robustComponents: Robust GFA components

In GFA: Group Factor Analysis

Robust GFA components

Description

robustComponents analyzes a collection of sampling chains and returns robust components.

Usage

robustComponents(models, corThr = 0.9, matchThr = 0.5)

Arguments

models	Either a vector containing the file names, where the models are saved as 'res', or a list containing the models.
corThr	How close two components are required to be, in terms of correlation, in order to match them.
matchThr	How big proportion of the chains need to contain the component to include it in the robust components.

Details

The function returns the effects (i.e. reconstructions) of robust components to the data level. It is useful for a thorough model interpretation, accumulating power over several sampling chains by comparing them in the observation space (as opposed to the latent space). The function is needed for this task, as the extreme multi-modality of factor analysis prohibits efficient sampling techniques that would result in a posterior estimate converging to the true posterior in practice. The function uses a heuristic correlation-based procedure to analyze which components occur frequently in GFA sampling chains.

Value

A list with the following elements (when input data are paired in two modes, the returned list is of length 2, containing the following elements for each mode):

Krobust	The number of robust components found with the given thresholds.
effect	The component effect in the data space; and array of size N \times \sum_{m=1}^M D_m \times Krobust.
indices	The corresponding component indices; a length(models) \times Krobust matrix. Negative indices denote the closest component in the corresponding repetition, having no components above the threshold.
cor	The correlations of the components matched to this robust component; a matrix of size length(models) \times Krobust. The correlations are reported relative to the first repetition with this component observed.

Examples

X <- matrix(rnorm(10*2),10,2)
W <- matrix(rnorm(15*2),15,2)
Y <- tcrossprod(X,W) + matrix(rnorm(10*15),10,15)
opts <- getDefaultOpts() #Default options
#Fast runs for the demo, default options recommended in general
opts[c("iter.burnin", "iter.max")] <- c(500, 1000)
res <- list()
for(i in 1:4) res[[i]] <- gfa(list(Y[,1:6],Y[,7:15]),opts=opts,K=3)
rob <- robustComponents(res)

GFA documentation built on Oct. 21, 2023, 5:06 p.m.