ridgeRegression: No intercept ridge regression, optimal lambda
In dajmcdon/cplr: Compressed and penalized linear regression
Description
Usage
Arguments
Value
Description
Computes the ridge regression estimate, optimizing over the tuning parameter
lambda using generalized cross validation (GCV)
Usage
1
ridgeRegression(X, Y, lam.upper = min(dim(X)))
Arguments
X
design matrix
Y
response vector
lam.upper
a vector with the smallest and largest lambda values to try
Value
A list containing th vector of coefficients at the GCV minimizing value of lambda (bhat) and the best value of lambda (lamStar).
dajmcdon/cplr documentation built on May 14, 2019, 3:29 p.m.
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Description Usage Arguments Value
Description
Computes the ridge regression estimate, optimizing over the tuning parameter lambda using generalized cross validation (GCV)
Usage
1 | ridgeRegression(X, Y, lam.upper = min(dim(X)))
|
Arguments
X |
design matrix |
Y |
response vector |
lam.upper |
a vector with the smallest and largest lambda values to try |
Value
A list containing th vector of coefficients at the GCV minimizing value of lambda (bhat) and the best value of lambda (lamStar).
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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