Description Usage Arguments Details Value Author(s) References See Also Examples
This function can be used to find the Ordinary Ridge Regression Estimated values and corresponding scalar Mean Square Error (MSE) value. Further the variation of MSE can be determined graphically.
1 |
formula |
in this section interested model should be given. This should be given as 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 rid
returns the Ordinary 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 rid
returns all the scalar MSE values and corresponding parameter values of Ordinary Ridge Regression Estimator.
P.Wijekoon, A.Dissanayake
Hoerl, A.E. and Kennard, R.W. (1970) Ridge Regression Biased estimation for non orthogonal problem, 12, pp.55–67.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Portland cement data set is used.
data(pcd)
k<-0.01
rid(Y~X1+X2+X3+X4-1,k,data=pcd) # Model without the intercept is considered.
## To obtain the variation of MSE of Ordinary Ridge Regression Estimator.
data(pcd)
k<-c(0:10/10)
plot(rid(Y~X1+X2+X3+X4-1,k,data=pcd),
main=c("Plot of MSE of Ordinary 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(rid(Y~X1+X2+X3+X4-1,k,data=pcd))
smse<-mseval[order(mseval[,2]),]
points(smse[1,],pch=16,cex=0.6)
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