lrmest (version 3.0)

aur: Almost Unbiased Ridge Estimator

Description

aur can be used to find the Almost Unbiased Ridge Estimated values and corresponding scalar Mean Square Error (MSE) value in the linear model. Further the variation of MSE can be shown graphically.

Usage

aur(formula, k, data = NULL, na.action, ...)

Arguments

formula

in this section interested model should be given. This should be given as a formula.

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 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.

Value

• If k is a single numeric values then aur returns the Almost Unbiased 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 aur returns all the scalar MSE values and corresponding parameter values of Almost Unbiased Ridge Estimator.

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 obtained the variation of scalar MSE values graphically. See Examples.

References

Akdeniz, F. and Erol, H. (2003) Mean Squared Error Matrix Comparisons of Some Biased Estimators in Linear Regression in Communications in Statistics - Theory and Methods, volume 32 DOI:10.1081/STA-120025385

See Also

plot

Examples

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

## To obtain the variation of MSE of Almost Unbiased Ridge Estimator.
data(pcd)
k<-c(0:10/10)
plot(aur(Y~X1+X2+X3+X4-1,k,data=pcd),
main=c("Plot of MSE of Almost Unbiased 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(aur(Y~X1+X2+X3+X4-1,k,data=pcd))
smse<-mseval[order(mseval[,2]),]
points(smse[1,],pch=16,cex=0.6)