`ridgregextra`

focuses on finding the ridge parameter value k which makes the VIF values closest to 1 while keeping them above 1 as stressed “Applied Linear Statistical Models” (Kutner et al., 2004). The package includes the `ridgereg_k`

function, presents a system that automatically determines the k value in a certain range defined by the user and provides detailed ridge regression results. `ridgereg_k`

also provides ridge regression tables (VIF, MSE, R2, Beta, Stdbeta) using `vif_k`

function for k ridge parameter values generated between certain lower and upper bound values.

In addition, the `ridge_reg`

function provides users the ridge regression results for a manually entered k value. Finally `ridgregextra`

provides three sets of graphs consisting k versus VIF values, regression coefficents and standard errors of them.

`ridgregextra`

was presented for the first time in “Why R? Turkey 2022” conference.

`ridgregextra`

from CRAN`install.packages("ridgregextra")`

`ridgregextra`

development versionPlease make sure that you installed `devtools`

package.

If you would like to install dev version of the package, please use following command.

`devtools::install_github(filizkrdg/ridgregextra)`

You can use `isdals`

package to have example data to test `ridgregextra`

package. `isdals`

package is being installed, while you are installing `ridgregextra`

package, so you don’t have to install the package again.

- Prepare the dataset

```
library(isdals)
data(bodyfat)
x=bodyfat[,-1]
y=bodyfat[,1]
```

- Run
`ridgereg_k`

function to get coefficients by using alternative approach to traditional ridge regression techniques.

```
ridgereg_k(x,y,0,1)
```

You can use `mctest`

package to have example data to test `ridgregextra`

package. `mctest`

package is being installed, while you are installing `ridgregextra`

package, so you don’t have to install the package again.

- Prepare the dataset

```
library("mctest")
x=Hald[,-1]
y=Hald[,1]
```

- Run
`ridgereg_k`

function to get coefficients by using alternative approach to traditional ridge regression techniques.

`ridgereg_k(x,y,0,1)`

- Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W., Applied Linear Statistical Models, pp.430-440, 2004.
- Karadağ, F. and Sazak, H.S., “R Algorithm for Ridge Parameter Estimation in Ridge Regression” Why R? Turkey 2022 Conference, online, Verbal, Summary Text, p.13, 2022. (https://www.nobelyayin.com/why-r-turkiye-2022-konferansi-18447.html)

For any questions please contact:

- Filiz Karadag, filiz.karadag@ege.edu.tr
- Hakan Savas Sazak, hakan.savas.sazak@ege.edu.tr
- Olgun Aydin, olgun.aydin@pg.edu.pl