Description Details Author(s) References See Also
Various implementations of algorithms for probabilistic PCA, with an emphasis on covariance matrix estimation and network reconstruction in the presence of missing values.
Algorithms for PPCA have been ported from the PCAMV
MATLAB toolbox (Ilin and Raiko, 2010) and
extended from the pcaMethods
(Stacklies et. al., 2007) R-package to focus on covariance matrix estimation and network reconstruction in the presence of missing values. Full PCA functionality with pcaMethods
is retained in pcaNet
due to the use of the pcaRes class.
The inverse of the covariance matrix from PPCA can be computed efficiently, and this functionality is provided in ppca2Covinv
. Using the false discovery rate method from Strimmer (2008), the estimated partial correlations can be tested to construct a network. Whilst default behaviour for this is available, the full output of the testing is also provided, so that users may further explore the statistics using fdrtool
. Functionality for visualising the covariance matrix is provided, as well as for the reconstructed network using igraph
(Csardi and Nepusz, 2006).
Paul DW Kirk and Harry Gray
Maintainers: <paul.kirk@mrc-bsu.cam.ac.uk> <h.w.gray@dundee.ac.uk>
Oba, S., Sato, M.A., Takemasa, I., Monden, M., Matsubara, K.I. and Ishii, S., 2003. doi.
Stacklies, W., Redestig, H., Scholz, M., Walther, D. and Selbig, J., 2007. doi.
Ilin, A. and Raiko, T., 2010. link
Porta, J.M., Verbeek, J.J. and Kroese, B.J., 2005. link
Strimmer, K., 2008. link.
Strimmer, K., 2008. doi.
Csardi, G. and Nepusz, T., 2006. link.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.