README.md

In sametsoekel/eXtreme-Least-Squares: A Modeling Approach that Optimizes Future Errors in Least Squares

XLS

An R package that allows modeling with partial weighted regression to remove over effect in time series models that are heavily loaded on the lag variable and to fit the model in a healthy state.

Installation

You can install the released version of XLS from CRAN with:

install.packages("XLS")

Or install the development version of XLS from Github with:

devtools::install_github("sametsoekel/eXtreme-Least-Squares")

Example

Fit an eXtreme Least Squares Model:

library(XLS)

df <- datasets::airquality

ordered_df <- df[with(df,order(Month,Day)),]

model <- xls.fit(Ozone ~ Solar.R + Wind + Temp,na.omit(ordered_df),
error_weights = c(0.4,0.3,0.2,0.1),error_ahead_level = 4)

sametsoekel/eXtreme-Least-Squares documentation built on April 3, 2022, 12:08 a.m.