R: Computes K-fold cross-validated error curve for elastic net
cv.enet
R Documentation
Computes K-fold cross-validated error curve for elastic net
Description
Computes the K-fold cross-validated mean squared prediction error for
elastic net.
Usage
cv.enet(x, y, K = 10, lambda, s, mode,trace = FALSE, plot.it = TRUE, se = TRUE, ...)
Arguments
x
Input to lars
y
Input to lars
K
Number of folds
lambda
Quadratic penalty parameter
s
Abscissa values at which CV curve should be computed.
A value, or vector of values, indexing the path. Its values depends on the mode= argument
mode
Mode="step" means the s= argument indexes the LARS-EN step number. If mode="fraction", then s should be a number
between 0 and 1, and it refers to the ratio of the L1 norm of the
coefficient vector, relative to the norm at the full LS solution.
Mode="norm" means s refers to the L1 norm of the coefficient vector.
Abbreviations allowed. If mode="norm", then s should be the L1 norm of
the coefficient vector. If mode="penalty", then s should be the 1-norm
penalty parameter.
trace
Show computations?
plot.it
Plot it?
se
Include standard error bands?
...
Additional arguments to enet
Value
Invisibly returns a list with components (which can be plotted using plotCVLars)
fraction
Values of s
cv
The CV curve at each value of fraction
cv.error
The standard error of the CV curve
Author(s)
Hui Zou and Trevor Hastie
References
Zou and Hastie (2005) "Regularization and
Variable Selection via the Elastic Net"
Journal of the Royal Statistical Society, Series B,76,301-320.
Examples
data(diabetes)
attach(diabetes)
## use the L1 fraction norm as the tuning parameter
cv.enet(x2,y,lambda=0.05,s=seq(0,1,length=100),mode="fraction",trace=TRUE,max.steps=80)
## use the number of steps as the tuning parameter
cv.enet(x2,y,lambda=0.05,s=1:50,mode="step")
detach(diabetes)