cvrelaxo: Cross validation for "Relaxed Lasso"
In relaxo: Relaxed Lasso

Description Usage Arguments Details Value Author(s) References See Also Examples

Compute the "Relaxed Lasso" solution with minimal cross-validated L2-loss.

1	cvrelaxo(X, Y, K = 5, phi = seq(0, 1, length = 10), max.steps = min( 2* length(Y), 2 * ncol(X)), fast = TRUE, keep.data = TRUE, warn=TRUE)

`X`	as in function `relaxo`
`Y`	as in function `relaxo`
`K`	Number of folds. Defaults to 5.
`phi`	as in function `relaxo`
`max.steps`	as in function `relaxo`
`fast`	as in function `relaxo`
`keep.data`	as in function `relaxo`
`warn`	as in function `relaxo`

The plot method is not useful for result of cvrelaxo (as no path of solutions exists).

An object of class relaxo, for which print and predict methods exist

Nicolai Meinshausen nicolai@stat.berkeley.edu

N. Meinshausen, "Relaxed Lasso", Computational Statistics and Data Analysis, to appear. http://www.stat.berkeley.edu/~nicolai

See also relaxo for computation of the entire solution path

	data(diabetes)

## Center and scale variables
   	x <- scale(diabetes$x)
	y <- scale(diabetes$y)
	
## Compute "Relaxed Lasso" solution and plot results
 	object <- relaxo(x,y)
	plot(object)   
	
## Compute cross-validated solution with optimal 
## predictive performance and print relaxation parameter phi and 
## penalty parameter lambda of the found solution
	cvobject <- cvrelaxo(x,y)
	print(cvobject$phi)
	print(cvobject$lambda)
	
## Compute fitted values and plot them versus actual values     
	fitted.values <- predict(cvobject)
	plot(fitted.values,y)
	abline(c(0,1))