Description Usage Arguments Value Author(s) References Examples

Perform a (multi)collinearity diagnostic of a correlation matrix of predictor variables based several indicators, as shown by Olivoto et al. (2017).

1 |

`.data` |
The data to be analyzed. Must be a symmetric correlation matrix or a dataframe containing the predictor variables and one possible grouping variable. |

`group_var` |
A possible factor to group the data. If more than two factors are in the dataset, the extra factor column(s) will be excluded with a message. |

`n` |
If a correlation matrix is provided, then |

`verbose` |
If |

The following values are returned. Please, note that if a grouping variable is used, then the results are returned into a list.

`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 [email protected]

Olivoto, T., V.Q. Souza, M. Nardino, I.R. Carvalho, M. Ferrari, A.J. Pelegrin, V.J. Szareski, and D. Schmidt. 2017. Multicollinearity in path analysis: a simple method to reduce its effects. Agron. J. 109:131-142. doi:10.2134/agronj2016.04.0196. 10.2134/agronj2016.04.0196.

1 2 3 4 5 6 7 8 9 10 11 | ```
# Using the correlation matrix
cor_iris = cor(iris[,1:4])
n = nrow(iris)
colindiag(cor_iris, n = n, verbose = FALSE)
# Using the pipe operator %>%
library(dplyr)
cor_iris %>% colindiag(n = n, verbose = FALSE)
# Diagnostic by species, storing into an object
col_diag_spec = iris %>% colindiag(group_var = Species)
``` |

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