ogmix
can be used to obtain the Mixed Regression Estimated values and corresponding scalar Mean Square Error (MSE) value.
1 |
formula |
in this section interested model should be given. This should be given as a |
r |
is a j by 1 matrix of linear restriction, r = Rβ + δ + ν. Values for |
R |
is a j by p of full row rank j ≤ p matrix of linear restriction, r = Rβ + δ + ν. Values for |
dpn |
dispersion matrix of vector of disturbances of linear restricted model, r = Rβ + δ + ν. Values for |
delt |
values of E(r) - Rβ and that should be given as either a |
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.
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.
ogmix
returns the Ordinary Generalized Mixed Regression Estimated values, standard error values, t statistic values,p value and corresponding scalar MSE value.
P.Wijekoon, A.Dissanayake
Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403–411
Theil, H. and Goldberger, A.S. (1961) On pure and mixed statistical estimation in economics in International Economic review, volume 2, pp. 65–78
1 2 3 4 5 6 7 8 |
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.