this penalty
# 
# opt.gb1 <- optimGLMBoostPenalty(x,y,minstepno=50,maxstepno=200,
#                                 start.penalty=5000,family=binomial(),
#                                 trace=TRUE)
# 
# #   extract the penalty found/used for the fit
# opt.gb1$penalty
# 
# ## End(Not run)