Description Usage Arguments Details Value Author(s) References See Also Examples
This function can be used to find the Restricted Ridge Regression 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 ‘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. |
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 rrre
returns the Restricted Ridge Regression 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 rrre
returns all the scalar MSE values and corresponding parameter values of Restricted Ridge Regression Estimator.
P.Wijekoon, A.Dissanayake
Sarkara, N. (1992), A new estimator combining the ridge regression and the restricted least squares methods of estimation in Communications in Statistics - Theory and Methods, volume 21, pp. 1987–2000. DOI:10.1080/03610929208830893
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## 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)
rrre(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 Restricted Ridge Regression 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(rrre(Y~X1+X2+X3+X4-1,r,R,dpn,delt,k,data=pcd),
main=c("Plot of MSE of Restricted Ridge Regression 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(rrre(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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