findSigLevelFdr: This function has not been properly implemented yet
In KCsmart: Multi sample aCGH analysis package using kernel convolution

Description Usage Arguments Details Value Author(s) See Also Examples

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

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

`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

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).

A list with the cutoffs corresponding to the given FDR

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

Jorma de Ronde

plotScaleSpace

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')