Fit a probabilistic principal components and covariates analysis model to a metabolomic data set, and assess uncertainty via the jackknife.
Fit a probabilistic principal components analysis model to a metabolomic data set, and assess uncertainty via the jackknife.
Fit a probabilistic principal components analysis (PPCA) model to a metabolomic data set via the EM algorithm.
First M-step of the AECM algorithm when fitting a mixture of PPCA models.
Second M-step of the AECM algorithm when fitting a mixture of PPCA models.
NMR metabolomic spectra from urine samples of 18 mice.
Plot scores from a fitted PPCA model
Fit a probabilistic principal components and covariates analysis (PPCCA) model to a metabolomic data set via the EM algorithm.
Plot scores from a fitted MPPCA model
Function to scale metabolomic spectral data.
Fit a mixture of probabilistic principal components analysis (MPPCA) model to a metabolomic data set via the EM algorithm to perform simultaneous dimension reduction and clustering.
Plot scores from a fitted PPCCA model.
Function to scale covariates.
Second E step of the AECM algorithm when fitting a mixture of PPCA models.
Plot loadings resulting from fitting a MPPCA model.
Plot loadings.
First E step of the AECM algorithm when fitting a mixture of PPCA models.
Probabilistic latent variable models for metabolomic data.
Function to plot a heatmap of BIC values.
Plot loadings and their associated confidence intervals.
Assess convergence of an EM algorithm.
NMR spectral data from brain tissue samples.