Ordinary Least Square Estimators
ols can be used to calculate the values of Ordinary Least Square Estimated values and corresponding scaler Mean Square Error (MSE) value.
ols(formula, data, na.action, ...)
in this section interested model should be given. This should be given as a
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.
if the dataset contain
na.actionindicate what should happen to those
- currently disregarded.
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.
olsreturns 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.
Nagler, J. (Updated 2011) Notes on Ordinary Least Square Estimators.
## Portland cement data set is used. data(pcd) ols(Y~X1+X2+X3+X4-1,data=pcd) # Model without the intercept is considered.