Description Usage Arguments Value Author(s) See Also Examples
Performs a Kfold crossvalidation for GAMBoost
in search for the optimal number of boosting steps.
1 2 3 4 5 6 
x 

y 
response vector of length 
x.linear 
optional 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
maxstepno 
maximum number of boosting steps to evaluate. 
family,weights,calc.hat,calc.se 
arguments passed to 
trace 
logical value indicating whether information on progress should be printed. 
parallel 
logical value indicating whether computations in the crossvalidation folds should be performed in parallel on a compute cluster, using package 
upload.x 
logical value indicating whether 
multicore 
indicates whether computations in the crossvalidation folds should be performed in parallel, using package 
folds 
if not 
K 
number of folds to be used for crossvalidation. 
type, pred.cutoff 
goodnessoffit criterion: likelihood ( 
just.criterion 
logical value indicating wether a list with the goodnessoffit information should be returned or a 
... 
miscellaneous parameters for the calls to 
GAMBoost
fit with the optimal number of boosting steps or list with the following components:
criterion 
vector with goodnessof fit criterion for boosting step 
se 
vector with standard error estimates for the goodnessoffit criterion in each boosting step. 
selected 
index of the optimal boosting step. 
folds 
list of length 
Harald Binder binderh@unimainz.de
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## Not run:
## Generate some data
x < matrix(runif(100*8,min=1,max=1),100,8)
eta < 0.5 + 2*x[,1] + 2*x[,3]^2
y < rbinom(100,1,binomial()$linkinv(eta))
## Fit the model with smooth components
gb1 < GAMBoost(x,y,penalty=400,stepno=100,trace=TRUE,family=binomial())
## 10fold crossvalidation with prediction error as a criterion
gb1.crit < cv.GAMBoost(x,y,penalty=400,maxstepno=100,trace=TRUE,
family=binomial(),
K=10,type="error",just.criterion=TRUE)
## Compare AIC and estimated prediction error
which.min(gb1$AIC)
which.min(gb1.crit$criterion)
## End(Not run)

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