ols: Ordinary Least Square Estimators In lrmest: Different Types of Estimators to Deal with Multicollinearity

Description

`ols` can be used to calculate the values of Ordinary Least Square Estimated values and corresponding scaler Mean Square Error (MSE) value.

Usage

 `1` ```ols(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.

If there is any dependence present among the independent variables (multicollinearity) then it will be indicated as a warning massage. In case of multicollinearity Ordinary Least Square Estimators are not the best estimators.

Value

`ols` returns the Ordinary Least Square Estimated values, standard error values, t statistic values,p value and corresponding scalar MSE value. In addition if the dataset contains multicollinearity then it will be indicated as a warning massage.

Author(s)

P.Wijekoon, A.Dissanayake

References

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

`checkm`

Examples

 ```1 2 3``` ```## Portland cement data set is used. data(pcd) ols(Y~X1+X2+X3+X4-1,data=pcd) # Model without the intercept is considered. ```

Example output

```\$`*****Ordinary Least Square Estimator******`
Estimate Standard_error t_statistic p_value
X1   2.1930         0.1853     11.8367   0.000
X2   1.1533         0.0479     24.0565   0.000
X3   0.7585         0.1595      4.7551   0.001
X4   0.4863         0.0414     11.7443   0.000

\$`*****Mean square error value*****`
MSE
0.0638

Warning message:
In ols(Y ~ X1 + X2 + X3 + X4 - 1, data = pcd) :
There is a multicollinearity
```

lrmest documentation built on May 29, 2017, 9:02 a.m.