In rdevito/MSFA: Maximum Likelihood Estimation and Bayesian analysis of MSFA models

The following example illustrates how to fit a MSFA model via the ECM Algorithm, using a data set available in the Bioconductor repository (www.bioconductor.org).

Getting the data

Some pre-processing is required to get the data into a form suitable for the analysis. This was already done, and the resulting data frame is saved into the data_immune object. The commands that were used to form it are included in the help file for the data object.

library(MSFA)
data(data_immune)
help(data_immune)

Obtaining suitable starting values for model parameters

Then we get suitable starting values for model parameters, selecting $K=3$ common factors and $3, 4$ study-specific factors for the two studies, respectively.

start_value <- start_msfa(X_s = data_immune, k = 3, j_s = c(3, 4))

Fitting the model via ECM

Now everything is in place for estimating the model parameters via the ECM algorithm

mle <-  ecm_msfa(data_immune, start_value, trace = FALSE)

The estimated matrix of common loadings can be represented by a suitable heatmap:

library(gplots)
heatmap.2(mle$Phi,dendrogram='none', Rowv=FALSE, Colv=FALSE,trace='none', density.info="none", col=heat.colors(256))

rdevito/MSFA documentation built on March 18, 2020, 2:57 p.m.