relaxo: Relaxed Lasso (relaxo)
In relaxo: Relaxed Lasso

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

Computes all "Relaxed Lasso" solutions.

1	relaxo(X, Y, phi = seq(0, 1, length = 4), max.steps = min(2length(Y), 2 ncol(X)), fast = TRUE, keep.data = TRUE, warn=TRUE)

`X`	n x p-dimensional matrix or data frame containing the predictor variables; columns are supposed to be scaled and centered.
`Y`	n-dimensional numerical response vector; supposed to be centered to mean 0.
`phi`	Relaxation parameter in [0,1]. A value of phi=1 corresponds to the regular Lasso solutions; a value of phi=0 computes the OLS estimates on the set of variables selected by the Lasso.
`max.steps`	Maximal number of steps the LARS algorithm is run.
`fast`	Should the estimates be computed in approx. the same time as the LARS algorithm? If fast=TRUE, minor deviations from the original Relaxed Lasso solution can occur.
`keep.data`	Should the data be kept for later usage e.g. (when computing predicted values for the training data) ?
`warn`	If TRUE, warnings are given if the predictor variables X are not centered and scaled or if the reponse variable is not centered) ?

An object of class relaxo, for which plot and predict methods are available.

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 cvrelaxo for computation of the cross-validated solution with optimal predictive performance

	data(diabetes)

## Center and scale variables
   	x <- scale(diabetes$x)
	y <- as.numeric(scale(diabetes$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))