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

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

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.

`predict.lmridge`

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

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.

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]*. Cule and De lorio, 2012.

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 residuals `residuals`

, ridge PRESS `press.lmridge`

# NOT RUN { 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])) # }