A function to plot the scores resulting from fitting a PPCCA model to metabolomic data.
ppcca.scores.plot(output, Covars, group = FALSE, covarnames=NULL)An object resulting from fitting a PPCCA model.
An N x L covariate data matrix where each row is a set of covariates.
Should it be relevant, a vector indicating the known treatment group membership of each observation.
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, G., Gormley, I.C. and Brennan, L. (2010) Probabilistic principal components analysis for metabolomic data. Technical report. University College Dublin, Ireland.