Log-ratio transformation
This function applies a log transformation to the data, either CLR or ILR
logratio.transfo(X, logratio = c("none", "CLR", "ILR"), offset = 0)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. |
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.
logratio.transfo simply returns the log-ratio transformed
data.
Florian Rohart, Kim-Anh Lê Cao, Al J Abadi
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.
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
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