classPredictionMatrix.BigBang {galgo} | R Documentation

## Predicts class for samples from chromosomes

### Description

Predicts class for samples from chromosomes.

### Usage

    ## S3 method for class 'BigBang'
    classPredictionMatrix(o,
    	filter="none",
    	subset=TRUE,
    	classFunc=o$data$classFunc,
    	classes=NULL,
    	sampleNames=NULL,
    	chromosomes=NULL,
    	verbose=TRUE,
    	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 `$bestChromosomes` in `BigBang` object variable). If it is a numeric vector length one, a positive value means take those top chromosomes sorted by fitness, a negative value take those at bottom.
`classFunc` | The function that provides the class prediction.
`classes` | The known classes if they are different than those in `BigBang$classes` (or `BigBang$data$classes`).
`sampleNames` | Sample names if they are different than those in `BigBang$classes` (or `BigBang$data$classes`).
`chromosomes` | Specific chromosome list. The default is use the solution from `BigBang` object filtered by `filter` and `subset`.
`verbose` | Display processing information.
`use.cache` | Save/Restore values from previous computations with same parameters.

### Details

`classFunc` is called for each chromosome, therefore this routine can be time consuming depending on the behaviour of `classFunc`. The default `classFunc` from `configBB.VarSel` computes the class by majority of votes using all splits. Use `...` for specifying `splits`, `set` or any other parameter for `classFunc`.

### Value

A matrix whose rows are samples and columns are classes. Each value is the number of times the sample was predicted as that class.

### 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`. `*confusionMatrix()`.

### Examples

    ## Not run:
       #bb is a BigBang object
       cpm <- classPredictionMatrix(bb)
       cpm
       cm <- confusionMatrix(bb)
       cm
       #equivalent and quicker because classPredictionMatrix is provided
       cm <- confusionMatrix(bb, cpm)
       cm

       specificityClass(bb, cm)
       specificityClass(bb, cpm)
       specificityClass(bb)
       # all are equivalent
       sensitivityClass(bb, cpm)
       sensitivityClass(bb, cm)
       sensitivityClass(bb)
       # all are equivalent

    ## End(Not run)

[Package galgo version 1.4 Index]