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Missing Values Policy


Compositional data are quantitative (positive) descriptions of the parts of some whole, carrying relative, rather than absolute, information (ie. only relative changes are relevant; Aitchison 1986).

Basically, two situations can be outlined:

  • The presence of zeros: these are considered as observed quantities, but which happen to be below the detection limit (thus interpreted as small unknown values).

  • The presence of missing values (NA): these indicate that the quantities in question have not been observed.

When creating a CompositionMatrix object, the presence of zero and NA values is allowed: this makes it possible to explore and visualize the data while preserving the missing structure. However, the user must deal with these missing values before proceeding further (e.g. by removing incomplete cases or replacing the values concerned): log-ratio transformations cannot be computed in the presence of missing values.


If you need more advanced features (e.g. imputation of missing values), you should consider the compositions or robCompositions package.


Aitchison, J. (1986). The Statistical Analysis of Compositional Data. London: Chapman and Hall.

See also

Other imputation methods: replace_NA(), replace_zero()