Two plots from Ridge regression are generated: The MSE resulting from Generalized Cross Validation (GCV) versus the Ridge parameter lambda, and the regression coefficients versus lambda. The optimal choice for lambda is indicated.
plotRidge(formula, data, lambda = seq(0.5,50, by =0.05),...)
Arguments
formula: formula, like yX, i.e., dependentresponse variables
data: data frame to be analyzed
lambda: possible values for the Ridge parameter to evaluate
...: additional plot arguments
Details
For all values provided in lambda the results for Ridge regression are computed. The function lm.ridge is used for cross-validation and Ridge regression.
Returns
predicted: predicted values for the optimal lambda
lambdaopt: optimal Ridge parameter lambda from GCV
References
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.