Repository files navigation

TheGMCM package(Bilgrau et. al., 2016)offers R functions that very fast perform high-dimensional meta-analysis(Li et. al., 2011)and general unsupervised cluster analysis(Tewari et. al., 2011)using Gaussian Copula Mixture Models.Online documentation is availablehere.

Gaussian copula mixture models (GMCMs) are a very flexible alternative to regular Gaussian mixture models (GMMs) in unsupervised cluster analysis of continuous data where non-normal clusters are present.GMCMs models theranks of the observed data and are thus invariant to monotone increasing transformations of the data, i.e. they are semi-parametric and only the ordering of the data is important providing needed flexibility.A special-case of GMCMs can be used for a novel meta-analysis approach in high-dimensional settings.In this context, the model tries to cluster results into two groups which agree and do not agree on statistical evidence. These two groups corresponds to a reproducible and irreproducible group.

The optimization of the complicated likelihood function is difficult, however.GMCM utilizesRcppandRcppArmadilloto evaluate the likelihood function quickly and arrive at a parameter estimate using either standard numerical optimization routines or an pseudo EM algorithm.

Additional information, documentation, help, and examples can be found byhere or by running?GMCM inR.The paper[1] is also found as a vignette byvignette("GMCM-JStatSoft") orthe official website online..The core user functions ofGMCM arefit.full.GMCM andfit.meta.GMCM.

The released and tested version ofGMCM is available atCRAN(Comprehensive R Archive Network).It can be installed from within R by running

install.packages("GMCM")

If you wish to install the latest version ofGMCM directly from the master branch at GitHub, run

#install.packages("remotes")  # Install remotes if neededremotes::install_github("AEBilgrau/GMCM")

Note, that this version is in development and is likely different from the version at CRAN.As such, it may be unstable. Be sure that you have thepackage development prerequisitesif you wish to install the package from the source.

When installed, runGMCM::runGMCM() to launch a local instance of the GMCM shiny application also availableonline at shinyapps.io.Runnews(package = "GMCM") to view the latest changes of GMCM or visithere.

For previous versions ofGMCM, visit the oldreleases at GitHub or thearchive at CRAN.

As noted above, the usage of GMCM comes in two different applications; one general and one special.

An example of using the package to fit special GMCMs for meta analysis of is described herevignette("usage-example-special-model"). This model is a specific special case of the general GMCMs.

An example of unsupervised clustering using the package is found withvignette("usage-example-general-model") for general purposes.

The package also provides a graphical user interface via Shiny for both its uses. Seevignette("usage-shiny-graphical-interface").

1. Anders Ellern Bilgrau, Poul Svante Eriksen, Jakob Gulddahl Rasmussen, HansErik Johnsen, Karen Dybkaer, Martin Boegsted (2016).GMCM: UnsupervisedClustering and Meta-Analysis Using Gaussian Mixture Copula Models.Journal of Statistical Software, 70(2), 1-23.doi:10.18637/jss.v070.i02