ogmix
From lrmest v3.0
by Ajith Dissanayake
Ordinary Generalized Mixed Regression Estimator
ogmix
can be used to obtain the Mixed Regression Estimated values and corresponding scalar Mean Square Error (MSE) value.
Usage
ogmix(formula, r, R, dpn, 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 avector
or amatrix
. 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 avector
or amatrix
. See ‘Examples’.  dpn

dispersion matrix of vector of disturbances of linear restricted model, $r = R\beta + \delta + \nu$. Values for
dpn
should be given as either avector
(only the diagonal elements) or amatrix
. See ‘Examples’.  delt

values of $E(r)  R\beta$ and that should be given as either a
vector
or amatrix
. 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 fromenvironment(formula)
, typically the environment from which the function is called.  na.action

if the dataset contain
NA
values, thenna.action
indicate what should happen to thoseNA
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 calculate the Ordinary Generalized Mixed Regression Estimator the prior information are required. Therefore those prior information should be mentioned within the function.
Value
ogmix
returns the Ordinary Generalized Mixed Regression 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. 403411
Theil, H. and Goldberger, A.S. (1961) On pure and mixed statistical estimation in economics in International Economic review, volume 2, pp. 6578
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)
dpn<c(0.0439,0.0029,0.0325)
delt<c(0,0,0)
ogmix(Y~X1+X2+X3+X41,r,R,dpn,delt,data=pcd)
# Model without the intercept is considered.
Community examples
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