fitnessSplits.BigBang: Computes the fitness function from chromosomes for different...

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

Description

Computes the fitness function from chromosomes for different splits.

Usage

## S3 method for class 'BigBang'
fitnessSplits(o,
	filter="none",
	subset=TRUE,
	fitnessFunc=o$data$modelSelectionFunc,
	maxCache=1e+06,
	chromosomes=NULL,
	use.cache=TRUE,
	...)

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). If it is a numeric vector length one, a positive value means take those top chromosomes sorted by fitness, a negative value take those at bottom.
fitnessFunc	The function that provides the fitness for every chromosome. If the fitness is “split-sensitive” it should returns only one value (like the common $galgo$fitnessFunc variable). If the fitness does the splitting process itself (like $data$modelSelectionFunc), the result should be a vector of a fitness value for every split. The default use $data$modelSelectionFunc.
maxCache	The maximum number of values to be saved in the BigBang object (all variables starting with "fitnessSplits"). Useful for saving results between R sessions.
chromosomes	The chromosomes to process. The default is using filter and subset to extract the chromosomes from the BigBang object.
use.cache	Save/Restore values from previous computations with same parameters.

Value

A Matrix with chromosomes in rows and splits in columns. Each value is the result of the fitness function in a given chromosome on an split.

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. *plot().

Examples

## Not run: 
   #bb is a BigBang object
   fs <- fitnessSplits(bb)
   fs
   fs <- fitnessSplits(bb, fitnessFunc=bb$galgo$fitnessFunc)
   fs
   fs <- fitnessSplits(bb, fitnessFunc=bb$data$modelSelectionFunc) # default
   fs
   
## End(Not run)
 

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