R: Apply the PCIT algorithm

pcit {PCIT} | R Documentation

## Apply the PCIT algorithm

### Description

Given a correlation matrix the PCIT algorithm (Reverter & Chan 2008) is applied to identify significant correlations. If a parallel environment running Rmpi is detected, a parallel implementation will be run unless force.serial=TRUE

### Usage

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

### 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.
`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".
`pass.type` | - The type of approach used to pass the correlation matrix from the master CPU to the slave CPUs. Current options are "file", "memory" and "db".

### Value

Linear indices are returned for those correlations found to be 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.

### Examples

    	data(PCIT)
    	m <- m[1:200,1:200]        # just use a small subset of the data

    	result <- pcit(m)

[Package PCIT version 1.5-3 Index]