| Title: | S-Type Estimators |
|---|---|
| Description: | 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. |
| Authors: | Hakan Savas Sazak [aut] (ORCID: <https://orcid.org/0000-0001-6123-1214>), Filiz Karadag [cre] (ORCID: <https://orcid.org/0000-0002-0116-7772>), Olgun Aydin [aut] (ORCID: <https://orcid.org/0000-0002-7090-0931>) |
| Maintainer: | Filiz Karadag <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.0 |
| Built: | 2026-05-26 07:56:08 UTC |
| Source: | https://github.com/filizkrdg/s-type.est |
This function fits a regression model using the S-type estimators.
regstype(y, x)regstype(y, x)
y |
Dependent variables (Dataframe, vector). |
x |
Explanatory variables (Dataframe, matrix). |
A list containing the model coefficients and diagnostics.
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) regstype(y,x)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) regstype(y,x)
This function performs weighted regression analysis.
regweighteds(y, x, W)regweighteds(y, x, W)
y |
Dependent variables (Dataframe, vector) |
x |
Explanatory variables (Dataframe, matrix) |
W |
A numeric vector of weights. |
A list containing the regression model results.
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) W=runif(111, min = 0, max = 1) regweighteds(y,x,W)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) W=runif(111, min = 0, max = 1) regweighteds(y,x,W)