Perform a (multi)collinearity diagnostic of a correlation matrix of predictor variables using several indicators, as shown by Olivoto et al. (2017).
colindiag(.data, ..., by = NULL, n = NULL)
The data to be analyzed. It must be a symmetric correlation
matrix, or a data frame, possible with grouped data passed from
Variables to use in the correlation. If
One variable (factor) to compute the function by. It is a shortcut
If a correlation matrix is provided, then
.data is a grouped data passed from
then the results will be returned into a list-column of data frames.
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.
ncorhigh Number of correlation greather than |0.8|.
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 firstname.lastname@example.org
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
# Using the correlation matrix library(metan) cor_iris <- cor(iris[,1:4]) n <- nrow(iris) col_diag <- colindiag(cor_iris, n = n) # Using a data frame col_diag_gen <- data_ge2 %>% group_by(GEN) %>% colindiag() # Diagnostic by levels of a factor # For variables with "N" in variable name col_diag_gen <- data_ge2 %>% group_by(GEN) %>% colindiag(contains("N"))
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