cv.rlasso: rLASSO using path algorithm, where an optimal tuning...
In KenLi93/PPWLSE: Penalized Precision-Weighted Least Square Estimators
Description
rLASSO using path algorithm, where an optimal tuning parameter is found by
cross validation
Usage
1
Arguments
X
design matrix
Y
outcome vector
lambda
a vector of tuning parameters. If left unspecified, using the
sequence of numbers from 0 to the smallest tuning parameter such that the
solution is the zero vector
nlambda
the number of the sequence of the tuning parameters.
nfolds
number of folds in the cross-validation
KenLi93/PPWLSE documentation built on Dec. 18, 2021, 3:32 a.m.
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Description
rLASSO using path algorithm, where an optimal tuning parameter is found by cross validation
Usage
1 |
Arguments
X |
design matrix |
Y |
outcome vector |
lambda |
a vector of tuning parameters. If left unspecified, using the sequence of numbers from 0 to the smallest tuning parameter such that the solution is the zero vector |
nlambda |
the number of the sequence of the tuning parameters. |
nfolds |
number of folds in the cross-validation |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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