pcit {PCIT} | R Documentation |
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
result <- pcit(m, force.serial=FALSE, force.parallel=FALSE, nslaves=NULL, pass.file=TRUE, verbose=getOption("verbose"), tol.type=c("mean", "min", "max", "median"))
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". |
Linear indices are returned for those correlations found to be non-significant.
Nathan S. Watson-Haigh
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.