pcit {PCIT}R Documentation

Apply the PCIT algorithm to a correlation matrix

Description

Given a correlation matrix the PCIT algorithm is applied to identify non-significant correlations. If a parallel environment running Rmpi is detected, a parallel implementation will be run unless force.serial=TRUE

Usage

  result <- pcit(m, force.serial=FALSE, force.parallel=FALSE, nslaves=NULL,
    pass.file=TRUE, verbose=getOption("verbose"), tol.type=c("mean", "min", "max", "median"))

Arguments

m - A correlation matrix.
force.serial - A boolean to indicate if the serial implementation of PCIT should be forced.
force.parallel - A boolean to indicate if the parallel implementation of PCIT should be forced.
nslaves - The number of slaves to spawn. By default, as many slaves as possible are spawned. UNTESTED OPTION.
pass.file - A boolean to indicate if the correlation matrix should be passed between the master and slave CPUs as a file.
verbose - A boolean to indicate if verbose output should be used.
tol.type - The type of tolerance measure to be used in PCIT. Current options are "mean", "min" and "max".

Value

Linear indices are returned for those correlations found to be non-significant.

Author(s)

Nathan S. Watson-Haigh

References

Reverter, A. & Chan, E.K., 2008. Combining partial correlation and an information theory approach to the reversed-engineering of gene co-expression networks. Bioinformatics, btn482.


[Package PCIT version 1.01-1 Index]