Arguments
- x
A CompositionMatrix object.
Value
A matrix
.
References
Aitchison, J. (1986). The Statistical Analysis of Compositional Data. London: Chapman and Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .
Greenacre, M. J. (2019). Compositional Data Analysis in Practice. Boca Raton: CRC Press.
See also
Other statistics:
aggregate()
,
dist()
,
mahalanobis()
,
mean()
,
variation()
Examples
## Coerce to compositional data
data("hongite")
coda <- as_composition(hongite)
## Variance matrix
var(coda)
#> A B C D E
#> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604
#> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135
#> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375
#> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579
#> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000
## Covariance matrix
cov(coda)
#> A_B A_C A_D A_E B_C B_D
#> A_B 0.25927416 -0.6042738 -0.10259133 -0.12558948 -0.8635479 -0.36186549
#> A_C -0.60427379 1.5328586 0.25211090 0.36189072 2.1371324 0.85638469
#> A_D -0.10259133 0.2521109 0.08281464 0.01715854 0.3547022 0.18540597
#> A_E -0.12558948 0.3618907 0.01715854 0.13856035 0.4874802 0.14274802
#> B_C -0.86354795 2.1371324 0.35470223 0.48748020 3.0006804 1.21825018
#> B_D -0.36186549 0.8563847 0.18540597 0.14274802 1.2182502 0.54727146
#> B_E -0.38486364 0.9661645 0.11974987 0.26414983 1.3510281 0.50461351
#> C_D 0.50168246 -1.2807477 -0.16929626 -0.34473217 -1.7824302 -0.67097871
#> C_E 0.47868431 -1.1709679 -0.23495236 -0.22333036 -1.6496522 -0.71363666
#> D_E -0.02299815 0.1097798 -0.06565610 0.12140181 0.1327780 -0.04265795
#> B_E C_D C_E D_E
#> A_B -0.3848636 0.5016825 0.4786843 -0.02299815
#> A_C 0.9661645 -1.2807477 -1.1709679 0.10977982
#> A_D 0.1197499 -0.1692963 -0.2349524 -0.06565610
#> A_E 0.2641498 -0.3447322 -0.2233304 0.12140181
#> B_C 1.3510281 -1.7824302 -1.6496522 0.13277797
#> B_D 0.5046135 -0.6709787 -0.7136367 -0.04265795
#> B_E 0.6490135 -0.8464146 -0.7020147 0.14439996
#> C_D -0.8464146 1.1114515 0.9360155 -0.17543592
#> C_E -0.7020147 0.9360155 0.9476375 0.01162200
#> D_E 0.1444000 -0.1754359 0.0116220 0.18705791
## Variation matrix
variation(coda)
#> A B C D E
#> A 0.0000000 0.2592742 1.53285862 0.08281464 0.1385604
#> B 0.8011244 0.0000000 3.00068035 0.54727146 0.6490135
#> C 1.5999805 0.7988561 0.00000000 1.11145146 0.9476375
#> D 1.5483008 0.7471764 -0.05167973 0.00000000 0.1870579
#> E 1.7152103 0.9140859 0.11522977 0.16690950 0.0000000