plotRidge function

Plot results of Ridge regression

Plot results of Ridge regression

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.

Author(s)

Peter Filzmoser P.Filzmoser@tuwien.ac.at

See Also

lm.ridge, plotRidge

Examples

data(PAC) res=plotRidge(y~X,data=PAC,lambda=seq(1,20,by=0.5))