predict.lmridge: Predict method for Linear Ridge Model Fits

In lmridge: Linear Ridge Regression with Ridge Penalty and Ridge Statistics

Predict method for Linear Ridge Model Fits

Description

Predicted values based on linear ridge regression model for scalar or vector values of biasing parameter K.

Usage

## S3 method for class 'lmridge'
predict(object, newdata, na.action=na.pass, ...)

Arguments

object	An object of class "lmridge".
newdata	An optional data frame in which to look for variables with which to predict.
na.action	Function determine what should be done with missing values in newdata. The default is to predict NA.
...	Not presently used in this implementation.

Details

The predict.lmridge function produces predicted values, obtained by evaluating the regression function in the frame newdata which defaults to model.frame (object). If newdata is omitted the predictions are based on the data used for the fit. In that case how cases with missing values in the original fit are handled is determined by the na.action argument of that fit. If na.action = na.omit omitted cases will not appear in the predictions, whereas if na.action = na.exclude they will appear (in predictions), with value NA.

Value

predict.lmridge produces a vector of predictions or a matrix of predictions for scalar or vector values of biasing parameter.

Note

Variables are first looked for in newdata and then are searched for in the usual way (which will include the environment of the formula used in the fit). A warning will be given if the variables found are not of the same length as those in the newdata if it was supplied.

Author(s)

Muhammad Imdad Ullah, Muhammad Aslam

References

Cule, E. and De lorio, M. (2012). A semi-Automatic method to guide the choice of ridge parameter in ridge regression. arXiv:1205.0686v1 [stat.AP].

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.

See Also

The ridge model fitting lmridge, ridge residuals residuals, ridge PRESS press.lmridge

Examples

mod <- lmridge(y~., as.data.frame(Hald), K = seq(0, 0.2, 0.05))
predict(mod)
predict(mod, newdata = as.data.frame(Hald[1:5, -1]))

lmridge documentation built on Jan. 15, 2023, 5:06 p.m.