cvrelaxo {relaxo} | R Documentation |
Compute the "Relaxed Lasso" solution with minimal cross-validated L2-loss.
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))