pcaModels.BigBang {galgo} | R Documentation |
Plots models in principal components space.
## S3 method for class 'BigBang' pcaModels( O, models, data=O$data$data, traspose=FALSE, center=TRUE, scale=TRUE, subset=NULL, main=O$main, sampleColors=NULL, sampleNames=NULL, npc=4, pch=19, gap=0.25, classes=NULL, show.loadings=FALSE, loadings.round=6, labels=TRUE, order=0, col=1:200, columns=NULL, jitterFactor=0, ...)
models |
The models(chromosomes) to plot. It can be a chromosome list or models resulted from |
data |
Data if this is not provided in |
traspose |
Traspose the data (for display and data restrictions). |
subset |
To limit the usage of |
center |
Logical value indicating whether scalling by genes to mean 0. See |
scale |
Logical value indicating whether scalling by genes to 1 variance. See |
main,gap,pch |
Plot parameters (method pairs). If |
sampleColors |
Colors for samples. |
sampleNames |
To plot the samples names. Use the variable |
classes |
Sample classes. The default is using |
... |
Other parameters for |
Returns the results of prcomp in a list.
Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf
Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675
For more information see BigBang
.,
*plot()
,
*forwardSelectionModels()
,
prcomp
(),
princomp
().
## Not run: #bb is a BigBang object pcaModels(bb, bb$bestChromosomes[1]) fsm <- forwardSelectionModels(bb) fsm names(fsm) heatmapModels(fsm, subset=1) fsm <- forwardSelectionModels(bb, minFitness=0.9, fitnessFunc=bb$galgo$fitnessFunc) heatmapModels(fsm, subset=1) pcaModels(fsm, subset=1) fitnessSplits(bb, chromosomes=list(fsm$models[[1]])) ## End(Not run)