Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 |
formula |
in this section interested model should be given. This should be given as a |
r |
is a j by 1 matrix of linear restriction, r = Rβ + δ + ν. Values for |
R |
is a j by p of full row rank j ≤ p matrix of linear restriction, r = Rβ + δ + ν. Values for |
dpn |
dispersion matrix of vector of disturbances of linear restricted model, r = Rβ + δ + ν. Values for |
delt |
values of E(r) - Rβ and that should be given as either a |
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 |
na.action |
if the dataset contain |
... |
currently disregarded. |
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’.
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.
P.Wijekoon, A.Dissanayake
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## 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)
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