# ogols

0th

Percentile

##### 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.

Keywords
~kwd1 , ~kwd2
##### 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 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.

##### 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.

• ogols
##### Examples
## Portland cement data set is used.
data(pcd)
ogols(Y~X1+X2+X3+X4-1,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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