elasticnet (version 1.3)

# 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

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

## 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

```# NOT RUN {
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)
# }
```