GFA model
In CCAGFA: Bayesian Canonical Correlation Analysis and Group Factor Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

Prunes out unnecessary components and determines for each of the remaining components whether it is shared or not. In other words, the function reveals the component allocation into shared and view-specific ones.

1 2	CCAtrim(model, threshold = 0.001) GFAtrim(model, threshold = 0.001)

`model`	A list of model parameters as returned by `CCA` or `GFA`.
`threshold`	The proportion of relative variance explained that components need to exceed to be detected as active.

This function can be used to prune out unnecessary components and to recognize which of the components are shared. This can be useful for interpretative purposes, but it is typically not necessary to apply this function prior to making predictions (with GFApred or otherwise). The inactive components will anyway automatically cancel out for the predictive formulas. The code works well for low-dimensional data, but for complex high-dimensional data sources one should check whether the trimming is reasonable; in such cases it is difficult to make clear decisions on component activity.

A list of parameter values as returned by GFA. The list also includes two extra elements:

`trimmed`	A boolean variable indicating that the model has been trimmed with this function.
`active`	A binary matrix indicating for each component (column) in which views (row) it is active.

Seppo Virtanen and Arto Klami

GFA, CCA

  #
  # Assume we have a variable model which has been learned with
  # GFAexperiment() or GFA().
  # Then the following line would trim it:
  #
  # trimmed <- GFAtrim(model)
  #