mahala | R Documentation |
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
library(metan)
library(dplyr)
# Compute the mean for genotypes
means <- mean_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)
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