press.lmridge | R Documentation |
The press.lmridge
function computes predicted residual sum of squares (PRESS) (see Allen, 1971).
press(object, ...) ## S3 method for class 'lmridge' press(object, ...)
object |
An object of class "lmridge". |
... |
Not presently used in this implementation. |
All of the n
leave-one-out predicted residual sum of squares is calculated by fitting full regression model by using, ∑(\hat{e}_{i,k})(1-(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.
The press.lmridge
produces a vector of PRESS or a matrix of PRESS for scalar or vector values of biasing parameter.
Muhammad Imdad Ullah, Muhammad Aslam
Allen, D. M. (1971). Mean Square Error of Prediction as a Criterion for Selecting Variables. Technometrics, 13, 469-475. doi: 10.1080/00401706.1971.10488811.
Allen, D. M. (1974). The Relationship between Variable Selection and Data Augmentation and Method for Prediction. Technometrics, 16, 125-127. doi: 10.1080/00401706.1974.10489157.
Hoerl, A. E., Kennard, R. W., and Baldwin, K. F. (1975). Ridge Regression: Some Simulation. Communication in Statistics, 4, 105-123. doi: 10.1080/03610927508827232.
Hoerl, A. E. and Kennard, R. W., (1970). Ridge Regression: Biased Estimation of Nonorthogonal Problems. Technometrics, 12, 55-67. doi: 10.1080/00401706.1970.10488634.
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
mod <- lmridge(y~., as.data.frame(Hald), K = seq(0, 0.5, 0.04)) press(mod)
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