rls
From lrmest v3.0
by Ajith Dissanayake
Restricted Least Square Estimator
This function can be used to find the Restricted Least Square Estimated values and corresponding scalar Mean Square Error (MSE) value.
Usage
rls(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 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’.  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 find the results of Restricted Least Square Estimator, prior information should be specified.
Value
rls
returns the Restricted Least Square Estimated values, standard error values, t statistic values,p value and corresponding scalar MSE value.
References
Hubert, M.H. and Wijekoon, P. (2006) Improvement of the Liu estimator in the linear regression medel, Chapter (48)
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)
rls(Y~X1+X2+X3+X41,r,R,delt,data=pcd) # Model without the intercept is considered.
Community examples
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