buildCount.BigBang: Builds the rank and frequency stability counting

In galgo: Genetic Algorithms for Multivariate Statistical Models from Large-Scale Functional Genomics Data

Description

Builds the rank and frequency stability counting.

Usage

## S3 method for class 'BigBang'
buildCount(o, filter="none", subset=TRUE, maxCounts=o$maxCounts, ...)

Arguments

filter	The BigBang object can save information about solutions that did not reach the goalFitness. filter=="solutions" ensures that only chromosomes that reach the goalFitness are considered. fitlter=="none" take all chromosomes. filter=="nosolutions" consider only no-solutions (for comparative purposes).
subset	Second level of filter. subset can be a vector specifying which filtered chromosomes are used. It can be a logical vector or a numeric vector (indexes in order given by $bestChromosomes in BigBang object variable).
maxCounts	Controls the fine-detail in computing the rank in a way that only maxCount ranks are saved despite of the real number solutions. For 1000 solutions and maxCount of 100, the rank is saved every 10 generations. The The default value is 100. Increasing this value increase the amount of memory and time needed to compute the rank stability plot; however, the level of detail in stability is increased.

Value

Nothing.

Author(s)

Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf

References

Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675

See Also

For more information see BigBang.

Examples

## Not run: 
   buildCount(bb, maxCounts=300)
   plot(bb, type="generankstability")
   
## End(Not run)
 

galgo documentation built on May 2, 2019, 4:20 a.m.