ppcca.scores.plot: Plot scores from a fitted PPCCA model.
In MetabolAnalyze: Probabilistic Latent Variable Models for Metabolomic Data

Description Usage Arguments Details Author(s) References See Also

A function to plot the scores resulting from fitting a PPCCA model to metabolomic data.

1	ppcca.scores.plot(output, Covars, group = FALSE, covarnames=NULL)

`output`	An object resulting from fitting a PPCCA model.
`Covars`	An N x L covariate data matrix where each row is a set of covariates.
`group`	Should it be relevant, a vector indicating the known treatment group membership of each observation.
`covarnames`	Should it be relevant, a vector string indicating the names of the covariates.

This function produces a series of scatterplots each illustrating the estimated score for each observation within the reduced q dimensional space. The uncertainty associated with the score estimate is also illustrated through its 95

It is often the case that observations are known to belong to treatment groups; the treatment group membership of each observation can be illustrated on the plots produced by utilizing the ‘group’ argument.

Nyamundanda Gift, Isobel Claire Gormley and Lorraine Brennan

Nyamundanda, G., Gormley, I.C. and Brennan, L. (2010) Probabilistic principal components analysis for metabolomic data. Technical report. University College Dublin, Ireland.

ppcca.metabol, ppcca.metabol.jack

MetabolAnalyze documentation built on Aug. 31, 2019, 5:05 p.m.