@@ -73,7 +73,7 @@ export(crlmm,

        crlmmCopynumber2, crlmmCopynumberLD)

 export(constructIlluminaCNSet)

 export(linesCNSetLM)

-export(linearModel.noweights, computeCN)

+export(linearModel.noweights, computeCN, fit.lm1, fit.lm2, fit.lm3, fit.lm4, construct)

@@ -49,22 +49,24 @@ getFeatureData.Affy <- function(cdfName, copynumber=FALSE){

 	##A few of the snpProbes do not match -- I think it is chromosome Y.

 	M[is.na(M[, "isSnp"]), "isSnp"] <- 1L

 	return(new("AnnotatedDataFrame", data=data.frame(M)))

-	##list(snpIndex, npIndex, fns)

-	##crlmmOpts$snpRange <- range(snpIndex)

-	##crlmmOpts$npRange <- range(npIndex)

 }

 construct <- function(filenames, cdfName, copynumber=FALSE,

 		      sns, verbose=TRUE, batch, fns){

+	if(!missing(batch)){

+		stopifnot(length(batch) == length(sns))

+	}

+	if(missing(sns) & missing(filenames)) stop("one of filenames or samplenames (sns) must be provided")

 	if(verbose) message("Initializing container for assay data elements alleleA, alleleB, call, callProbability, CA, CB")

-	if(missing(sns)) sns <- basename(filenames)

-	protocolData <- getProtocolData.Affy(filenames)

-	if(missing(batch)){

+	if(!missing(filenames)){

+		if(missing(sns)) sns <- basename(filenames)

+		protocolData <- getProtocolData.Affy(filenames)

 		protocolData$batch <- as.numeric(as.factor(protocolData$ScanDate))

-	} else 	{

-		if(length(batch) != length(filenames))

-			stop("batch variable must be the same length as the filenames")

-		protocolData$batch <- batch

+	} else{

+		protocolData <- new("AnnotatedDataFrame",

+				    data=data.frame(batch=batch),

+				    varMetadata=data.frame(labelDescription="batch",

+				    row.names="batch"))

 	}

 	rownames(pData(protocolData)) <- sns

 	featureData <- getFeatureData.Affy(cdfName, copynumber=copynumber)

@@ -73,7 +75,7 @@ construct <- function(filenames, cdfName, copynumber=FALSE,

 		if(all(is.na(index))) stop("fns not in featureNames")

 		featureData <- featureData[index, ]

 	}

-	nr <- nrow(featureData); nc <- length(filenames)

+	nr <- nrow(featureData); nc <- length(sns)

 	ffObjects <- list(alleleA=initializeBigMatrix(name="A", nr, nc),

 			  alleleB=initializeBigMatrix(name="B", nr, nc),

 			  call=initializeBigMatrix(name="call", nr, nc),

@@ -99,8 +101,6 @@ construct <- function(filenames, cdfName, copynumber=FALSE,

 	return(callSet)

 }

-

-

 genotype <- function(filenames,

 		     cdfName,

 		     batch,

@@ -313,364 +313,6 @@ genotypeLD <- function(filenames,

 }

 genotype2 <- genotypeLD

-

-genotype3 <- function(filenames,

-		      cdfName,

-		      batch,

-		      mixtureSampleSize=10^5,

-		      eps=0.1,

-		      verbose=TRUE,

-		      seed=1,

-		      sns,

-		      copynumber=FALSE,

-		      probs=rep(1/3, 3),

-		      DF=6,

-		      SNRMin=5,

-		      recallMin=10,

-		      recallRegMin=1000,

-		      gender=NULL,

-		      returnParams=TRUE,

-		      badSNP=0.7){

-	if(!isPackageLoaded("ff")) stop("Must load package 'ff'")

-	if(!copynumber){

-		callSet <- crlmm2(filenames=filenames,

-				  cdfName=cdfName,

-				  mixtureSampleSize=mixtureSampleSize,

-				  eps=eps,

-				  verbose=verbose,

-				  sns=sns,

-				  probs=probs,

-				  DF=DF,

-				  SNRMin=SNRMin,

-				  recallMin=recallMin,

-				  recallRegMin=recallRegMin,

-				  gender=gender,

-				  returnParams=returnParams,

-				  badSNP=badSNP)

-		return(callSet)

-	}

-	if(missing(cdfName)) stop("must specify cdfName")

-	if(!isValidCdfName(cdfName)) stop("cdfName not valid.  see validCdfNames")

-	if(missing(batch)){

-		warning("The batch variable is not specified. The scan date of the array will be used as a surrogate for batch.  The batch variable does not affect the preprocessing or genotyping, but is important for copy number estimation.")

-	} else {

-		if(length(batch) != length(filenames))

-			stop("batch variable must be the same length as the filenames")

-	}

-	if(missing(sns)) sns <- basename(filenames)

-	## callSet contains potentially very big matrices

-	callSet <- construct(filenames=filenames,

-			     cdfName=cdfName,

-			     copynumber=TRUE,

-			     sns=sns,

-			     verbose=verbose)

-	if(missing(batch)){

-		protocolData(callSet)$batch <- as.numeric(as.factor(protocolData(callSet)$ScanDate))

-	}

-	if(!missing(batch)) protocolData(callSet)$batch <- batch

-	##lM(callSet) <- initializeParamObject(list(featureNames(callSet), unique(protocolData(callSet)$batch)))

-	mixtureParams <- matrix(NA, 4, length(filenames))

-	snp.index <- which(isSnp(callSet)==1)

-##	snprmaRes <- snprma2(filenames=filenames,

-##			       mixtureSampleSize=mixtureSampleSize,

-##			       fitMixture=TRUE,

-##			       eps=eps,

-##			       verbose=verbose,

-##			       seed=seed,

-##			       cdfName=cdfName,

-##			       sns=sns)

-##	if(verbose) message("Finished preprocessing.")

-##	open(snprmaRes[["A"]])

-##	open(snprmaRes[["B"]])

-##	open(snprmaRes[["SNR"]])

-##	open(snprmaRes[["SKW"]])

-##	open(snprmaRes[["mixtureParams"]])

-##	if(verbose) message("Updating elements of callSet")

-####	bb = ocProbesets()*ncol(A)*8

-####	ffrowapply(A(callSet)[i1:i2, ] <- snprmaRes[["A"]][i1:i2, ], X=snprmaRes[["A"]], BATCHBYTES=bb)

-####	ffrowapply(B(callSet)[i1:i2, ] <- snprmaRes[["B"]][i1:i2, ], X=snprmaRes[["B"]], BATCHBYTES=bb)

-##	##batches <- splitIndicesByLength(1:nrow(snprmaRes[["A"]]), ocProbesets())

-##	for(j in 1:ncol(callSet)){

-##		A(callSet)[snp.index, j] <- snprmaRes[["A"]][, j]

-##		B(callSet)[snp.index, j] <- snprmaRes[["B"]][, j]

-##	}

-##	if(verbose) message("Finished updating elements of callSet")

-##	stopifnot(identical(featureNames(callSet)[snp.index], snprmaRes$gns))

-##	pData(callSet)$SKW <- snprmaRes$SKW

-##	pData(callSet)$SNR <- snprmaRes$SNR

-##	mixtureParams <- snprmaRes$mixtureParams

-	np.index <- which(isSnp(callSet) == 0)

-	cnrmaRes <- cnrma2(A=A(callSet),

-			   filenames=filenames,

-			   row.names=featureNames(callSet)[np.index],

-			   cdfName=cdfName,

-			   sns=sns,

-			   seed=seed,

-			   verbose=verbose)

-##	if(verbose) message("Entering crlmmGT2...")

-##	rm(cnrmaRes); gc()

-##	## as.matrix needed when ffdf is used

-##	tmp <- crlmmGT2(A=snprmaRes[["A"]],

-##			B=snprmaRes[["B"]],

-##			SNR=snprmaRes[["SNR"]],

-##			mixtureParams=snprmaRes[["mixtureParams"]],

-##			cdfName=cdfName,

-##			row.names=NULL, ##featureNames(callSet),##[snp.index],

-##			col.names=sampleNames(callSet),

-##			probs=probs,

-##			DF=DF,

-##			SNRMin=SNRMin,

-##			recallMin=recallMin,

-##			recallRegMin=recallRegMin,

-##			gender=gender,

-##			verbose=verbose,

-##			returnParams=returnParams,

-##			badSNP=badSNP)

-##	if(verbose) message("Leaving crlmmGT2")

-##	open(tmp[["calls"]])

-##	open(tmp[["confs"]])

-##	##bb = ocProbesets()*ncol(A)*8

-##	for(j in 1:ncol(callSet)){

-##		snpCall(callSet)[snp.index, j] <- tmp[["calls"]][, j]

-##		snpCallProbability(callSet)[snp.index, j] <- tmp[["confs"]][, j]

-##	}

-####	ffrowapply(snpCall(callSet)[i1:i2, ] <- tmp[["calls"]][i1:i2, ], X=tmp[["calls"]], BATCHBYTES=bb)

-####	ffrowapply(snpCallProbability(callSet)[i1:i2, ] <- tmp[["confs"]][i1:i2, ], X=tmp[["confs"]], BATCHBYTES=bb)

-##	callSet$gender <- tmp$gender

-####	cnSet <- as(callSet, "CNSetLM")

-##	return(callSet)

-	return(cnrmaRes)

-}

-

-

-

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

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

-## For Illumina

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

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

-##getPhenoData <- function(sampleSheet=NULL, arrayNames=NULL,

-##			 arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A")){

-##	if(!is.null(arrayNames)) {

-##		pd = new("AnnotatedDataFrame", data = data.frame(Sample_ID=arrayNames))

-##	}

-##	if(!is.null(sampleSheet)) { # get array info from Illumina's sample sheet

-##		if(is.null(arrayNames)){

-##			##arrayNames=NULL

-##			if(!is.null(arrayInfoColNames$barcode) && (arrayInfoColNames$barcode %in% colnames(sampleSheet))) {

-##				barcode = sampleSheet[,arrayInfoColNames$barcode]

-##				arrayNames=barcode

-##			}

-##			if(!is.null(arrayInfoColNames$position) && (arrayInfoColNames$position %in% colnames(sampleSheet))) {  

-##				position = sampleSheet[,arrayInfoColNames$position]

-##				if(is.null(arrayNames))

-##					arrayNames=position

-##				else

-##					arrayNames = paste(arrayNames, position, sep=sep)

-##				if(highDensity) {

-##					hdExt = list(A="R01C01", B="R01C02", C="R02C01", D="R02C02")

-##					for(i in names(hdExt))

-##						arrayNames = sub(paste(sep, i, sep=""), paste(sep, hdExt[[i]], sep=""), arrayNames)

-##				}

-##			}

-##		}

-##		pd = new("AnnotatedDataFrame", data = sampleSheet)

-##		sampleNames(pd) <- basename(arrayNames)               

-##	}

-##	if(is.null(arrayNames)) {

-##		arrayNames = gsub(paste(sep, fileExt$green, sep=""), "", dir(pattern=fileExt$green, path=path))

-##		if(!is.null(sampleSheet)) {

-##			sampleSheet=NULL

-##			cat("Could not find required info in \'sampleSheet\' - ignoring.  Check \'sampleSheet\' and/or \'arrayInfoColNames\'\n")

-##		}

-##		pd = new("AnnotatedDataFrame", data = data.frame(Sample_ID=arrayNames))

-##	}

-##	return(pd)

-##}

-##constructRG <- function(filenames, cdfName, sns, verbose, fileExt, sep, sampleSheet, arrayInfoColNames){

-##	if(verbose)	message("reading first idat file to extract feature data")

-##	grnfile <- paste(filenames[1], fileExt$green, sep=sep)

-##	if(!file.exists(grnfile)){

-##                stop(paste(grnfile, " does not exist. Check fileExt argument"))

-##        }

-##        G <- readIDAT(grnfile)

-##        idsG = rownames(G$Quants)

-##        nr <- length(idsG)

-##	fD <- new("AnnotatedDataFrame", data=data.frame(row.names=idsG))##, varMetadata=data.frame(labelDescript

-##	nr <- nrow(fD)

-##	dns <- list(featureNames(fD), basename(filenames))

-##	RG <- new("NChannelSet",

-##		  R=initializeBigMatrix(name="R", nr=nr, nc=length(filenames)),

-##		  G=initializeBigMatrix(name="G", nr=nr, nc=length(filenames)),

-##		  zero=initializeBigMatrix(name="zero", nr=nr, nc=length(filenames)),

-##		  featureData=fD,

-##		  annotation=cdfName)

-##	phenoData(RG) <- getPhenoData(sampleSheet=sampleSheet, arrayNames=filenames,

-##				      arrayInfoColNames=arrayInfoColNames)

-####	pD <- data.frame(matrix(NA, length(sampleNames(RG)), 12), row.names=sampleNames(RG))

-####	colnames(pD) <- c("Index","HapMap.Name","Name","ID",

-####			  "Gender", "Plate", "Well", "Group", "Parent1",

-####			  "Parent2","Replicate","SentrixPosition")

-##	##phenoData(RG) <- new("AnnotatedDataFrame", data=pD)

-##	pD <- data.frame(matrix(NA, length(sampleNames(RG)), 1), row.names=sampleNames(RG))

-##	colnames(pD) <- "ScanDate"

-##	protocolData(RG) <- new("AnnotatedDataFrame", data=pD)

-##	sampleNames(RG) <- basename(filenames)

-##	storageMode(RG) <- "environment"

-##	RG##featureData=ops$illuminaOpts[["featureData"]])

-##}

-##crlmmIlluminaRS <- function(sampleSheet=NULL,

-##			    arrayNames=NULL,

-##			    batch,

-##			    ids=NULL,

-##			    path=".",

-##			    arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"),

-##			    highDensity=FALSE,

-##			    sep="_",

-##			    fileExt=list(green="Grn.idat", red="Red.idat"),

-##			    stripNorm=TRUE,

-##			    useTarget=TRUE,

-##			    row.names=TRUE, 

-##			    col.names=TRUE,

-##			    probs=c(1/3, 1/3, 1/3), DF=6, SNRMin=5, gender=NULL,

-##			    seed=1, save.ab=FALSE, snpFile, cnFile,

-##			    mixtureSampleSize=10^5, eps=0.1, verbose=TRUE,

-##			    cdfName, sns, recallMin=10, recallRegMin=1000,

-##			    returnParams=FALSE, badSNP=.7,

-##			    copynumber=FALSE,

-##			    load.it=TRUE) {

-##	if(missing(cdfName)) stop("must specify cdfName")

-##	if(!isValidCdfName(cdfName)) stop("cdfName not valid.  see validCdfNames")

-##	if(missing(sns)) sns <- basename(arrayNames)

-##	if(missing(batch)){

-##		warning("The batch variable is not specified. The scan date of the array will be used as a surrogate for batch.  The batch variable does not affect the preprocessing or genotyping, but is important for copy number estimation.")

-##	} else {

-##		if(length(batch) != length(sns))

-##			stop("batch variable must be the same length as the filenames")

-##	}	

-##	batches <- splitIndicesByLength(seq(along=arrayNames), ocSamples())

-##	k <- 1

-##	for(j in batches){

-##		if(verbose) message("Batch ", k, " of ", length(batches))

-##		RG <- readIdatFiles(sampleSheet=sampleSheet[j, ],

-##				     arrayNames=arrayNames[j],

-##				     ids=ids,

-##				     path=path,

-##				     arrayInfoColNames=arrayInfoColNames,

-##				     highDensity=highDensity,

-##				     sep=sep,

-##				     fileExt=fileExt,

-##				     saveDate=TRUE)

-##		RG <- RGtoXY(RG, chipType=cdfName)

-##		protocolData <- protocolData(RG)

-##		res <- preprocessInfinium2(RG,

-##					   mixtureSampleSize=mixtureSampleSize,

-##					   fitMixture=TRUE,

-##					   verbose=verbose,

-##					   seed=seed,

-##					   eps=eps,

-##					   cdfName=cdfName,

-##					   sns=sns[j],

-##					   stripNorm=stripNorm,

-##					   useTarget=useTarget)

-##		rm(RG); gc()

-##		## MR: number of rows should be number of SNPs + number of nonpolymorphic markers.

-##		##  Here, I'm just using the # of rows returned from the above function

-##		if(k == 1){

-##			if(verbose) message("Initializing container for alleleA, alleleB, call, callProbability")

-##			callSet <- new("SnpSuperSet",

-##				       alleleA=initializeBigMatrix(name="A", nr=nrow(res[[1]]), nc=length(sns)),

-##				       alleleB=initializeBigMatrix(name="B", nr=nrow(res[[1]]), nc=length(sns)),

-##				       call=initializeBigMatrix(name="call", nr=nrow(res[[1]]), nc=length(sns)),

-##				       callProbability=initializeBigMatrix(name="callPr", nr=nrow(res[[1]]), nc=length(sns)),

-##				       annotation=cdfName)

-##			sampleNames(callSet) <- sns

-##			phenoData(callSet) <- getPhenoData(sampleSheet=sampleSheet,

-##							   arrayNames=sns,

-##							   arrayInfoColNames=arrayInfoColNames)

-##			pD <- data.frame(matrix(NA, length(sns), 1), row.names=sns)

-##			colnames(pD) <- "ScanDate"

-##			protocolData(callSet) <- new("AnnotatedDataFrame", data=pD)

-##			pData(protocolData(callSet))[j, ] <- pData(protocolData)

-##			featureNames(callSet) <- res[["gns"]]

-##			pData(callSet)$SNR <- initializeBigVector("crlmmSNR-", length(sns), "double")

-##			pData(callSet)$SKW <- initializeBigVector("crlmmSKW-", length(sns), "double")

-##			##pData(callSet)$SKW <- rep(NA, length(sns))

-##			##pData(callSet)$SNR <- rep(NA, length(sns))

-##			pData(callSet)$gender <- rep(NA, length(sns))

-##			mixtureParams <- initializeBigMatrix("crlmmMixt-", nr=4, nc=ncol(callSet), vmode="double")

-##			save(mixtureParams, file=file.path(ldPath(), "mixtureParams.rda"))

-##			if(missing(batch)){

-##				protocolData(callSet)$batch <- rep(NA, length(sns))

-##			} else{

-##				protocolData(callSet)$batch <- batch

-##			}

-##			featureData(callSet) <- addFeatureAnnotation(callSet)

-##			open(mixtureParams)

-##			open(callSet$SNR)

-##			open(callSet$SKW)

-##		}

-##		if(k > 1 & nrow(res[[1]]) != nrow(callSet)){

-##			##RS: I don't understand why the IDATS for the

-##			##same platform potentially have different lengths

-##			res[["A"]] <- res[["A"]][res$gns %in% featureNames(callSet), ]

-##			res[["B"]] <- res[["B"]][res$gns %in% featureNames(callSet), ]

-##		}

-##		if(missing(batch)){

-##			protocolData(callSet)$batch[j] <- as.numeric(as.factor(protocolData$ScanDate))

-##		}

-##		## MR: we need to define a snp.index vs np.index

-##		snp.index <- match(res$gns, featureNames(callSet))		

-##		A(callSet)[snp.index, j] <- res[["A"]]

-##		B(callSet)[snp.index, j] <- res[["B"]]

-##		pData(callSet)$SKW[j] <- res$SKW

-##		pData(callSet)$SNR[j] <- res$SNR

-##		mixtureParams[, j] <- res$mixtureParams

-##		rm(res); gc()

-##		k <- k+1

-##	}

-##	save(callSet, file=file.path(ldPath(), "callSet.rda"))

-##	##otherwise, A and B get overwritten

-##	##AA <- initializeBigMatrix("crlmmA", nrow(callSet), ncol(callSet), "integer")

-##	##BB <- initializeBigMatrix("crlmmB", nrow(callSet), ncol(callSet), "integer")

-##	##bb = ocProbesets()*ncol(A)*8

-##	AA <- clone(A(callSet))

-##	BB <- clone(B(callSet))

-##	##ffrowapply(AA[i1:i2, ] <- A(callSet)[i1:i2, ], X=A(callSet), BATCHBYTES=bb)

-##	##ffrowapply(BB[i1:i2, ] <- B(callSet)[i1:i2, ], X=B(callSet), BATCHBYTES=bb)

-##	##crlmmGT2 overwrites A and B.

-##	tmp <- crlmmGT2(A=A(callSet),

-##			B=B(callSet),

-##			SNR=callSet$SNR,

-##			mixtureParams=mixtureParams,

-##			cdfName=annotation(callSet),

-##			row.names=featureNames(callSet),

-##			col.names=sampleNames(callSet),

-##			probs=probs,

-##			DF=DF,

-##			SNRMin=SNRMin,

-##			recallMin=recallMin,

-##			recallRegMin=recallRegMin,

-##			gender=gender,

-##			verbose=verbose,

-##			returnParams=returnParams,

-##			badSNP=badSNP)

-##	open(tmp[["calls"]])

-##	open(tmp[["confs"]])

-##	A(callSet) <- AA

-##	B(callSet) <- BB

-##	snpCall(callSet) <- tmp[["calls"]]

-##	## MR: many zeros in the conf. scores (?)

-##	snpCallProbability(callSet) <- tmp[["confs"]]

-##	callSet$gender <- tmp$gender

-##	if(copynumber) cnSet <- as(callSet, "CNSetLM")

-##	close(mixtureParams)

-##	rm(tmp); gc()

-##	return(cnSet)

-##}

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

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

-

 rowCovs <- function(x, y, ...){

 	notna <- !is.na(x)

 	N <- rowSums(notna)

@@ -706,8 +348,6 @@ applyByGenotype <- function(x, FUN, G){

 	tmp

 }

-

-

 rowCors <- function(x, y, ...){

 	N <- rowSums(!is.na(x))

 	x <- suppressWarnings(log2(x))

@@ -751,12 +391,13 @@ nuphiAllele2 <- function(allele, Ystar, W, Ns){

 	ses <- matrix(NA, 2, nrow(Ystar))

 	for(i in 1:nrow(Ystar)){

 		betahat[, i] <- crossprod(matrix(IXTX[, i], ncol(X), ncol(X)), crossprod(matrix(Xstar[, i], nrow=nrow(X)), Ystar[i, ]))

-		ssr <- sum((Ystar[i, ] - matrix(Xstar[, i], nrow(X), ncol(X)) %*% matrix(betahat[, i], ncol(X), 1))^2)

-		ses[, i] <- sqrt(diag(matrix(IXTX[, i], ncol(X), ncol(X)) * ssr))

+		##ssr <- sum((Ystar[i, ] - matrix(Xstar[, i], nrow(X), ncol(X)) %*% matrix(betahat[, i], ncol(X), 1))^2)

+		##ses[, i] <- sqrt(diag(matrix(IXTX[, i], ncol(X), ncol(X)) * ssr))

 	}

-	nu <- betahat[1, ]

-	phi <- betahat[2, ]

-	return(list(nu, phi))

+##	nu <- betahat[1, ]

+##	phi <- betahat[2, ]

+##	return(list(nu, phi))

+	return(betahat)

 }

 ## linear regression without weights -- design matrix is same for all snps

@@ -1206,7 +847,7 @@ fit.lm1 <- function(idxBatch,

 		    MIN.PHI,

 		    verbose,

 		    weighted.lm, ...){

-	physical <- get("physical")

+	if(isPackageLoaded("ff")) physical <- get("physical")

 	if(verbose) message("Probe batch ", idxBatch, " of ", length(snpBatches))

 	snps <- snpBatches[[idxBatch]]

 	batches <- split(seq(along=batch(object)), batch(object))

@@ -1323,8 +964,10 @@ fit.lm1 <- function(idxBatch,

 		if(!weighted.lm){

 			res <- nuphiAllele2(allele="B", Ystar=YB, W=wB, Ns=Ns)

 		} else res <- linearModel.noweights(allele="B", Ystar=YB, W=wB, Ns=Ns)

-		nuB[, J] <- res[[1]]

-		phiB[, J] <- res[[2]]

+		##nuB[, J] <- res[[1]]

+		nuB[, J] <- res[1, ]

+		##phiB[, J] <- res[[2]]

+		phiB[, J] <- res[2, ]

 		if(THR.NU.PHI){

 			nuA[nuA[, J] < MIN.NU, J] <- MIN.NU

 			nuB[nuB[, J] < MIN.NU, J] <- MIN.NU

@@ -1337,7 +980,6 @@ fit.lm1 <- function(idxBatch,

 		rm(G, A, B, NORM, wA, wB, YA,YB, res, negA, negB, Np, Ns)

 		gc()

 	}

-

 	cA[cA < 0.05] <- 0.05

 	cB[cB < 0.05] <- 0.05

 	cA[cA > 5] <-  5

@@ -3238,8 +2880,8 @@ ellipseCenters <- function(object, index, allele, batch, log.it=TRUE){

 	return(centers)	

 }

-computeCN <- function(filenames,

-		      object,

+computeCN <- function(object,

+		      filenames,

 		      which.batches,

 		      MIN.SAMPLES=10,

 		      SNRMin=5,

@@ -3281,7 +2923,7 @@ computeCN <- function(filenames,

 			load(file.path(ldPath(), "flags.snps.rda"))

 		} 

 		if(verbose) message("Estimating allele-specific copy number at autosomal SNPs")

-		FUN <- "fit.lm1"

+		FUN <- fit.lm1

 	}

 	if(type=="autosome.nps"){

 		marker.index <- which(chromosome(object) < 23 & !isSnp(object) & !is.na(chromosome(object)))		

@@ -3302,7 +2944,7 @@ computeCN <- function(filenames,

 			load(file.path(ldPath(), "flags.nps.rda"))

 		} 

 		if(verbose) message("Estimating allele-specific copy number at autosomal SNPs")

-		FUN <- "fit.lm2"

+		FUN <- fit.lm2

 	}

 	if(type=="X.snps"){

 		marker.index <- which(chromosome(object) == 23 & isSnp(object) & !is.na(chromosome(object)))

@@ -3323,7 +2965,7 @@ computeCN <- function(filenames,

 			load(file.path(ldPath(), "flags.X.snps.rda"))

 		} 

 		if(verbose) message("Estimating allele-specific copy number at autosomal SNPs")

-		FUN <- "fit.lm3"

+		FUN <- fit.lm3

 	}

 	if(type=="X.nps"){

 		marker.index <- which(chromosome(object) == 23 & !isSnp(object) & !is.na(chromosome(object)))		

@@ -3343,7 +2985,7 @@ computeCN <- function(filenames,

 			load(file.path(ldPath(), "flags.X.nps.rda"))

 		} 

 		if(verbose) message("Estimating allele-specific copy number at autosomal SNPs")

-		FUN <- "fit.lm4"

+		FUN <- fit.lm4

 	}

 	index.strata <- splitIndicesByLength(marker.index, ocProbesets())

 	obj <- construct(filenames=filenames,

@@ -3353,9 +2995,10 @@ computeCN <- function(filenames,

 			 sns=sampleNames(object),

 			 fns=featureNames(object)[marker.index])

 	ocLapply(seq(along=index.strata),

-		 match.fun(FUN),

+		 ##match.fun(FUN),

+		 FUN,

 		 marker.index=marker.index,

-		 object=object,

+		 object=obj,

 		 Ns=Ns,

 		 normal=normal,

 		 snpflags=flags,