| 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))