Compute the "Relaxed Lasso" solution with minimal crossvalidated L2loss.
1 
X 
as in function 
Y 
as in function 
K 
Number of folds. Defaults to 5. 
phi 
as in function 
max.steps 
as in function 
fast 
as in function 
keep.data 
as in function 
warn 
as in function 
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
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 crossvalidated 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))

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