ogols
From lrmest v3.0
by Ajith Dissanayake
Ordinary Generalized Ordinary Least Square Estimators
ogols
can be used to calculate the values of Ordinary Generalized Ordinary Least Square Estimated values and corresponding scaler Mean Square Error (MSE) value.
Usage
ogols(formula, data, na.action, ...)
Arguments
 formula

in this section interested model should be given. This should be given as a
formula
.  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.
Value
ogols
returns the Ordinary Generalized Ordinary 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. 403411
Nagler, J. (Updated 2011) Notes on Ordinary Least Square Estimators.
Examples
## Portland cement data set is used.
data(pcd)
ogols(Y~X1+X2+X3+X41,data=pcd)
# Model without the intercept is considered.
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