@@ -1,21 +1,21 @@

 Package: crlmm

 Type: Package

 Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.

-Version: 1.13.12

-Date: 2010-12-10

-Author: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.edu.au>, Ingo Ruczinski <iruczins@jhsph.edu>, Rafael A Irizarry

+Version: 1.13.14

+Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry

 Maintainer: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>

 Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms

 License: Artistic-2.0

-Depends: R (>= 2.13.0),

-         methods,

-         Biobase (>= 2.15.0),

-         oligoClasses (>= 1.17.34)

-Imports: affyio (>= 1.19.2),

+Depends: R (>= 2.14.0)

+Imports: methods,

+         Biobase (>= 2.15.4),

+         oligoClasses (>= 1.17.36),

+         BiocGenerics,

+         affyio (>= 1.23.2),

          ellipse,

-         genefilter (>= 1.33.0),

+         genefilter (>= 1.37.1),

          mvtnorm,

-         preprocessCore (>= 1.13.4),

+         preprocessCore (>= 1.17.7),

          splines,

          stats,

          SNPchip,

@@ -41,10 +41,8 @@ Collate: AllGenerics.R

 	 crlmmGT2.R

          crlmm-illumina.R

 	 snprma-functions.R

-	 cn-functions.R

          utils.R

          zzz.R

 	 test_crlmm_package.R

 LazyLoad: yes

 biocViews: Microarray, Preprocessing, SNP, Bioinformatics, CopyNumberVariants

-Packaged: 2011-04-30 17:10:59 UTC; biocbuild

\ No newline at end of file

@@ -1,56 +1,68 @@

 useDynLib("crlmm", .registration=TRUE)

-##---------------------------------------------------------------------------

-## Biobase

-##---------------------------------------------------------------------------

-importClassesFrom(Biobase, AnnotatedDataFrame, AssayData, eSet, SnpSet,

-		  NChannelSet, MIAME, Versioned, VersionedBiobase,

-		  Versions)

-importMethodsFrom(Biobase, annotation, "annotation<-",

-                  annotatedDataFrameFrom, assayData, "assayData<-",

-                  combine, dims, experimentData, "experimentData<-",

-                  fData, featureData, "featureData<-", featureNames,

-                  fvarMetadata, fvarLabels, pData, "pData<-", phenoData,

-                  "phenoData<-", protocolData, "protocolData<-",

-                  pubMedIds, rowMedians, sampleNames, snpCall,

-                  snpCallProbability,

-		  "snpCall<-", "snpCallProbability<-", storageMode,

-                  "storageMode<-", updateObject, varLabels)

-importFrom(Biobase, assayDataElement, assayDataElementNames,

-           assayDataElementReplace, assayDataNew, classVersion,

-           validMsg)

-

-##---------------------------------------------------------------------------

-## oligoClasses

-##---------------------------------------------------------------------------

-importClassesFrom(oligoClasses, SnpSuperSet, AlleleSet, CNSet)##, ff_or_matrix)

-importMethodsFrom(oligoClasses, allele, calls, "calls<-", confs,

-		  "confs<-", cnConfidence, "cnConfidence<-", isSnp,

-		  chromosome, position, A, B,

-		  "A<-", "B<-", open, close, flags,

-		  openff, closeff,

-		  batchStatistics, "batchStatistics<-", updateObject,

-		  checkOrder)

-

-importFrom(oligoClasses, chromosome2integer, celfileDate, list.celfiles,

-           copyNumber, initializeBigMatrix, initializeBigVector, isPackageLoaded)

-

-

-importFrom(graphics, abline, axis, layout, legend, mtext, par, plot,

-           polygon, rect, segments, text, points, boxplot, lines)

-

-importFrom(lattice, xyplot, simpleKey, panel.grid, panel.xyplot, lrect, ltext,

-	   lpoints, panel.number, lpolygon)

-

-importFrom(grDevices, grey)

-importFrom(affyio, read.celfile.header, read.celfile)

-importFrom(preprocessCore, normalize.quantiles.use.target, normalize.quantiles)

-importFrom(utils, data, packageDescription, setTxtProgressBar, txtProgressBar)

-importFrom(stats, coef, cov, dnorm, kmeans, lm, mad, median, quantile, sd, update)

-importFrom(genefilter, rowSds)

-importFrom(mvtnorm, dmvnorm)

+importClassesFrom(Biobase, AssayData, eSet)

+

+importClassesFrom(methods, ANY, character, formula, integer, list,

+                  matrix, oldClass)

+importFrom(methods, setOldClass)

+

+## importClassesFrom(oligoClasses, CNSet, CNSetLM, ff_matrix,

+##                   ff_or_matrix, oligoSnpSet)

+importClassesFrom(oligoClasses, CNSet, oligoSnpSet, ff_or_matrix)

+##setOldClass(ff_or_matrix)

+

+importMethodsFrom(Biobase, annotatedDataFrameFrom, annotation,

+                  assayData, experimentData, featureData,

+                  "featureData<-", featureNames, "featureNames<-",

+                  pData, "pData<-", phenoData, "phenoData<-",

+                  protocolData, "protocolData<-", rowMedians,

+                  sampleNames, snpCall, "snpCall<-",

+                  snpCallProbability, "snpCallProbability<-",

+                  storageMode, "storageMode<-", varLabels)

+

+importMethodsFrom(BiocGenerics, cbind, colnames, Filter, get,

+                  intersect, lapply, ncol, NCOL, nrow, NROW, order,

+                  paste, pmax, pmin, rbind, rownames, sapply, setdiff,

+                  table, union, unique)

+

+importMethodsFrom(genefilter, show)

+

+importMethodsFrom(oligoClasses, A, "A<-", B, batch, batchNames,

+                  batchStatistics, "batchStatistics<-", calls,

+                  chromosome, close, confs, flags, isSnp, mean, nu,

+                  open, phi, "sampleNames<-")

+

+importFrom(affyio, read.celfile, read.celfile.header)

+

+importFrom(Biobase, assayDataElement, assayDataElementReplace,

+           assayDataNew, copyEnv)

+

 importFrom(ellipse, ellipse)

-##importFrom(ff, ffdf, physical.ff, physical.ffdf, ffrowapply)

+

+importFrom(genefilter, rowSds)

+

+importFrom(lattice, lpolygon, panel.grid, panel.number, panel.xyplot,

+           xyplot)

+

+importFrom(methods, "@<-", as, callNextMethod, is, new, validObject)

+

+importFrom(mvtnorm, rmvnorm)

+

+importFrom(oligoClasses, celfileDate, chromosome2integer, i2p,

+           initializeBigMatrix, initializeBigVector, integerMatrix,

+	   isPackageLoaded,

+           ldPath, ocLapply, ocProbesets, ocSamples,

+	   parStatus,

+           splitIndicesByLength, splitIndicesByNode)

+

+importFrom(preprocessCore, normalize.quantiles,

+           normalize.quantiles.use.target)

+

+importFrom(stats, coef, cov, dnorm, kmeans, lm, mad, median, quantile,

+           sd)

+

+importFrom(utils, packageDescription, setTxtProgressBar,

+           txtProgressBar)

 ##----------------------------------------------------------------------------

 ## export

@@ -1,4 +1,5 @@

 setOldClass("ellipse")

 setOldClass("ffdf")

+##setOldClass("ff_matrix")

 ##setClassUnion("ff_or_matrix", c("ffdf", "ff_matrix", "matrix"))

 setClass("PredictionRegion", contains="list")

deleted file mode 100644

@@ -1,117 +0,0 @@

-copynumber <- function(filenames,

-		       batch,

-		       summaries=c("lrr", "baf", "ca", "cb", "gt", "gtconf"),

-		       write=FALSE,

-		       outdir=".",

-		       onefile=FALSE,

-		       rda=TRUE){

-	fnamelist <- split(filenames, batch)

-	batchlist <- split(batch, batchlist)

-	## for each element in list.  Avoid

-	foreach(i=fnamelist) %do% simpleusage(filenames=fnameslist[[i]], batch=batchlist[[i]])

-}

-

-simpleusage <- function(filenames, batch, ...){

-	object <- genotype(fnamelist, batch, ...)

-	## run genotype on each element in fnamelist

-	object <- genotypeSummary(object)

-	## different than currently implemented

-	##    - shrink to saved batch medians, etc.

-	##    - shinkage across markers (?)

-	object <- shrinkSummary(object)

-	results <- array(NA, nrow(object), ncol(object), length(summaries))

-	if(c("ca", "cb") %in% summaries){

-		## estimateCnParameters

-		##results <- estimatecnParameters(...)

-	} else results <- NULL

-	is.baf <- "baf" %in% summaries || "lrr" %in% summaries

-	if(is.baf){

-		results2 <- calculateRtheta(object) ## returns list

-		## keep as list, or coerce to array

-	}

-	if(write){

-		##write2(, onefile=onefile)

-	}

-	return(results)

-}

-

-

-imputeTheta <- function(ia, ib, theta, S=100){

-##	y <- rbind(y1, y2)

-	y <- theta

-	N <- nrow(y); p <- ncol(y)

-

-	mu0 <- apply(y, 2, mean, na.rm=TRUE)

-	sd0 <- mu0/5

-	L0 <- matrix(.1, p, p); diag(L0) <- 1; L0 <- L0*outer(sd0, sd0)

-	nu0 <- p+2; S0 <- L0

-

-	## starting values

-	Sigma <- S0

-	Y.full <- y

-	O <- 1*(!is.na(y))

-	if(all(O[1, ] == 0)){

-		return(rep(NA, length(ia)))

-	}

-	for(j in seq_len(p)) Y.full[is.na(Y.full[, j]), j] <- mu0[j]

-	## Gibbs sampler

-	##THETA <- SIGMA <- Y.MISS <- NULL

-	## problems:  approx. 90 observations for the means in the other batches

-	##   -- only 3 observations for the mean in the current batch.

-##	THETA <- matrix(NA, iter, p)

-##	SIGMA <- matrix(NA, iter, p^2)

-	Y.MISS <- matrix(NA, S, sum(O[1,]==0))

-	bafs <- matrix(NA, S, length(a))

-	for(s in seq_len(S)){

-		## update lambda

-		ybar <- apply(Y.full, 2, mean)

-		Ln <- solve(solve(L0) + N*solve(Sigma))

-		mun <- Ln%*%(solve(L0)%*%mu0 + N*solve(Sigma)%*%ybar)

-		lambda <- rmvnorm(1, mun, Ln)

-

-		##update sigma

-		Sn <- S0+(t(Y.full)-c(lambda))%*%t(t(Y.full)-c(lambda))

-		Sigma <- solve(rwish(nu0+N, solve(Sn)))

-

-		## update missing data (only care about the first row.)

-		a <- O[1, ] == 1

-		b <- O[1, ] == 0

-		iSa <- solve(Sigma[a,a])

-		beta.j <- Sigma[b,a]%*%iSa

-		Sigma.j <- Sigma[b,b]-Sigma[b,a]%*%iSa%*%Sigma[a,b]

-		lambda.j <- lambda[b]+beta.j%*%(t(Y.full[1,a, drop=FALSE]-lambda[a]))

-		Y.full[1,b] <- rmvnorm(1, lambda.j, Sigma.j)

-		##SIGMA[s, ] <- c(Sigma)

-		Y.MISS[s, ] <- Y.full[1, O[1, ]==0]

-	}

-	na.cols <- which(is.na(y[1, ]))

-	THETA <- matrix(NA, S, 3)

-	THETA[, na.cols] <- Y.MISS

-	if(length(na.cols) < length(ia))

-		THETA[, -na.cols] <- y[1, -na.cols]

-

-	obs.theta <- atan2(ib, ia)*2/pi

-	theta.aa <- matrix(THETA[, 1], S, 3)

-	theta.ab <- matrix(THETA[, 2], S, 3)

-	theta.bb <- matrix(THETA[, 3], S, 3)

-	lessAA <- obs.theta < theta.aa

-	lessAB <- obs.theta < theta.ab

-	lessBB <- obs.theta < theta.bb

-	grAA <- !lessAA ## >= theta.aa

-	grAB <- !lessAB ## >= theta.ab

-	grBB <- !lessBB ## >= theta.bb

-	##not.na <- !is.na(theta.aa)

-	I1 <- grAA & lessAB

-	I2 <- grAB & lessBB

-	##mu <- apply(Y.MISS, 2, mean)

-	##bf <- matrix(NA, S, length(ia))

-	bf <- rep(NA, S*length(ia))

-	I1 <- as.logical(I1); I2 <- as.logical(I2)

-	bf[I1] <- 0.5 * as.numeric(((obs.theta-theta.aa)/(theta.ab-theta.aa)))[I1]

-	bf[I2] <- as.numeric((.5 * (obs.theta - theta.ab) / (theta.bb - theta.ab)))[I2] + 0.5

-	bf[as.logical(lessAA)] <- 0

-	bf[as.logical(grBB)] <- 1

-	bf <- matrix(bf, S, 3, byrow=TRUE)

-	pm.bf <- apply(bf, 2, mean)

-	return(pm.bf)

-}

@@ -1896,85 +1896,85 @@ isCall <- function(G, call){

 	G.call

 }

-computeSummary <- function(object, G.call, call, I, allele, Ns, index){

-	k <- match("grandMean", batchNames(object))

-	stats <- summaryStats(G.call, I, FUNS=c("rowMedians", "rowMAD"))

-	Ns[, k] <- rowSums(G.call, na.rm=TRUE)

-	updateStats(stats, Ns, object, call, allele, index)

-	gc()

-	return()

-}

+##computeSummary <- function(object, G.call, call, I, allele, Ns, index){

+##	k <- match("grandMean", batchNames(object))

+##	stats <- summaryStats(G.call, I, FUNS=c("rowMedians", "rowMAD"))

+##	Ns[, k] <- rowSums(G.call, na.rm=TRUE)

+##	updateStats(stats, Ns, object, call, allele, index)

+##	gc()

+##	return()

+##}

-updateTau <- function(object, tau2, G.call, call, I, allele, index){

-	k <- match("grandMean", batchNames(object))

-	logI <- log2(I)

-	rm(I); gc()

-	tau2[, k] <- summaryStats(G.call, logI, FUNS="rowMAD")^2

-	if(call==1 & allele=="A"){

-		tau2A.AA(object)[index, ] <- tau2

-	}

-	if(call==1 & allele=="B"){

-		tau2B.AA(object)[index, ] <- tau2

-	}

-	if(call==3 & allele=="A"){

-		tau2A.BB(object)[index, ] <- tau2

-	}

-	if(call==3 & allele=="B"){

-		tau2B.BB(object)[index, ] <- tau2

-	}

-	NULL

-}

+##updateTau <- function(object, tau2, G.call, call, I, allele, index){

+##	k <- match("grandMean", batchNames(object))

+##	logI <- log2(I)

+##	rm(I); gc()

+##	tau2[, k] <- summaryStats(G.call, logI, FUNS="rowMAD")^2

+##	if(call==1 & allele=="A"){

+##		tau2A.AA(object)[index, ] <- tau2

+##	}

+##	if(call==1 & allele=="B"){

+##		tau2B.AA(object)[index, ] <- tau2

+##	}

+##	if(call==3 & allele=="A"){

+##		tau2A.BB(object)[index, ] <- tau2

+##	}

+##	if(call==3 & allele=="B"){

+##		tau2B.BB(object)[index, ] <- tau2

+##	}

+##	NULL

+##}

-updateCors <- function(cors, G.call, I){

-	k <- match("grandMean", batchNames(object))

-	cors[, k] <- rowCors(I[[1]]*G.call, I[[2]]*G.call, na.rm=TRUE)

-	if(call==1){

-		corrAA(object)[index, ] <- cors

-	}

-	if(call==2){

-		corrAB(object)[index, ] <- cors

-	}

-	if(call==3){

-		corrBB(object)[index, ] <- cors

-	}

-}

+##updateCors <- function(cors, G.call, I){

+##	k <- match("grandMean", batchNames(object))

+##	cors[, k] <- rowCors(I[[1]]*G.call, I[[2]]*G.call, na.rm=TRUE)

+##	if(call==1){

+##		corrAA(object)[index, ] <- cors

+##	}

+##	if(call==2){

+##		corrAB(object)[index, ] <- cors

+##	}

+##	if(call==3){

+##		corrBB(object)[index, ] <- cors

+##	}

+##}

-updateStats <- function(stats, Ns, object, call, allele, tau2, index){

-	if(call==1){

-		Ns.AA(object)[index, ] <- Ns

-		if(allele=="A"){

-			medianA.AA(object)[index, k] <- stats[, 1]

-			madA.AA(object)[index, k] <- stats[, 2]

-			updateTau(object, tau2, G.call, call, I, allele, index)

-		} else {

-			medianB.AA(object)[index, k] <- stats[, 1]

-			madB.AA(object)[index, k] <- stats[, 2]

-			updateTau(object, tau2, G.call, call, I, allele, index)

-		}

-	}

-	if(call==2){

-		Ns.AB(object)[index, ] <- Ns

-		if(allele=="A"){

-			medianA.AB(object)[index, k] <- stats[, 1]

-			madA.AB(object)[index, k] <- stats[, 2]

-		} else {

-			medianB.AB(object)[index, k] <- stats[, 1]

-			madB.AB(object)[index, k] <- stats[, 2]

-		}

-	}

-	if(call==3){

-		Ns.BB(object)[index, ] <- Ns

-		if(allele=="A"){

-			medianA.BB(object)[index, k] <- stats[, 1]

-			madA.BB(object)[index, k] <- stats[, 2]

-			updateTau(object, tau2, G.call, call, I, allele, index)

-		} else {

-			medianB.BB(object)[index, k] <- stats[, 1]

-			madB.BB(object)[index, k] <- stats[, 2]

-			updateTau(object, tau2, G.call, call, I, allele, index)

-		}

-	}

-}

+##updateStats <- function(stats, Ns, object, call, allele, tau2, index){

+##	if(call==1){

+##		Ns.AA(object)[index, ] <- Ns

+##		if(allele=="A"){

+##			medianA.AA(object)[index, k] <- stats[, 1]

+##			madA.AA(object)[index, k] <- stats[, 2]

+##			updateTau(object, tau2, G.call, call, I, allele, index)

+##		} else {

+##			medianB.AA(object)[index, k] <- stats[, 1]

+##			madB.AA(object)[index, k] <- stats[, 2]

+##			updateTau(object, tau2, G.call, call, I, allele, index)

+##		}

+##	}

+##	if(call==2){

+##		Ns.AB(object)[index, ] <- Ns

+##		if(allele=="A"){

+##			medianA.AB(object)[index, k] <- stats[, 1]

+##			madA.AB(object)[index, k] <- stats[, 2]

+##		} else {

+##			medianB.AB(object)[index, k] <- stats[, 1]

+##			madB.AB(object)[index, k] <- stats[, 2]

+##		}

+##	}

+##	if(call==3){

+##		Ns.BB(object)[index, ] <- Ns

+##		if(allele=="A"){

+##			medianA.BB(object)[index, k] <- stats[, 1]

+##			madA.BB(object)[index, k] <- stats[, 2]

+##			updateTau(object, tau2, G.call, call, I, allele, index)

+##		} else {

+##			medianB.BB(object)[index, k] <- stats[, 1]

+##			madB.BB(object)[index, k] <- stats[, 2]

+##			updateTau(object, tau2, G.call, call, I, allele, index)

+##		}

+##	}

+##}

 crlmmCopynumber <- function(object,

 			    MIN.SAMPLES=10,

@@ -14,7 +14,7 @@ getCor <- function(object, gt){

 getTau2 <- function(object, gt){

 	bs <- batchStatistics(object)

 	nma <- paste("tau2A", gt, sep=".")

-	nma <- paste("tau2B", gt, sep=".")

+	nmb <- paste("tau2B", gt, sep=".")

 	tau2.a <- assayDataElement(bs, nma)

 	tau2.b <- assayDataElement(bs, nmb)

 	cbind(tau2.a, tau2.b)

@@ -11,6 +11,8 @@ cnSet2oligoSnpSet <- function(object){

 	is.lds <- ifelse(is(calls(object), "ff_matrix") | is(calls(object), "ffdf"), TRUE, FALSE)

 	if(is.lds) stopifnot(isPackageLoaded("ff"))

 	b.r <- calculateRBaf(object)

+	b <- integerMatrix(b.r[[1]], 1000)

+	r <- integerMatrix(b.r[[2]], 100)

 ##	if(is.lds){

 ##		## initialize a big matrix for raw copy number

 ##		message("creating an ff object for storing total copy number")

@@ -438,36 +440,36 @@ setMethod("calculatePosteriorMean", signature(object="CNSet"),

 		  return(pm)

 	  })

-		  .bivariateCenter <- function(nu, phi){

-			  ##  lexical scope for mus, CA, CB

-			  if(CA <= 2 & CB <= 2 & (CA+CB) < 4){

-				  mus[,1, ] <- log2(nu[, 1, ] + CA *

-						    phi[, 1, ])

-				  mus[,2, ] <- log2(nu[, 2, ] + CB *

-						    phi[, 2, ])

-			  } else { ## CA > 2

-				  if(CA > 2){

-					  theta <- pi/4*Sigma[,2,]

-					  shiftA <- CA/4*phi[, 1, ] * cos(theta)

-					  shiftB <- CA/4*phi[, 1, ] * sin(theta)

-					  mus[, 1, ] <- log2(nu[, 1, ] + 2 * phi[,1,]+shiftA)

-					  mus[, 2, ] <- log2(nu[, 2, ] + CB *phi[,2,]+shiftB)

-				  }

-				  if(CB > 2){

-					  ## CB > 2

-					  theta <- pi/2-pi/4*Sigma[,2,]

-					  shiftA <- CB/4*phi[, 2, ] * cos(theta)

-					  shiftB <- CB/4*phi[, 2, ] * sin(theta)

-					  mus[, 1, ] <- log2(nu[, 1, ] + CA*phi[,1,]+shiftA)

-					  mus[, 2, ] <- log2(nu[, 2, ]+ 2*phi[,2,]+shiftB)

-				  }

-				  if(CA == 2 & CB == 2){

-					  mus[, 1, ] <- log2(nu[, 1, ] + 1/2*CA*phi[,1,])

-					  mus[, 2, ] <- log2(nu[, 2, ]+ 1/2*CB*phi[,2,])

-				  }

-			  }

-			  mus

-		  }

+##.bivariateCenter <- function(nu, phi){

+##			  ##  lexical scope for mus, CA, CB

+##			  if(CA <= 2 & CB <= 2 & (CA+CB) < 4){

+##				  mus[,1, ] <- log2(nu[, 1, ] + CA *

+##						    phi[, 1, ])

+##				  mus[,2, ] <- log2(nu[, 2, ] + CB *

+##						    phi[, 2, ])

+##			  } else { ## CA > 2

+##				  if(CA > 2){

+##					  theta <- pi/4*Sigma[,2,]

+##					  shiftA <- CA/4*phi[, 1, ] * cos(theta)

+##					  shiftB <- CA/4*phi[, 1, ] * sin(theta)

+##					  mus[, 1, ] <- log2(nu[, 1, ] + 2 * phi[,1,]+shiftA)

+##					  mus[, 2, ] <- log2(nu[, 2, ] + CB *phi[,2,]+shiftB)

+##				  }

+##				  if(CB > 2){

+##					  ## CB > 2

+##					  theta <- pi/2-pi/4*Sigma[,2,]

+##					  shiftA <- CB/4*phi[, 2, ] * cos(theta)

+##					  shiftB <- CB/4*phi[, 2, ] * sin(theta)

+##					  mus[, 1, ] <- log2(nu[, 1, ] + CA*phi[,1,]+shiftA)

+##					  mus[, 2, ] <- log2(nu[, 2, ]+ 2*phi[,2,]+shiftB)

+##				  }

+##				  if(CA == 2 & CB == 2){

+##					  mus[, 1, ] <- log2(nu[, 1, ] + 1/2*CA*phi[,1,])

+##					  mus[, 2, ] <- log2(nu[, 2, ]+ 1/2*CB*phi[,2,])

+##				  }

+##			  }

+##			  mus

+##		  }

 ## for a given copy number, return a named list of bivariate normal prediction regions

 ##   - elements of list are named by genotype

@@ -214,7 +214,7 @@ setMethod("annotatedDataFrameFrom", "ffdf", Biobase:::annotatedDataFrameFromMatr

 ## Document this...

 getBAF <- function(theta, canonicalTheta)

-    .Call('normalizeBAF', theta, ct)

+    .Call('normalizeBAF', theta, canonicalTheta)

 validCEL <- function(celfiles){

Binary files a/data/cnSetExample.rda and b/data/cnSetExample.rda differ

@@ -31,6 +31,7 @@ R. Scharpf

 }

 \examples{

+library(oligoClasses)

 if(require(hapmapsnp6)){

   path <- system.file("celFiles", package="hapmapsnp6")

   cels <- list.celfiles(path, full.names=TRUE)

@@ -11,7 +11,9 @@

 }

 \details{

-  This object was created from the copynumber vignette in inst/scripts.

+  This object was created from the copynumber vignette in

+  inst/scripts. A subset of markers was selected to keep the package

+  size small.

 }

 \usage{

@@ -82,6 +82,7 @@ crlmm2(filenames, row.names=TRUE, col.names=TRUE,

 }

 \examples{

 ## this can be slow

+library(oligoClasses)

 if (require(genomewidesnp6Crlmm) & require(hapmapsnp6)){

   path <- system.file("celFiles", package="hapmapsnp6")

@@ -34,6 +34,7 @@ R. Scharpf

 }

 \examples{

+library(oligoClasses)

 data(cnSetExample2)

 table(batch(cnSetExample2))

 sample.index <- which(batch(cnSetExample2) == "CUPID")