Description Usage Arguments Details Value Author(s) References See Also Examples
This routine helps in finding an optimum stepsize modification factor for GAMBoost
, i.e., that results in an optimum in terms of crossvalidated loglikelihood.
1 2 3 4 
x 

y 
response vector of length 
x.linear 
optional 
direction 
direction of line search for an optimal stepsize modification factor (starting from value 1). 
start.stepsize 
step size used for the line search. A final step is performed using half this size. 
iter.max 
maximum number of search iterations. 
constant.cv.res 
result of 
parallel 
logical value indicating whether evaluation of crossvalidation folds should be performed in parallel
on a compute cluster. This requires library 
trace 
logical value indicating whether information on progress should be printed. 
... 
miscellaneous parameters for 
A coarse line search is performed for finding the best parameter stepsize.factor.linear
for GAMBoost
. If an pendistmat.linear
argument is provided (which is passed on to GAMBoost
), a search for factors smaller than 1 is sensible (corresponding to direction="down"
). If no connection information is provided, it is reasonable to employ direction="both"
, for avoiding restrictions without subject matter knowledge.
List with the following components:
factor.list 
array with the evaluated stepsize modification factors. 
critmat 
matrix with the mean loglikelihood for each stepsize modification factor in the course of the boosting steps. 
optimal.factor.index 
index of the optimal stepsize modification factor. 
optimal.factor 
optimal stepsize modification factor. 
optimal.step 
optimal boosting step number, i.e., with minimum mean loglikelihood, for stepsize modification factor 
Written by Harald Binder binderh@unimainz.de.
Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of highdimensional risk prediction models. BMC Bioinformatics. 10:18.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  ## Not run:
## Generate some data
n < 100; p < 10
# covariates with nonlinear (smooth) effects
x < matrix(runif(n*p,min=1,max=1),n,p)
eta < 0.5 + 2*x[,1] + 2*x[,3]^2 + x[,9].5
y < rbinom(n,1,binomial()$linkinv(eta))
# Determine stepsize modification factor for a generalize linear model
# As there is no connection matrix, perform search into both directions
optim.res < optimStepSizeFactor(direction="both",
y=y,x.linear=x,family=binomial(),
penalty.linear=200,
trace=TRUE)
# Fit with obtained stepsize modification parameter and optimal number of boosting
# steps obtained by crossvalidation
gb1 < GAMBoost(x=NULL,y=y,x.linear=x,family=binomial(),penalty.linear=200,
stepno=optim.res$optimal.step,
stepsize.factor.linear=optim.res$optimal.factor)
summary(gb1)
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.