Cross validation for "Relaxed Lasso"

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Description

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

Usage

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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 nicolai@stat.berkeley.edu

References

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

See Also

See also relaxo for computation of the entire solution path

Examples

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	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))