# rliu: Restricted Liu Estimator In lrmest: Different Types of Estimators to Deal with Multicollinearity

## Description

This function can be used to find the Restricted Liu Estimated values and corresponding scalar Mean Square Error (MSE) value. Further the variation of MSE can be shown graphically.

## Usage

 `1` ```rliu(formula, r, R, delt, d, data = NULL, na.action, ...) ```

## Arguments

 `formula` in this section interested model should be given. This should be given as a `formula`. `r` is a j by 1 matrix of linear restriction, r = Rβ + δ + ν. Values for `r` should be given as either a `vector` or a `matrix`. See ‘Examples’. `R` is a j by p of full row rank j ≤ p matrix of linear restriction, r = Rβ + δ + ν. Values for `R` should be given as either a `vector` or a `matrix`. See ‘Examples’. `delt` values of E(r) - Rβ and that should be given as either a `vector` or a `matrix`. See ‘Examples’. `d` a single numeric value or a vector of set of numeric values. See ‘Examples’. `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 `plot` so as to obtain the variation of scalar MSE values graphically. See ‘Examples’.

## Value

If `d` is a single numeric values then `rliu` returns the Restricted Liu Estimated values, standard error values, t statistic values, p value and corresponding scalar MSE value.

If `d` is a vector of set of numeric values then `rliu` returns all the scalar MSE values and corresponding parameter values of Restricted Liu Estimator.

## Author(s)

P.Wijekoon, A.Dissanayake

## References

Hubert, M.H. and Wijekoon, P. (2006) Improvement of the Liu estimator in the linear regression medel, Chapter (4-8)

`plot`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```data(pcd) d<-0.05 r<-c(2.1930,1.1533,0.75850) R<-c(1,0,0,0,0,1,0,0,0,0,1,0) delt<-c(0,0,0) rliu(Y~X1+X2+X3+X4-1,r,R,delt,d,data=pcd) # Model without the intercept is considered. ## To obtain the variation of MSE of Resticted Liu Estimator. data(pcd) d<-c(0:10/10) r<-c(2.1930,1.1533,0.75850) R<-c(1,0,0,0,0,1,0,0,0,0,1,0) delt<-c(0,0,0) plot(rliu(Y~X1+X2+X3+X4-1,r,R,delt,d,data=pcd), main=c("Plot of MSE of Restricted Liu Estimator"),type="b", cex.lab=0.6,adj=1,cex.axis=0.6,cex.main=1,las=1,lty=3,cex=0.6) mseval<-data.frame(rliu(Y~X1+X2+X3+X4-1,r,R,delt,d,data=pcd)) smse<-mseval[order(mseval[,2]),] points(smse[1,],pch=16,cex=0.6) ```

### Example output

```\$`*****Restricted Liu Estimator*****`
Estimate Standard_error t_statistic pvalue
X1   2.1800         0.0000     11.7661 0.0000
X2   1.1563         0.0000     24.1189 0.0000
X3   0.7491         0.0000      4.6963 0.0011
X4   0.4883         0.0197          NA     NA

\$`*****Mean square error value*****`
MSE
7e-04

Warning messages:
1: In model.matrix.default(md, cal, contrasts) :
non-list contrasts argument ignored
2: In model.matrix.default(md, cal, contrasts) :
non-list contrasts argument ignored
There were 12 warnings (use warnings() to see them)
There were 12 warnings (use warnings() to see them)
```

lrmest documentation built on May 1, 2019, 6:29 p.m.