Running ‘PairedPSCNData,SEG.R’ [0s/1s]
  Running ‘PairedPSCNData.R’ [0s/0s]
  Running ‘RawCopyNumbers,states.R’ [1s/2s]
  Running ‘RawCopyNumbers.R’ [2s/3s]
  Running ‘RawGenomicSignals.R’ [1s/2s]
  Running ‘RawGenomicSignals.SEG,MP.R’ [1s/2s]
  Running ‘RawGenomicSignals.SEG.R’ [1s/2s]
Running the tests in ‘tests/RawGenomicSignals.SEG.R’ failed.
Complete output:
  > library("aroma.core")
  Loading required package: R.utils
  Loading required package: R.oo
  Loading required package: R.methodsS3
  R.methodsS3 v1.7.1 (2016-02-15) successfully loaded. See ?R.methodsS3 for help.
  R.oo v1.21.0 (2016-10-30) successfully loaded. See ?R.oo for help.
  
  Attaching package: 'R.oo'
  
  The following objects are masked from 'package:methods':
  
      getClasses, getMethods
  
  The following objects are masked from 'package:base':
  
      attach, detach, gc, load, save
  
  R.utils v2.6.0 (2017-11-04) successfully loaded. See ?R.utils for help.
  
  Attaching package: 'R.utils'
  
  The following object is masked from 'package:utils':
  
      timestamp
  
  The following objects are masked from 'package:base':
  
      cat, commandArgs, getOption, inherits, isOpen, parse, warnings
  
  Loading required package: R.filesets
  R.filesets v2.12.0 successfully loaded. See ?R.filesets for help.
  
  Attaching package: 'R.filesets'
  
  The following objects are masked from 'package:R.utils':
  
      extract, validate
  
  The following objects are masked from 'package:base':
  
      append, readLines
  
  Loading required package: R.devices
  R.devices v2.15.1 (2016-11-09) successfully loaded. See ?R.devices for help.
  aroma.core v3.1.1 (2017-09-12) successfully loaded. See ?aroma.core for help.
  
  Attaching package: 'aroma.core'
  
  The following objects are masked from 'package:base':
  
      .Machine, colMeans, colSums, library, require, write
  
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Simulating copy-number data
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Number of loci
  > J <- 500
  > 
  > mu <- double(J)
  > mu[100:150] <- mu[100:150] + 1
  > mu[320:400] <- mu[320:400] - 1
  > eps <- rnorm(J, sd=1/2)
  > y <- mu + eps
  > x <- sort(runif(length(y), max=length(y))) * 1e5;
  > w <- runif(J)
  > w[320:400] <- 0.001
  > 
  > 
  > cn <- RawCopyNumbers(y, x)
  > print(cn)
  RawCopyNumbers:
  Name: <none>
  Number of rows: 500
  Number of columns: 3
  Columns: chromosome [numeric], x [numeric], cn [numeric]
  Chromosomes: 0 [1]
  Number of loci: 500
  Position range: [251749,4.99638e+07]
  Mean distance between loci: 99623.4
  > 
  > plot(cn, ylim=c(-3,3), col="#aaaaaa", xlab="Position (Mb)")
  > 
  > cnS <- binnedSmoothing(cn, by=500e3)
  > print(cnS)
  RawCopyNumbers:
  Name: <none>
  Number of rows: 100
  Number of columns: 3
  Columns: chromosome [numeric], x [numeric], cn [numeric]
  Chromosomes:  [0]
  Number of loci: 100
  > lines(cnS, col="black", lwd=3)
  > 
  > 
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > # Segment
  > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  > legend <- c()
  > 
  > if (require("DNAcopy")) {
  +   fit <- segmentByCBS(cn)
  +   cnr <- extractCopyNumberRegions(fit)
  +   print(cnr)
  +   drawLevels(cnr, col="red", lwd=3)
  +   legend <- c(legend, red="CBS")
  + }
  Loading required package: DNAcopy
  CopyNumberRegions:
  Number of regions: 5
  > 
  > if (require("GLAD") && packageVersion("GLAD") != "9.9.9") {
  +   fit <- segmentByGLAD(cn)
  +   cnr <- extractCopyNumberRegions(fit)
  +   print(cnr)
  +   drawLevels(cnr, col="blue", lwd=3)
  +   legend <- c(legend, blue="GLAD")
  + }
  Loading required package: GLAD
  
  ######################################################################################
  
  Have fun with GLAD
  
  For smoothing it is possible to use either
  the AWS algorithm (Polzehl and Spokoiny, 2002,
  or the HaarSeg algorithm (Ben-Yaacov and Eldar, Bioinformatics,  2008,
  
  If you use the package with AWS, please cite:
  Hupe et al. (Bioinformatics, 2004, and Polzehl and Spokoiny (2002,
  
  If you use the package with HaarSeg, please cite:
  Hupe et al. (Bioinformatics, 2004, and (Ben-Yaacov and Eldar, Bioinformatics, 2008,
  
  For fast computation it is recommanded to use
  the daglad function with smoothfunc=haarseg
  
  ######################################################################################
  
  New options are available in daglad: see help for details.
  
  Error in UseMethod("as.profileCGH") : 
    no applicable method for 'as.profileCGH' applied to an object of class "data.frame"
  Calls: segmentByGLAD -> segmentByGLAD.RawGenomicSignals -> <Anonymous>
  Execution halted