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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