Description Usage Arguments Details Value References Examples
Hellwig's method selects a subset of independent variables in a linear regression model based on their correlations with some dependent variable as well as correlations between themselves. The goal is to select a subset of variables which are fairly independent from each other but highly correlated with the dependent variable.
1  hellwig(y, x, method = "pearson")

y 
numeric, dependent variable 
x 
numeric matrix, independent variables 
method 
character, type of correlation measures used, passed to

Given m independent variables Hellwig's method consists of evaluating all 2^m  1 combinations using the following steps:
Individual capacity of an independent variable in a subset is given by:
h_kj = r_0j^2 / sum_{i \in I} r_ij
where r_0j is correlation of jth independent variable with the dependent variable, r_ij is a correlation with ith and jth dependent variable, and I is a focal set of independent variables.
Integral capacity of information for every combination k is equal to:
H_k = sum_j h_kj
The subset with the highest value of H_k should be selected.
Data frame with two columns: k
combination of independent
variables in the form of xyz where x, y, z... are the indices of columns
in x
, and h
the capacity of the subset H_k.
TODO Add references
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