Modified_Gilmour9p

<p>special illustrative data if more than two tests
are done in the loop in the function final_model()
original Gilmour table was modified
data has no real meaning</p>

data

Several methods may be found for selecting a subset of regressors from a set of k candidate variables in multiple linear regression.
One possibility is to evaluate all possible regression models and comparing them using Mallows's Cp statistic (Cp) according to Gilmour original study.
Full model is calculated, all possible combinations of regressors are generated, adjusted Cp for each submodel are computed, and the submodel with the minimum adjusted value Cp (ModelMin) is calculated.
To identify the final model, the package applies a sequence of hypothesis tests on submodels nested within ModelMin, following the approach outlined in Gilmour's original paper.
For more details see the help of the function final_model() and the original study (1996) <doi:10.2307/2348411>.

Josef Dolejs

gilmour

The Interpretation of Adjusted Cp Statistic

Modified_Gilmour9p function

<p>An object of class <code>data.frame</code> with 24 rows and 10 columns.</p>

Format

Author

special illustrative data if more than two tests
are done in the loop in the function final_model()
original Gilmour table was modified
data has no real meaning — Modified_Gilmour9p

special illustrative data if more than two tests
are done in the loop in the function final_model()
original Gilmour table was modified
data has no real meaning

Modified_Gilmour9p: special illustrative data if more than two tests are done in the loop in the function final_model() original Gilmour table was modified data has no real meaning

Description

Usage

Arguments

Format

Author

References