Ordinary Generalized Ordinary Least Square Estimators

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Description

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

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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 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.

Value

ogols returns the Ordinary Generalized Ordinary Least Square Estimated values, standard error values, t statistic values,p value and corresponding scalar MSE value.

Author(s)

P.Wijekoon, A.Dissanayake

References

Arumairajan, S. and Wijekoon, P. (2015) ] Optimal Generalized Biased Estimator in Linear Regression Model in Open Journal of Statistics, pp. 403–411

Nagler, J. (Updated 2011) Notes on Ordinary Least Square Estimators.

Examples

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## Portland cement data set is used.
data(pcd)
ogols(Y~X1+X2+X3+X4-1,data=pcd)     
# Model without the intercept is considered.

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