Description Usage Arguments Value Author(s) Examples
Perform a (multi)collinearity diagnosis of a correlation matrix of predictor variables based on the analysis of eigenvalues and eigenvectors.
1 |
x |
The data to be analyzed. Must be a symmetric correlation matrix or a dataframe containing the predictor variables |
n |
If a correlation matrix is the data input, thus |
cormat |
A symmetric Pearson's coefficient correlation matrix between the variables |
corlist |
A hypothesis testing for each of the correlation coefficients |
evalevet |
The eigenvalues with associated eigenvectors of the correlation matrix |
VIF |
The Variance Inflation Factors, being the diagonal elements of the inverse of the correlation matrix. |
CN |
The Condition Number of the correlation matrix, given by the ratio between the largest and smallest eigenvalue. |
det |
The determinant of the correlation matrix. |
largest_corr |
The largest correlation (in absolute value) observed. |
smallest_corr |
The smallest correlation (in absolute value) observed. |
weight_var |
The variables with largest eigenvector (largest weight) in the eigenvalue of smallest value, sorted in decreasing order. |
Tiago Olivoto tiagoolivoto@gmail.com
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