# ogliu

0th

Percentile

##### Ordinary Generalized Liu Estimator

ogliu can be used to find the Ordinary Generalized Liu Estimated values and corresponding scalar Mean Square Error (MSE) value in the linear model. Further the variation of MSE can be shown graphically.

Keywords
~kwd1, ~kwd2
##### Usage
ogliu(formula, d, data = NULL, na.action, ...)
##### Arguments
formula
in this section interested model should be given. This should be given as a formula.
d
a single numeric value or a vector of set of numeric values. See Example.
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 ogliu returns the Ordinary Generalized 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 ogliu returns all the scalar MSE values and corresponding parameter values of Ordinary Generalized Liu Estimator.

##### References

Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403--411 Liu, K. (1993) A new class of biased estimate in linear regression in Communications in Statistics-Theory and Methods 22, pp. 393--402.

plot

• ogliu
##### Examples
## Portland cement data set is used.
data(pcd)
d<-0.05
ogliu(Y~X1+X2+X3+X4-1,d,data=pcd)
# Model without the intercept is considered.

## To obtain the variation of MSE of Ordinary Generalized Liu Estimator.
data(pcd)
d<-c(0:10/10)
plot(ogliu(Y~X1+X2+X3+X4-1,d,data=pcd),main=c("Plot of MSE of
Ordinary Generalized 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(ogliu(Y~X1+X2+X3+X4-1,d,data=pcd))
smse<-mseval[order(mseval[,2]),]
points(smse[1,],pch=16,cex=0.6)
Documentation reproduced from package lrmest, version 3.0, License: GPL-2 | GPL-3

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