The press.lmridge function computes predicted residual sum of squares (PRESS) (see Allen, 1971).
press(object, …)
# S3 method for lmridge
press(object, …)An object of class "lmridge".
Not presently used in this implementation.
The press.lmridge produces a vector of PRESS or a matrix of PRESS for scalar or vector values of biasing parameter.
All of the n leave-one-out predicted residual sum of squares is calculated by fitting full regression model by using, \(\sum\frac{\hat{e}_{i,k}}{1-\frac{1}{n}-H_{ii_{R,k}}}\), where \(H_{ii_{R,k}}\) is hat matrix from ridge model fit, \(\hat{e_{i,k}}\) is the ith residual at specific value of \(K\).
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.
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 residual residuals, ridge predicted value predict
# NOT RUN {
mod <- lmridge(y~., as.data.frame(Hald), K = seq(0, 0.5, 0.04))
press(mod)
# }
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