Compute the Mahalanobis distance of all pairwise rows in .means. The result is a symmetric matrix containing the distances that may be used for hierarchical clustering.

mahala(.means, covar, inverted = FALSE)

Arguments

.means

A matrix of data with, say, p columns.

covar

The covariance matrix.

inverted

Logical argument. If TRUE, covar is supposed to contain the inverse of the covariance matrix.

Value

A symmetric matrix with the Mahalanobis' distance.

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{ library(metan) library(dplyr) # Compute the mean for genotypes means <- means_by(data_ge, GEN) %>% column_to_rownames("GEN") # Compute the covariance matrix covmat <- cov(means) # Compute the distance dist <- mahala(means, covmat) # Dendrogram dend <- dist %>% as.dist() %>% hclust() %>% as.dendrogram() plot(dend)
# }