# mixe

0th

Percentile

##### Ordinary Mixed Regression Estimator

mixe can be used to obtain the Mixed Regression Estimated values and corresponding scalar Mean Square Error (MSE) value.

Keywords
~kwd1 , ~kwd2
##### Usage
mixe(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 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’.
dpn
dispersion matrix of vector of disturbances of linear restricted model, $r = R\beta + \delta + \nu$. Values for dpn should be given as either a vector (only the diagonal elements) 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 calculate the Mixed Regression Estimator the prior information are required. Therefore those prior information should be mentioned within the function.

##### Value

mixe returns the Mixed Regression Estimated values, standard error values, t statistic values,p value and corresponding scalar MSE value.

##### References

Theil, H. and Goldberger, A.S. (1961) On pure and mixed statistical estimation in economics in International Economic review, volume 2, pp. 65--78

• mixe
##### 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)
mixe(Y~X1+X2+X3+X4-1,r,R,dpn,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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