# lte3: Type (3) Liu Estimator In lrmest: Different Types of Estimators to Deal with Multicollinearity

## Description

This function can be used to find the Type (3) Liu Estimated values, corresponding scalar Mean Square Error (MSE) value and Prediction Sum of Square (PRESS) value in the linear model. Further the variation of MSE and PRESS values can be shown graphically.

## Usage

 1 lte3(formula, k, d, press = FALSE, data = NULL, na.action, ...)

## Arguments

 formula in this section interested model should be given. This should be given as a formula. k a single numeric value or a vector of set of numeric values. See ‘Examples’. d a single numeric value or a vector of set of numeric values. See ‘Examples’. press if “press=TRUE” then all the PRESS values and its corresponding parameter values are returned. Otherwise all the scalar MSE values and its corresponding parameter values are returned. data an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called. na.action if the dataset contain NA values, then na.action indicate what should happen to those NA values. ... currently disregarded.

## Details

Since formula has an implied intercept term, use either y ~ x - 1 or y ~ 0 + x to remove the intercept.

Use matplot so as to obtain the variation of scalar MSE values and PRESS values graphically. See ‘Examples’.

## Value

If k and d are single numeric values then lte3 returns the Type (3) Liu Estimated values, standard error values, t statistic values, p value, corresponding scalar MSE value and PRESS value.

If k and d are vector of set of numeric values then lte3 returns the matrix of scalar MSE values and if “press=TRUE” then lte3 returns the matrix of PRESS values of Type (3) Liu Estimator by representing k and d as column names and row names respectively.

## Author(s)

P.Wijekoon, A.Dissanayake

## References

Rong,Jian-Ying (2010) Adjustive Liu Type Estimators in linear regression models in communication in statistics-simulation and computation, volume 39 DOI:10.1080/03610918.2010.484120