EU2019

<p>illustrative econometric data from Eurostat
for 5 variables in 17 countries in 2019
columns: Country, LifExp , HDP, Unempl, Obesity, APassangers
remove the first column: rownames(EU2019)= EU2019[,1]; EU2019=EU2019[,-1]
data has 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

EU2019 function

<p>An object of class <code>data.frame</code> with 17 rows and 6 columns.</p>

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illustrative econometric data from Eurostat
for 5 variables in 17 countries in 2019
columns: Country, LifExp , HDP, Unempl, Obesity, APassangers
remove the first column: rownames(EU2019)= EU2019[,1]; EU2019=EU2019[,-1]
data has real meaning — EU2019

illustrative econometric data from Eurostat
for 5 variables in 17 countries in 2019
columns: Country, LifExp , HDP, Unempl, Obesity, APassangers
remove the first column: rownames(EU2019)= EU2019[,1]; EU2019=EU2019[,-1]
data has real meaning

EU2019: illustrative econometric data from Eurostat for 5 variables in 17 countries in 2019 columns: Country, LifExp , HDP, Unempl, Obesity, APassangers remove the first column: rownames(EU2019)= EU2019[,1]; EU2019=EU2019[,-1] data has real meaning

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