MSFA

author: Roberta De Vito, Ruggero Bellio

Fits the Multi-Study Factor Analysis model via ECM algorithm and via a Bayesian approach.

1 Fitting a MSFA model via the ECM Algorithm

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))

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2 Bayesian Analysis of a MSFA model

The following example illustrates a Bayesian analysis of a MSFA model. Although the methodology has been developed targeting the $p>n$ case, for the sake of simplicity we illustrate the analysis of the same data set employed for maximum likelihood estimation. The data set is 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)

Sampling from the posterior distribution

We fist estimate a model with a somewhat large dimension of the various loading matrices, so we set a dimension 10 for both the common factor loadings and the study-specific loadings. In order to get reproducible results, we set the random seed.

set.seed(1971)
out10_1010 <- sp_msfa(data_immune,  k = 10,  j_s = c(10, 10), trace = FALSE)

We take as the estimated $\Sigma_\Phi$ the posterior median

p <- ncol(data_immune[[1]])
nrun <- dim(out10_1010$Phi)[3]
SigmaPhi <-  SigmaLambda1 <- SigmaLambda2 <- array(0, dim=c(p, p, nrun))
for(j in 1:nrun)
{
  SigmaPhi[,,j] <- tcrossprod(out10_1010$Phi[,,j]) 
  SigmaLambda1[,,j] <- tcrossprod(out10_1010$Lambda[[1]][,,j]) 
  SigmaLambda2[,,j] <- tcrossprod(out10_1010$Lambda[[2]][,,j]) 
}
SigmaPhi <- apply(SigmaPhi, c(1, 2), median)
SigmaLambda1 <- apply(SigmaLambda1, c(1, 2), median)
SigmaLambda2 <- apply(SigmaLambda2, c(1, 2), median)
Phi <- apply(out10_1010$Phi, c(1, 2), median)

Choice of the number of factors

Then we proceed to the choice of the number of common latent factors.

plot(sp_eigen(SigmaPhi), pch = 16)
abline(h = 0.05, col = 2)

We note that 5 factors are above the $5%$ threshold, so we choose $K=5$. We proceed in a similar way for the two study-specific loading matrices:

par(mfrow=c(1, 2))
plot(sp_eigen(SigmaLambda1), pch=16)
abline(h = 0.05, col = 2)
plot(sp_eigen(SigmaLambda2), pch=16)
abline(h = 0.05, col = 2)

We end up with dimensions 3 and 4 for the study-specific factor loadings.

OP prostprocessing

We post-process the estimated loading matrix by the OP procedure

Phi_OP10 <- sp_OP(out10_1010$Phi[,1:5,], itermax = 10)

For larger data size, we recommend to reduce the output level in the call to sp_msfa.