Panel of three ridge related plots, df trace vs \(K\), RSS vs \(K\) and PRESS vs \(K\) for graphical judgement of optimal value of \(K\).
rplots.plot(x, abline = TRUE, …)An object of class "lmridge"
Vertical line to show minimum value of ridge PRESS at cartain value of biasing parameter \(K\) on PRESS vs \(K\) plot.
Not presently used in this implementation.
nothing
Function rplots.plot can be used to plot the values of df vs \(K\), RSS vs \(K\) and PRESS vs \(K\) for scalar or vector values of biasing parameter \(K\). If no argument is used then a vertical line will be drawn on ridge PRESS plot to show the minimum value of PRESS at certain \(K\). The panel of these three plots can be helful in selecting the optimal value of biasing parameter \(K\).
Allen, D. M. (1971). Mean Square Error of Prediction as a Criterion for Selecting Variables. Technometrics, 13, 469-475. Allen, 1971.
Allen, D. M. (1974). The Relationship between Variable Selection and Data Augmentation and Method for Prediction. Technometrics, 16, 125-127. Allen, 1974.
Berk, R. (2008). Statistical Learning from a Regression Perspective. Springer.
Hoerl, A. E., Kennard, R. W., and Baldwin, K. F. (1975). Ridge Regression: Some Simulation. Communication in Statistics, 4, 105-123. Hoer et al., 1975.
Hoerl, A. E. and Kennard, R. W., (1970). Ridge Regression: Biased Estimation of Nonorthogonal Problems. Technometrics, 12, 55-67. Hoerl and Kennard, 1970.
Imdad, M. U. Addressing Linear Regression Models with Correlated Regressors: Some Package Development in R (Doctoral Thesis, Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan), 2017.
The ridge model fitting lmridge, ridge CV and GCV plots cv.plot, variance bias trade-off plot bias.plot, m-scale and isrm plots isrm.plot, ridge AIC and BIC plots info.plot, ridge and VIF trace plot.lmridge
# NOT RUN {
mod <- lmridge(y~., as.data.frame(Hald), K = seq(0, 0.2, 0.005))
rplots.plot(mod)
rplots.plot(mod, abline = FALSE)
# }
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