cv.GAMBoost {GAMBoost}R Documentation

Cross-validation for GAMBoost fits

Description

Performs a K-fold cross-validation for GAMBoost in search for the optimal number of boosting steps.

Usage

cv.GAMBoost(x=NULL,y,x.linear=NULL,maxstepno=500,
            K=10,type="loglik",
            just.criterion=FALSE,trace=FALSE,...) 

Arguments

x n * p matrix of covariates with potentially non-linear influence. If this is not given (and argument x.linear is employed), a generalized linear model is fitted.
y response vector of length n.
x.linear optional n * q matrix of covariates with linear influence.
maxstepno maximum number of boosting steps to evaluate.
K number of folds to be used for cross-validation.
type goodness-of-fit criterion: likelihood ("loglik"), error rate for binary response data ("error") or squared error for others ("L2")
just.criterion logical value indicating wether a list with the goodness-of-fit information should be returned or a GAMBoost fit with the optimal number of steps.
trace logical value indicating whether information on progress should be printed.
... miscellaneous parameters for the calls to GAMBoost

Value

GAMBoost fit with the optimal number of boosting steps or list with the following components:

criterion vector with goodness-of fit criterion for boosting step 1 , ... , maxstep
se vector with standard error estimates for the goodness-of-fit criterion in each boosting step.
selected index of the optimal boosting step.

Author(s)

Harald Binder binderh@fdm.uni-freiburg.de

See Also

GAMBoost

Examples

## 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()) 

##  10-fold cross-validation 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)


[Package GAMBoost version 0.9-1 Index]