jointseg (version 1.0.2)

modelSelection: Model selection

Description

Select the best number of breakpoints

Usage

modelSelection(rse, n, c = 2.5, lambdas = NULL, method = c("Birge",
  "Lebarbier"))

Arguments

rse

RSE as output by pruneByDP

n

Length of the profile

c

Parameter for the model selection

lambdas

A list of candidate values for the calibration of the penalty

method

Method to calibrate the constant in the penalty for model selection

Value

A list with elements

lambda

A numerical value, the result of an internal model selection function

Details

This function is not intended to be called directly, but implicitly through jointSeg or PSSeg.

References

Lebarbier, E. (2005). Detecting multiple change-points in the mean of Gaussian process by model selection. Signal processing, 85(4), 717-736

Birg\'e, L. (2001). Gaussian model selection. J.Eur Math. Soc, 3(3):203-268

See Also

jointSeg

PSSeg

Examples

# NOT RUN {
## load known real copy number regions
affyDat <- acnr::loadCnRegionData(dataSet="GSE29172", tumorFraction=1)
sim <- getCopyNumberDataByResampling(1e4, 5, minLength=100, regData=affyDat)
Y <- as.matrix(sim$profile[, "c"])

## Find candidate breakpoints
K <- 50
resRBS <- segmentByRBS(Y, K=K)
## Prune candidate breakpoints
resDP <- pruneByDP(Y, candCP=resRBS$bkp)
selectedModel <- modelSelection(rse=resDP$rse, n=nrow(Y), method="Lebarbier")
str(selectedModel)

## breakpoints of the best model
bestBkp <- resDP$bkp[[selectedModel$kbest]]
print(bestBkp)

## truth
print(sim$bkp)

## Note that all of the above can be done directly using 'PSSeg'
res <- PSSeg(sim$profile, method="RBS", stat="c", K=K)
##  stopifnot(identical(res$bestBkp, bestBkp))
# }