elasticnet (version 1.0-3)

cv.enet: 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

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

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)