activeChromosomeSet.BigBang: Focus the analysis to different sets of chromosomes

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

Description

Swaps the "active" chromosomes for analysis. All the plots and methods compute the information from the variable $bestChromosomes, $bestFitness and $count. When callEnhancerFunc has been used it could be needed to use the same plots with different sets of chromosomes. activeChromosomeSet swaps the information between different chromosomes sets to concentrate the analysis on that set.

Usage

## S3 method for class 'BigBang'
activeChromosomeSet(.O,
	set=c("evolved", "default", "custom"),
	count=TRUE,
	chromosomes=NULL,
	fitness=NULL,
	...)

Arguments

set	"evolved" specify to analyse original chromosomes that were evolved insted of the enhanced (see evolvedChromosomes and evolvedFitness parameters). "default" restore the original chromosomes. "custom" is for user-specified chromosomes and fitness.
count	Instruct to re-build the count matrix used for some plots. Recommended to be TRUE always.
chromosomes	The chromosome set to analyse. The default is to use the variable $evolvedChromosomes from the BigBang object.
fitness	The fitness of the chromosomes to analyse. The default is to use the variable $evolvedFitness from the BigBang object.

Value

Returns 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

   # bb was created
   ## Not run: 
   activeChromosomeSet(bb, set="evolved")
   plot(bb)
   activeChromosomeSet(bb, set="default")
   plot(bb)
   
## End(Not run)
 

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