get_cor_matrices: Obtain correlation matrices for each subject.

In abnormally-distributed/rsfcNet: Analyze resting state functional connectivity networks

Description

Get regularized estimates of the correlation matrices using a variety of algorithms.

Usage

get_cor_matrices(scrubbed, method = "partial", n = NULL, n.nodes = NULL)

Arguments

scrubbed	The list of scrubbed time series matrices.
method	One of "pearson", "partial", or "covar".
n	The number of subjects in the list. Checks the global environment for "n" but may be assigned directly.
n.nodes	The number of nodes. Checks the global environment for "n.nodes" but may be assigned directly.

Details

For methods "pearson", "partial", "covar", the estimation is done using the James-Stein based methods described in Schaefer and Strimmer (2005) and the optimal shrinkage level calculated for each subject using the method of Opgen-Rhein and Strimmer (2007). Respectively these give the regularized pearson correlation matrix, shrinkage estimated partial correlation matrix, and regularized covariance matrix, This depends on the corpcor package.The covariance option shouldn't be used directly for network analysis, but can be used with whatever you need it for. As the number of observations increases relative to the number of variables these all converge onto the empirical pearson, partial, and covariance matrices respectively.

Value

The connectivity matrices.

Author(s)

Brandon Vaughan

References

Opgen-Rhein, R., and K. Strimmer (2007). Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach. Statist. Appl. Genet. Mol. Biol. 6:9. doi:10.2202/1544-6115.1252

Schaefer, J., and K. Strimmer (2005). A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4:32. doi:10.2202/1544-6115.1175

See Also

threshold_matrix binarize

Examples

cormats = get_cor_matrices(scrubbed_ts_list)

abnormally-distributed/rsfcNet documentation built on March 8, 2020, 5:32 p.m.