# ogrls

0th

Percentile

##### Ordinary Generalized Restricted Least Square Estimator

This function can be used to find the Ordinary Generalized Restricted Least Square Estimated values and corresponding scalar Mean Square Error (MSE) value.

Keywords
~kwd1 , ~kwd2
##### Usage
ogrls(formula, r, R, delt, data, na.action, ...)
##### Arguments
formula
in this section interested model should be given. This should be given as a formula.
r
is a $j$ by $1$ matrix of linear restriction, $r = R\beta + \delta + \nu$. Values for r should be given as either a vector or a matrix. See ‘Examples’.
R
is a $j$ by $p$ of full row rank $j \le p$ matrix of linear restriction, $r = R\beta + \delta + \nu$. Values for R should be given as either a vector or a matrix. See ‘Examples’.
delt
values of $E(r) - R\beta$ and that should be given as either a vector or a matrix. 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.
##### Details

Since formula has an implied intercept term, use either y ~ x - 1 or y ~ 0 + x to remove the intercept.

In order to find the results of Ordinary Generalized Restricted Least Square Estimator, prior information should be specified.

##### Value

ogrls returns the Ordinary Generalized Restricted Least Square Estimated values, standard error values, t statistic values,p value and corresponding scalar MSE value.

##### References

Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403--411

Hubert, M.H. and Wijekoon, P. (2006) Improvement of the Liu estimator in the linear regression medel, Chapter (4-8)

• ogrls
##### Examples
## Portland cement data set is used.
data(pcd)
r<-c(2.1930,1.1533,0.75850)
R<-c(1,0,0,0,0,1,0,0,0,0,1,0)
delt<-c(0,0,0)
ogrls(Y~X1+X2+X3+X4-1,r,R,delt,data=pcd)
# Model without the intercept is considered.

Documentation reproduced from package lrmest, version 3.0, License: GPL-2 | GPL-3

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