| residuals.lmridge {lmridge} | R Documentation |
Ridge Regression Residuals
Description
The residuals function computes the ridge residuals for scalar or vector value of biasing parameter K.
Usage
## S3 method for class 'lmridge'
residuals(object, ...)Arguments
object |
An object of class "lmridge". |
... |
Not presently used in this implementation. |
Details
The generic functions residuals can be used to compute residuals object of linear ridge regression from lmridge function.
Value
Returns a vector or a matrix of ridge residuals for scalar or vector value biasing parameter K provided as argument to lmridge function.
Author(s)
Muhammad Imdad Ullah, Muhammad Aslam
References
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. 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.
Lee, W. F. (1979). Model Estimation Using Ridge Regression with the Variance Normalization Criterion. Master thesis, Department of Educational Foundation, Memorial University of Newfoundland.
See Also
The ridge mode fitting lmridge, ridge prediction predict, ridge PRESS values press
Examples
mod <- lmridge(y~., as.data.frame(Hald), K = seq(0, 1, 0.2))
residuals(mod)