srliu: Stochastic Restricted Liu Estimator

In lrmest: Different Types of Estimators to Deal with Multicollinearity

Description

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

Usage

srliu(formula, r, R, dpn, 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’.
dpn	dispersion matrix of vector of disturbances of linear restricted model, r = Rβ + δ + ν. Values for dpn should be given as either a vector (only the diagonal elements) 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 srliu returns the Stochastic 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 srliu returns all the scalar MSE values and corresponding parameter values of Stochastic Resticted 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)

See Also

plot

Examples

## Portland cement data set is used.
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)
dpn<-c(0.0439,0.0029,0.0325)
delt<-c(0,0,0)
srliu(Y~X1+X2+X3+X4-1,r,R,dpn,delt,d,data=pcd)    
 # Model without the intercept is considered.

## To obtain the variation of MSE of Stochastic Restricted 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)
dpn<-c(0.0439,0.0029,0.0325)
delt<-c(0,0,0)
plot(srliu(Y~X1+X2+X3+X4-1,r,R,dpn,delt,d,data=pcd),
main=c("Plot of MSE of Stochastic 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(srliu(Y~X1+X2+X3+X4-1,r,R,dpn,delt,d,data=pcd))
smse<-mseval[order(mseval[,2]),]
points(smse[1,],pch=16,cex=0.6)

Example output

$`*****Stochastic Restricted Liu Estimator*****`
   Estimate Standard_error t_statistic pvalue
X1   2.1800         0.1158     11.7663 0.0000
X2   1.1563         0.0287     24.1193 0.0000
X3   0.7491         0.1002      4.6964 0.0011
X4   0.4883         0.0342          NA     NA

$`*****Mean square error value*****`
   MSE 
0.0257 

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