# info.plot: Model Selection Criteria Plots In lmridge: Linear Ridge Regression with Ridge Penalty and Ridge Statistics

## Description

Plot of ridge AIC and BIC model selection criteria against ridge degrees of freedom (see Akaike, 1974 <https://doi.org/10.1109/TAC.1974.1100705>; Imdad, 2017 and Schwarz, 1978 <https://doi.org/10.1214/aos/1176344136>).

## Usage

 `1` ```info.plot(x, abline = TRUE, ...) ```

## Arguments

 `x` An object of class "lmridge". `abline` Vertical line to show minimum value of ridge MSE at certain value of ridge degrees of freedom. `...` Not presently used in this implementation.

## Details

Plot of ridge AIC and BIC against ridge degress of freedom ∑_{j=1}^p (λ_j)/(λ_j+k). A vertical line represents the minimum ridge MSE at certain value of ridge df.

Nothing returned

## Author(s)

Muhammad Imdad Ullah, Muhammad Aslam

## References

Akaike, H. (1974). A new look at the Statistical Model Identification. IEEE Transaction on Automatic Control, 9(6), 716–723. Akaike, 1974.

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.

Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6(2), 461–464. Schwarz, 1978.

## See Also

The ridge model fitting `lmridge`, ridge CV and GCV plot`cv.plot`, variance biase trade-off plot `bias.plot`, m-scale and isrm plots `isrm.plot`, ridge and VIF trace `plot.lmridge`, miscellaneous ridge plots `rplots.plot`

## Examples

 ```1 2 3 4 5 6``` ```mod <- lmridge(y~., as.data.frame(Hald), K = seq(0, 0.15, 0.002)) ## for indication vertical line (df ridge) info.plot(mod) ## without vertical line set \code{abline = FALSE} info.plot(mod, abline = FALSE) ```

### Example output  ```
```

lmridge documentation built on May 2, 2019, 5:57 a.m.