rliu {lrmest} | R Documentation |
Restricted Liu Estimator
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
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 |
r |
is a |
R |
is a |
delt |
values of |
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 |
na.action |
if the dataset contain |
... |
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)
See Also
Examples
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)