ogaur

0th

Percentile

Ordinary Generalized Almost Unbiased Ridge Estimator

ogaur can be used to find the Ordinary Generalized 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.

Keywords
~kwd1, ~kwd2
Usage
ogaur(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 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 obtained the variation of scalar MSE values graphically. See Examples.

Value

  • If k is a single numeric values then ogaur returns the Ordinary Generalized 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 ogaur returns all the scalar MSE values and corresponding parameter values of Ordinary Generalized Almost Unbiased 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 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

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

## To obtain the variation of MSE of 
# Ordinary Generalized Almost Unbiased Ridge Estimator.
data(pcd)
k<-c(0:10/10)
plot(ogaur(Y~X1+X2+X3+X4-1,k,data=pcd),
main=c("Plot of MSE of Ordinary Generalized 
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(ogaur(Y~X1+X2+X3+X4-1,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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