ogsrre

0th

Percentile

Ordinary Generalized Stochastic Restricted Ridge Estimator

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

Keywords
~kwd1, ~kwd2
Usage
ogsrre(formula, r, R, dpn, delt, k, 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, $r = R\beta + \delta + \nu$. Values for r should be given as either a vector or a matrix. See
R
is a $j$ by $p$ of full row rank $j \le p$ matrix of linear restriction, $r = R\beta + \delta + \nu$. 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, $r = R\beta + \delta + \nu$. Values for dpn should be given as either a vector (only the diagonal elements) or a matrix. See Examples
delt
values of $E(r) - R\beta$ and that should be given as either a vector or a matrix. See Examples.
k
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 k is a single numeric values then ogsrre returns the Ordinary Generalized Stochastic Restricted Ridge Estimated values, standard error values, t statistic values, p value and corresponding scalar MSE value. If k is a vector of set of numeric values then ogsrre returns all the scalar MSE values and corresponding parameter values of Ordinary Generalized Stochastic Restricted Ridge Estimator.

References

Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403--411 Revan, M. (2009) A stochastic restricted ridge regression estimator in Journal of Multivariate Analysis, volume 100, issue 8, pp. 1706--1716

See Also

plot

Aliases
  • ogsrre
Examples
## Portland cement data set is used.
data(pcd)
k<-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)
ogsrre(Y~X1+X2+X3+X4-1,r,R,dpn,delt,k,data=pcd)     
 # Model without the intercept is considered.

## To obtain variation of MSE of Ordinary Generalized Stochastic 
# Restricted Ridge Estimator.
data(pcd)
k<-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(ogsrre(Y~X1+X2+X3+X4-1,r,R,dpn,delt,k,data=pcd),
main=c("Plot of MSE of Ordinary Generalized Stochastic Restricted Ridge 
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(ogsrre(Y~X1+X2+X3+X4-1,r,R,dpn,delt,k,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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