logratio-transformations {mixOmics} | R Documentation

## Log-ratio transformation

### Description

This function applies a log transformation to the data, either CLR or ILR

### Usage

    logratio.transfo(X, logratio = c("none", "CLR", "ILR"), offset = 0)

### Arguments

`X` | numeric matrix of predictors
`logratio` | log-ratio transform to apply, one of "none", "CLR" or "ILR"
`offset` | Value that is added to X for CLR and ILR log transformation. Default to 0.

### Details

`logratio.transfo` applies a log transformation to the data, either CLR (centered log ratio transformation) or ILR (Isometric Log Ratio transformation). In the case of CLR log-transformation, X needs to be a matrix of non-negative values and `offset` is used to shift the values away from 0, as commonly done with counts data.

### Value

`logratio.transfo` simply returns the log-ratio transformed data.

### Author(s)

Florian Rohart, Kim-Anh Lê Cao, Al J Abadi

### References

Kim-Anh Lê Cao, Mary-Ellen Costello, Vanessa Anne Lakis, Francois Bartolo, Xin-Yi Chua, Remi Brazeilles, Pascale Rondeau mixMC: a multivariate statistical framework to gain insight into Microbial Communities bioRxiv 044206; doi: http://dx.doi.org/10.1101/044206

John Aitchison. The statistical analysis of compositional data. Journal of the Royal Statistical Society. Series B (Methodological), pages 139-177, 1982.

Peter Filzmoser, Karel Hron, and Clemens Reimann. Principal component analysis for compositional data with outliers. Environmetrics, 20(6):621-632, 2009.

### See Also

`pca`, `pls`, `spls`, `plsda`, `splsda`.

### Examples

    data(diverse.16S)
    CLR = logratio.transfo(X = diverse.16S$data.TSS, logratio = 'CLR')
    # no offset needed here as we have put it prior to the TSS, see www.mixOmics.org/mixMC

[Package mixOmics version 6.16.3 Index]