Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.
|Author||Eemeli Leppaaho [aut, cre], Seppo Virtanen [aut], Muhammad Ammad-ud-din [ctb], Suleiman A Khan [ctb], Tommi Suvitaival [ctb], Inka Saarinen [ctb], Samuel Kaski [ctb]|
|Date of publication||2017-03-17 13:01:18 UTC|
|Maintainer||Eemeli Leppaaho <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
getDefaultOpts: A function for generating the default priors of GFA model
gfa: Gibbs sampling for group factor analysis
GFA-package: Group factor analysis.
informativeNoisePrior: Informative noise residual prior
normalizeData: Normalize data to be used by GFA
reconstruction: Full data reconstruction based on posterior samples
robustComponents: Robust GFA components
sequentialGfaPrediction: Sequential prediction of new samples from observed data views...
undoNormalizeData: A function for returning predictions into the original data...
visualizeComponents: Visualize GFA components
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