cv.cggd: Computes K-fold cross-validated error curve for cggd

In cggd: Continuous Generalized Gradient Descent

Description

Computes the K-fold cross-validated mean squared prediction error for cggd.

Usage

cv.cggd(x, y, nfolds = 6, kmax=40 , 
           trace = FALSE, plot.it = TRUE, se=TRUE, ...)

Arguments

x	Input to cggd
y	Input to cggd
nfolds	Number of folds
kmax	Max number of iterations per model
trace	Show computations
plot.it	Plot it
se	Include standard error bands
...	Additional arguments to cggd

Value

cv	The CV loss curve at each value of k
cv.error	The standard error of the CV curve

Author(s)

Cun-Hui Zhang and Ofer Melnik

References

Cun-Hui Zhang (2007) "Continuous Generalized Gradient Descent" Journal of Computational and Graphical Statistics ; see also http://stat.rutgers.edu/~cunhui/software/CGGD.html.

Examples

data(Wine)
attach(Wine)
cv.cggd(x.learning,y.learning,kmax=40,fctr=1e3)
detach(Wine)

cggd documentation built on May 30, 2017, 4:33 a.m.