<p>T1 contains simulated data without real meaning
the null hypothesis is not rejected in the first test
it illustrates the situation if full model is final model
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

T1 function

<p>An object of class <code>data.frame</code> with 16 rows and 13 columns.</p>

Format

Author

T1 contains simulated data without real meaning
the null hypothesis is not rejected in the first test
it illustrates the situation if full model is final model
data has no real meaning — T1

T1 contains simulated data without real meaning
the null hypothesis is not rejected in the first test
it illustrates the situation if full model is final model
data has no real meaning

T1: T1 contains simulated data without real meaning the null hypothesis is not rejected in the first test it illustrates the situation if full model is final model data has no real meaning

Description

Usage

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Format

Author

References