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

## Description

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

## Usage

 `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) ```

## Arguments

 `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`

## Details

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

## Value

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

## Author(s)

Nicolai Meinshausen [email protected]

## References

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
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ``` 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)) ```