ols
From lrmest v3.0
by Ajith Dissanayake
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.
Usage
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 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.
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.
References
Nagler, J. (Updated 2011) Notes on Ordinary Least Square Estimators.
See Also
Examples
## Portland cement data set is used.
data(pcd)
ols(Y~X1+X2+X3+X41,data=pcd) # Model without the intercept is considered.
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