S-Type Estimators

Implements the S-type estimators, novel robust estimators for general linear regression models, addressing challenges such as outlier contamination and leverage points. This package introduces robust regression techniques to provide a robust alternative to classical methods and includes diagnostic tools for assessing model fit and performance. The methodology is based on the study, "Comparison of the Robust Methods in the General Linear Regression Model" by Sazak and Mutlu (2023). This package is designed for statisticians and applied researchers seeking advanced tools for robust regression analysis.

S-type.est: An R package for the S-type estimators

This R package provides an implementation of the S-type.est, a robust estimation method designed for the general linear regression models. The S-type estimators were introduced by Sazak and Mutlu (2023) in their study, "Comparison of the Robust Methods in the General Linear Regression Model."

Features

• Performs robust linear regression using the S-type estimators.
• Minimizes the MSE of the model.
• Minimizes the influence of outliers and leverage points.
• Diagnostic tools for model performance assessment.
• Enhanced efficiency and adaptability for data sets with challenging characteristics.

Installation

To install the package from GitHub, use the following command:

Installing S-type.est development version

Please make sure that you installed devtools package first:

install.packages("devtools")

Install the package

devtools::install_github("filizkrgd/S-type.est")

Installing S-type.est from CRAN

install.packages("S-type.est")

Installing S-type.est development version

Example usage of the package.

You can use datasets package to have example data to test S-type.est package. datasets package is being installed, while you are installing S-type.est package, so you don't have to install the package again.

• Prepare the dataset

library(datasets)
data(airquality)
str(airquality)
cleanairquality=na.omit(airquality)
Y1=cleanairquality$Ozone
X1=cleanairquality$Temp
X2=cleanairquality$Wind
X3=cleanairquality$Solar.R
x=data.frame("X1"=X1,"X2"=X2,"X3"=X3)
y=data.frame("Y"=Y1)

• Run regstype function to get regression results using the S-type estimators.

regsx=regstype(y,x)

References

• Sazak, M. and Mutlu, B. (2023). Comparison of the Robust Methods in the General Linear Regression Model.

Contact

For any questions please contact:

• Hakan Savas Sazak, [email protected]
• Filiz Karadag, [email protected]
• Olgun Aydin, [email protected]