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)
.means | A matrix of data with, say, p columns. |
---|---|
covar | The covariance matrix. |
inverted | Logical argument. If |
A symmetric matrix with the Mahalanobis' distance.
Tiago Olivoto tiagoolivoto@gmail.com
# \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)# }