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lrmest
View on CRAN: Click
here
Download and install lrmest package within the R console
Install from CRAN:
install.packages("lrmest")
Install from Github:
library("remotes")
install_github("cran/lrmest")
Install by package version:
library("remotes")
install_version("lrmest", "3.0")
Attach the package and use:
library("lrmest")
Maintained by
Ajith Dissanayake
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
First Published: 2013-08-31
Latest Update: 2016-05-14
Description:
When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. To deal with multicollinearity several estimators are proposed in the literature. Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Generalized Ordinary Least Square Estimator (OGOLSE), Ordinary Ridge Regression Estimator (ORRE), Ordinary Generalized Ridge Regression Estimator (OGRRE), Restricted Least Square Estimator (RLSE), Ordinary Generalized Restricted Least Square Estimator (OGRLSE), Ordinary Mixed Regression Estimator (OMRE), Ordinary Generalized Mixed Regression Estimator (OGMRE), Liu Estimator (LE), Ordinary Generalized Liu Estimator (OGLE), Restricted Liu Estimator (RLE), Ordinary Generalized Restricted Liu Estimator (OGRLE), Stochastic Restricted Liu Estimator (SRLE), Ordinary Generalized Stochastic Restricted Liu Estimator (OGSRLE), Type (1),(2),(3) Liu Estimator (Type-1,2,3 LTE), Ordinary Generalized Type (1),(2),(3) Liu Estimator (Type-1,2,3 OGLTE), Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 ALTE), Ordinary Generalized Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 OGALTE), Almost Unbiased Ridge Estimator (AURE), Ordinary Generalized Almost Unbiased Ridge Estimator (OGAURE), Almost Unbiased Liu Estimator (AULE), Ordinary Generalized Almost Unbiased Liu Estimator (OGAULE), Stochastic Restricted Ridge Estimator (SRRE), Ordinary Generalized Stochastic Restricted Ridge Estimator (OGSRRE), Restricted Ridge Regression Estimator (RRRE) and Ordinary Generalized Restricted Ridge Regression Estimator (OGRRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically.
How to cite:
Ajith Dissanayake (2013). lrmest: Different Types of Estimators to Deal with Multicollinearity. R package version 3.0, https://cran.r-project.org/web/packages/lrmest. Accessed 06 Apr. 2025.
Other packages that cited lrmest R package
View lrmest citation profile
Other R packages that lrmest depends,
imports, suggests or enhances
Complete documentation for lrmest
Functions, R codes and Examples using
the lrmest R package
Some associated functions: alte1 . alte2 . alte3 . aul . aur . checkm . liu . lrmest2-package . lte1 . lte2 . lte3 . mixe . ogalt1 . ogalt2 . ogalt3 . ogaul . ogaur . ogliu . oglt1 . oglt2 . oglt3 . ogmix . ogols . ogre . ogrliu . ogrls . ogrrre . ogsrliu . ogsrre . ols . optimum . pcd . rid . rliu . rls . rrre . srliu . srre .
Some associated R codes: goeed.R . optimum.R . Full lrmest package functions and examples
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