genAlgConsensus: Find Edges Separating Two Groups using Multiple Genetic...

In bingat: Binary Graph Analysis Tools

Description

GA-Mantel is a fully multivariate method that uses a genetic algorithm to search over possible edge subsets using the Mantel correlation as the scoring measure for assessing the quality of any given edge subset.

Usage

	genAlgConsensus(data, covars, consensus = .5, numRuns = 10, 
		parallel = FALSE, cores = 3, ...)

Arguments

data	A matrix of edges(rows) for each sample(columns).
covars	A matrix of covariates(columns) for each sample(rows).
consensus	The required fraction (0, 1] of solutions containing an edge in order to keep it.
numRuns	Number of runs to do. In practice the number of runs needed varies based on data set size and the GA parameters set.
parallel	When this is 'TRUE' it allows for parallel calculation of the bootstraps. Requires the package doParallel.
cores	The number of parallel processes to run if parallel is 'TRUE'.
...	Other arguments for the GA function see genAlg

Details

Use a GA consensus approach to find edges that separate subjects based on group membership or set of covariates if you cannot run the GA long enough to get a final solution.

Value

A list containing

solutions	The best solution from each run.
consSol	The consensus solution.
selectedIndex	The selected edges by row number.

Author(s)

Sharina Carter, Elena Deych, Berkley Shands, William D. Shannon

Examples

	## Not run: 
		data(braingraphs)
		
		### Set covars to just be group membership
		covars <- matrix(c(rep(0, 19), rep(1, 19)))
		
		### We use low numbers for speed. The exact numbers to use depend
		### on the data being used, but generally the higher iters and popSize 
		### the longer it will take to run.  earlyStop is then used to stop the
		### run early if the results aren't improving.
		iters <- 500
		popSize <- 200
		earlyStop <- 250
		numRuns <- 3
		
		gaRes <- genAlgConsensus(braingraphs, covars, .5, numRuns, FALSE, 3, 
				iters, popSize, earlyStop)
	
## End(Not run)

bingat documentation built on May 1, 2019, 9:11 p.m.