R: Model tuning function

tune.iCluster2 {iCluster} | R Documentation

## Model tuning function

### Description

Model tuning process for choosing the number of clusters k and the lasso penalty parameters.

### Usage

    tune.iCluster2(datasets, k, n.lambda,nrep, mc.cores,max.iter)

### Arguments

`datasets` | A list containing data matrices. For each data matrix, the rows represent samples, and the columns represent genomic features.
`k` | Number of classes for the samples.
`nrep` | Number of training and test data partition for computing the reproducibility index.
`n.lambda` | The number of sampled points for the uniform design. Use the default value by setting n.lambda=NULL
`mc.cores` | Number of cores to use for parallel computation.
`max.iter` | Number of EM iterations.

### Value

A list with the following elements.

`best.fit` | Model fit under the optimal lambda values that give the highest reproducibility index.
`RI` | A vector of reproducibility index associated with each of the sampled lambda combination.
`ud` | Sampled lambda combinations under the uniform design

### Author(s)

Ronglai Shen shenr@mskcc.org

### References

Ronglai Shen, Adam Olshen, Marc Ladanyi. (2009). Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906-2912.

Ronglai Shen, Qianxing Mo, Nikolaus Schultz, Venkatraman E. Seshan, Adam B. Olshen, Jason Huse, Marc Ladanyi, Chris Sander. (2012). Integrative Subtype Discovery in Glioblastoma Using iCluster. PLoS ONE 7, e35236

### See Also

`iCluster2`,`plotiCluster`, `compute.pod`, `plotHeatmap`

### Examples

    library(iCluster)
    library(caTools, lib.loc="/apps/Rlib64/")
    library(gdata, lib.loc="/apps/Rlib64/")
    library(gtools, lib.loc="/apps/Rlib64/")
    library(gplots, lib.loc="/apps/Rlib64/")
    library(lattice, lib.loc="/apps/Rlib64/")
    library(parallel, lib.loc="/apps/Rlib64/")

    #data(simu.datasets)

    #cv.fit=alist()
    #for(k in 2:5){
    #  cat(paste("K=",k,sep=""),'\n')
    #  cv.fit[[k]]=tune.iCluster2(simu.datasets, k, mc.cores=6)
    #}

    ##Reproducibility index (RI) plot
    #plotRI(cv.fit)

    ##Based on the RI plot, k=3 is the best solution
    #best.fit=cv.fit[[3]]$best.fit

    ##Try different color schemes
    #plotHeatmap(fit=best.fit,datasets=simu.datasets,
    #sparse=c(TRUE,TRUE),col.scheme=list(bluered(256), greenred(256)))

[Package iCluster version 2.1.0 Index]