Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/ppcca.metabol.R
This function fits a probabilistic principal components and covariates analysis model to metabolomic spectral data via the EM algorithm.
1 2 | ppcca.metabol(Y, Covars, minq=1, maxq=2, scale = "none", epsilon = 0.1,
plot.BIC = FALSE, printout=TRUE)
|
Y |
An N x p data matrix in which each row is a spectrum. |
Covars |
An N x L covariate data matrix in which each row is a set of covariates. |
minq |
The minimum number of principal components to be fit. |
maxq |
The maximum number of principal components to be fit. |
scale |
Type of scaling of the data which is required. The default is "none". Options include "pareto' and "unit" scaling. See |
epsilon |
Value on which the convergence assessment criterion is based. Set by default to 0.1. |
plot.BIC |
Logical indicating whether or not a plot of the BIC values for the different models fitted should be provided. By default, the plot is not produced. |
printout |
Logical indicating whether or not a statement is printed on screen detailing the progress of the algorithm. |
This function fits a probabilistic principal components and covariates analysis model to metabolomic spectral data via the EM algorithm. A range of models with different numbers of principal components can be fitted.
Care should be taken with the form of covariates supplied. All covariates are standardized (to lie in [0,1]) within the ppcca.metabol function for stability reasons. Hence continuous covariates and binary valued categorical covariates are easily handled. For a categorical covariate with V levels, the equivalent V-1 dummy variables representation should be passed as an argument to ppcca.metabol.
A list containing:
q |
The number of principal components in the optimal PPCCA model, selected by the BIC. |
sig |
The posterior mode estimate of the variance of the error terms. |
scores |
An N x q matrix of estimates of the latent locations of each observation in the principal subspace. |
loadings |
The maximum likelihood estimate of the p x q loadings matrix. |
coefficients |
The maximum likelihood estimates of the regression coefficients associated with the covariates in the PPCCA model. |
BIC |
A vector containing the BIC values for the fitted models. |
AIC |
A vector containing the AIC values for the fitted models. |
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.
ppcca.metabol.jack
, ppcca.scores.plot
loadings.plot
1 2 3 4 5 6 7 | data(UrineSpectra)
## Not run:
mdlfit<-ppcca.metabol(UrineSpectra[[1]], UrineSpectra[[2]][,2], minq=2, maxq=2)
loadings.plot(mdlfit)
ppcca.scores.plot(mdlfit, UrineSpectra[[2]][,2], group=UrineSpectra[[2]][,1], covarnames="Weight")
## End(Not run)
|
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: mvtnorm
Loading required package: ellipse
Attaching package: 'ellipse'
The following object is masked from 'package:graphics':
pairs
Loading required package: gtools
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
q = 2 : PPCCA converged.
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