findSigLevelFdr {KCsmart} | R Documentation

## This function has not been properly implemented yet

### Description

Method to find the cutoff at which gains and losses are considered significant using permutations

### Usage

    findSigLevelFdr(data, observedSpm, n = 1, fdrTarget=0.05, maxmem=1000)

### Arguments

`data` | aCGH data in the same format as used for 'calcSpm'
`observedSpm` | A sample point matrix as produced by 'calcSpm'
`n` | Number of permutations
`fdrTarget` | Target False Discovery Rate (FDR)
`maxmem` | This parameter controls memory usage, set to lower value to lower memory consumption

### Details

The number of permutations needed for reliable results depends on the data and can not be determined beforehand. As a general rule-of-thumb around 100 permutations should be used for 'quick checks' and around 2000 permutations for more rigorous testing. The FDR method is less conservatie than the p-value based approach since instead of controlling the family wise error rate (FWER, P(false positive > 1)) it controls the false discovery rate (FDR) (false positives / total number of called data points).

### Value

A list with the cutoffs corresponding to the given FDR

`pos` | The cutoff for the gains
`neg` | The cutoff for the losses'

### Author(s)

Jorma de Ronde

### See Also

`plotScaleSpace`

### Examples

    data(hsSampleData)
    data(hsMirrorLocs)

    spm1mb <- calcSpm(hsSampleData, hsMirrorLocs)

    sigLevel1mb <- findSigLevelTrad(hsSampleData, spm1mb, n=3)

    plot(spm1mb, sigLevels=sigLevel1mb)
    plotScaleSpace(list(spm1mb), list(sigLevel1mb), type='g')

[Package KCsmart version 2.3.2 Index]