Description Usage Arguments Value Author(s) Examples
cross validation function for lars algorithm
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X |
the matrix (of size n*p) of the covariates. |
y |
a vector of length n with the response. |
nbFolds |
the number of folds for the cross-validation. |
index |
Values at which prediction error should be computed. This is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01). |
maxSteps |
Maximal number of steps for lars algorithm. |
eps |
Tolerance of the algorithm. |
A list containing
Mean prediction error for each value of index.
Standard error of cv.
Minimal cv criterion.
Value of lambda for which the cv criterion is minimal.
Values at which prediction error should be computed. This is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01).
Maximum number of steps of the lars algorithm.
Quentin Grimonprez
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