metric_var()computes the metric variance (or total variance), i.e. a global measure of spread.metric_sd()computes the metric standard deviation.
Usage
metric_var(x, ...)
metric_sd(x, ...)
# S4 method for CompositionMatrix
metric_var(x)
# S4 method for CompositionMatrix
metric_sd(x)Arguments
- x
A
CompositionMatrixobject.- ...
Currently not used.
Value
A numeric vector.
Details
The metric variance is the average of the CLR variances.
References
Boogaart, K. G. van den & Tolosana-Delgado, R. (2013). Analyzing Compositional Data with R. Berlin Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-36809-7 .
Hron, K. & Kubáček. L. (2011). Statistical Properties of the Total Variation Estimator for Compositional Data. Metrika, 74 (2): 221-230. doi:10.1007/s00184-010-0299-3 .
Pawlowsky-Glahn, V. & Egozcue, J. J. (2001). Geometric Approach to Statistical Analysis on the Simplex. Stochastic Environmental Research and Risk Assessment, 15(5): 384-398. doi:10.1007/s004770100077 .
See also
Other statistics:
aggregate(),
covariance(),
dist,
mahalanobis(),
margin(),
mean(),
quantile(),
scale(),
variation()
Examples
## Data from Aitchison 1986
data("hongite")
## Coerce to compositional data
coda <- as_composition(hongite)
## Mean
mean(coda)
#> A B C D E
#> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827
## Quantile
quantile(coda)
#> A B C D E
#> 0% 0.323 0.0520000 0.007 0.055 0.037
#> 25% 0.423 0.1290000 0.038 0.083 0.065
#> 50% 0.452 0.2380000 0.097 0.095 0.085
#> 75% 0.486 0.3233233 0.234 0.109 0.098
#> 100% 0.523 0.4660000 0.429 0.158 0.132
## Metric variance
metric_var(coda)
#> [1] 1.691324
## Metric standard deviation
metric_sd(coda)
#> [1] 0.6502546