@@ -351,7 +351,6 @@ shrinkGenotypeSummaries <- function(strata, index.list, object, MIN.OBS, MIN.SAM

 	for(k in seq(along=batches)){

 		B <- batches[[k]]

 		this.batch <- unique(as.character(batch(object)[B]))

-

 		medianA[[k]] <- cbind(medianA.AA[, k], medianA.AB[, k], medianA.BB[, k])

 		medianB[[k]] <- cbind(medianB.AA[, k], medianB.AB[, k], medianB.BB[, k])

 		madA <- cbind(madA.AA[, k], madA.AB[, k], madA.BB[, k])

@@ -393,8 +392,11 @@ shrinkGenotypeSummaries <- function(strata, index.list, object, MIN.OBS, MIN.SAM

 		unobservedAB <- NN[, 2] < MIN.OBS

 		unobservedBB <- NN[, 3] < MIN.OBS

 		unobserved.index <- vector("list", 3)

+		## AB, BB observed

 		unobserved.index[[1]] <- which(unobservedAA & (NN[, 2] >= MIN.OBS & NN[, 3] >= MIN.OBS))

+		## AA and BB observed (strange)

 		unobserved.index[[2]] <- which(unobservedAB & (NN[, 1] >= MIN.OBS & NN[, 3] >= MIN.OBS))

+		## AB and AA observed

 		unobserved.index[[3]] <- which(unobservedBB & (NN[, 2] >= MIN.OBS & NN[, 1] >= MIN.OBS))

 		res <- imputeCenter(medianA[[k]], medianB[[k]], index.complete, unobserved.index)

 		medianA[[k]] <- res[[1]]

@@ -619,7 +621,8 @@ summarizeMaleXGenotypes <- function(marker.index,

 				    DF.PRIOR,...){

 	nr <- length(marker.index)

 	nc <- length(batchNames(object))

-	NN.Mlist <- imputed.medianA <- imputed.medianB <- shrink.madA <- shrink.madB <- vector("list", nc)

+##	NN.Mlist <- imputed.medianA <- imputed.medianB <- shrink.madA <- shrink.madB <- vector("list", nc)

+	NN.Mlist <- medianA <- medianB <- shrink.madA <- shrink.madB <- vector("list", nc)

 	gender <- object$gender

 	GG <- as.matrix(calls(object)[marker.index, gender==1])

 	CP <- as.matrix(snpCallProbability(object)[marker.index, gender==1])

@@ -658,43 +661,46 @@ summarizeMaleXGenotypes <- function(marker.index,

 			tmp

 		}

 		statsA.AA <- summaryStats(G.AA, A, FUNS=c("rowMedians", "rowMAD"))

-		statsA.AB <- summaryStats(G.AB, A, FUNS=c("rowMedians", "rowMAD"))

+		##statsA.AB <- summaryStats(G.AB, A, FUNS=c("rowMedians", "rowMAD"))

 		statsA.BB <- summaryStats(G.BB, A, FUNS=c("rowMedians", "rowMAD"))

 		statsB.AA <- summaryStats(G.AA, B, FUNS=c("rowMedians", "rowMAD"))

-		statsB.AB <- summaryStats(G.AB, B, FUNS=c("rowMedians", "rowMAD"))

+		##statsB.AB <- summaryStats(G.AB, B, FUNS=c("rowMedians", "rowMAD"))

 		statsB.BB <- summaryStats(G.BB, B, FUNS=c("rowMedians", "rowMAD"))

-		medianA <- cbind(statsA.AA[, 1], statsA.AB[, 1], statsA.BB[, 1])

-		medianB <- cbind(statsB.AA[, 1], statsB.AB[, 1], statsB.BB[, 1])

-		madA <- cbind(statsA.AA[, 1], statsA.AB[, 1], statsA.BB[, 1])

-		madB <- cbind(statsB.AA[, 1], statsB.AB[, 1], statsB.BB[, 1])

-		rm(statsA.AA, statsA.AB, statsA.BB, statsB.AA, statsB.AB, statsB.BB)

-

-		NN.M <- cbind(N.AA.M, N.AB.M, N.BB.M)

+		medianA[[k]] <- cbind(statsA.AA[, 1], ##statsA.AB[, 1],

+				      statsA.BB[, 1])

+		medianB[[k]] <- cbind(statsB.AA[, 1], ##statsB.AB[, 1],

+				      statsB.BB[, 1])

+		madA <- cbind(statsA.AA[, 2],  ##statsA.AB[, 2],

+			      statsA.BB[, 2])

+		madB <- cbind(statsB.AA[, 2], ##statsB.AB[, 2],

+			      statsB.BB[, 2])

+		rm(statsA.AA, ##statsA.AB,

+		   statsA.BB, statsB.AA,

+		   ##statsB.AB,

+		   statsB.BB)

+		NN.M <- cbind(N.AA.M, ##N.AB.M,

+			      N.BB.M)

 		NN.Mlist[[k]] <- NN.M

-

 		shrink.madA[[k]] <- shrink(madA, NN.M, DF.PRIOR)

 		shrink.madB[[k]] <- shrink(madB, NN.M, DF.PRIOR)

-

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

 		## SNPs that we'll use for imputing location/scale of unobserved genotypes

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

-		index.complete <- indexComplete(NN.M[, -2], medianA, medianB, MIN.OBS)

-		if(length(index.complete) == 1){

-			if(index.complete == FALSE) return()

-		}

+		##index.complete <- indexComplete(NN.M[, -2], medianA[[k]], medianB[[k]], MIN.OBS)

+		index.complete <- indexComplete(NN.M, medianA[[k]], medianB[[k]], MIN.OBS)

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

 		## Impute sufficient statistics for unobserved genotypes (plate-specific)

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

-		res <- imputeCenterX(medianA, medianB, NN.M, index.complete, MIN.OBS)

-		imputed.medianA[[k]] <- res[[1]]

-		imputed.medianB[[k]] <- res[[2]]

+		res <- imputeCenterX(medianA[[k]], medianB[[k]], NN.M, index.complete, MIN.OBS)

+		medianA[[k]] <- res[[1]]

+		medianB[[k]] <- res[[2]]

 	}

 	return(list(madA=shrink.madA,

 		    madB=shrink.madB,

 		    NN.M=NN.Mlist,

-		    medianA=imputed.medianA,

-		    medianB=imputed.medianB))

+		    medianA=medianA,

+		    medianB=medianB))

 }

 ## X chromosome, SNPs

@@ -729,11 +735,14 @@ fit.lm3 <- function(strata,

 	phiA2 <- as.matrix(phiPrimeA(object)[marker.index, ])

 	phiB2 <- as.matrix(phiPrimeB(object)[marker.index, ])

 	if(enough.males){

-		res <- summarizeMaleXGenotypes(marker.index=marker.index, batches=batches,

-					       object=object, GT.CONF.THR=GT.CONF.THR,

+		res <- summarizeMaleXGenotypes(marker.index=marker.index,

+					       batches=batches,

+					       object=object,

+					       GT.CONF.THR=GT.CONF.THR,

 					       MIN.SAMPLES=MIN.SAMPLES,

 					       MIN.OBS=MIN.OBS,

-					       verbose=verbose, is.lds=is.lds,

+					       verbose=verbose,

+					       is.lds=is.lds,

 					       DF.PRIOR=DF.PRIOR/2)

 		madA.Mlist <- res[["madA"]]

 		madB.Mlist <- res[["madB"]]

@@ -803,18 +812,18 @@ fit.lm3 <- function(strata,

 			phiB2[, k] <- betas[3, ]

 		}

 		if(enough.men & !enough.women){

-			betas <- fit.wls(NN.M[, c(1,3)],

-					 sigma=madA.M[, c(1,3)],

+			betas <- fit.wls(NN.M,##[, c(1,3)],

+					 sigma=madA.M,##[, c(1,3)],

 					 allele="A",

-					 Y=medianA.M[, c(1,3)],

+					 Y=medianA.M,##[, c(1,3)],

 					 autosome=FALSE,

-					 X=cbind(1, c(0, 1)))

+					 X=cbind(1, c(1, 0)))

 			nuA[, k] <- betas[1, ]

 			phiA[, k] <- betas[2, ]

-			betas <- fit.wls(NN.M[, c(1,3)],

-					 sigma=madB.M[, c(1,3)],

+			betas <- fit.wls(NN.M,##[, c(1,3)],

+					 sigma=madB.M,#[, c(1,3)],

 					 allele="B",

-					 Y=medianB.M[, c(1,3)],

+					 Y=medianB.M,#[, c(1,3)],

 					 autosome=FALSE,

 					 X=cbind(1, c(0, 1)))

 			nuB[, k] <- betas[1, ]

@@ -1050,17 +1059,20 @@ imputeCenter <- function(muA, muB, index.complete, index.missing){

 	index <- index.missing

 	mnA <- muA

 	mnB <- muB

-	for(j in 1:3){

-		if(length(index[[j]]) == 0) next()

-		X <- cbind(1, mnA[index.complete,  -j, drop=FALSE], mnB[index.complete,  -j, drop=FALSE])

-		Y <- cbind(mnA[index.complete, j], mnB[index.complete,  j])

-		betahat <- solve(crossprod(X), crossprod(X,Y))

-		X <- cbind(1, mnA[index[[j]],  -j, drop=FALSE],  mnB[index[[j]],  -j, drop=FALSE])

-		mus <- X %*% betahat

-		muA[index[[j]], j] <- mus[, 1]

-		muB[index[[j]], j] <- mus[, 2]

+	if(length(index.complete) >= 100){

+		for(j in 1:3){

+			if(length(index[[j]]) == 0) next()

+			X <- cbind(1, mnA[index.complete,  -j, drop=FALSE], mnB[index.complete,  -j, drop=FALSE])

+			Y <- cbind(mnA[index.complete, j], mnB[index.complete,  j])

+			betahat <- solve(crossprod(X), crossprod(X,Y))

+			X <- cbind(1, mnA[index[[j]],  -j, drop=FALSE],  mnB[index[[j]],  -j, drop=FALSE])

+			mus <- X %*% betahat

+			muA[index[[j]], j] <- mus[, 1]

+			muB[index[[j]], j] <- mus[, 2]

+		}

 	}

-	list(muA, muB)

+	results <- list(muA, muB)

+	return(results)

 }

 indexComplete <- function(NN, medianA, medianB, MIN.OBS){

@@ -1069,15 +1081,19 @@ indexComplete <- function(NN, medianA, medianB, MIN.OBS){

 	index.complete <- intersect(Nindex, correct.order)

 	size <- min(5000, length(index.complete))

 	if(size == 5000) index.complete <- sample(index.complete, 5000, replace=TRUE)

-	if(length(index.complete) < 100){

-		warning("fewer than 100 snps pass criteria for imputing unobserve dgenotype location/scale")

-		return(FALSE)

-	}

+##	if(length(index.complete) < 100){

+##		stop("fewer than 100 snps pass criteria for imputing unobserved genotype location/scale")

+##	}

 	return(index.complete)

 }

 imputeCentersForMonomorphicSnps <- function(medianA, medianB, index.complete, unobserved.index){

 	cols <- c(3, 1, 2)

+	if(length(index.complete) < 100){

+		##message("index.complete less than 100.  No imputation")

+		results <- list(medianA=medianA, medianB=medianB)

+		return(results)

+	}

 	for(j in 1:3){

 		if(length(unobserved.index[[j]]) == 0) next()

 		kk <- cols[j]

@@ -1090,36 +1106,40 @@ imputeCentersForMonomorphicSnps <- function(medianA, medianB, index.complete, un

 		medianA[unobserved.index[[j]], -kk] <- mus[, 1:2]

 		medianB[unobserved.index[[j]], -kk] <- mus[, 3:4]

 	}

-	list(medianA=medianA, medianB=medianB)

+	results <- list(medianA=medianA, medianB=medianB)

+	return(results)

 }

 imputeCenterX <- function(muA, muB, Ns, index.complete, MIN.OBS){

-	index1 <- which(Ns[, 1] == 0 & Ns[, 3] > MIN.OBS)

+	##index1 <- which(Ns[, 1] == 0 & Ns[, 3] > MIN.OBS)

+	index1 <- which(Ns[, 1] == 0 & Ns[, 2] > MIN.OBS)

 	if(length(index1) > 0){

-		X <- cbind(1, muA[index.complete, 3], muB[index.complete, 3])

+		##X <- cbind(1, muA[index.complete, 3], muB[index.complete, 3])

+		X <- cbind(1, muA[index.complete, 2], muB[index.complete, 2])

 		Y <- cbind(1, muA[index.complete, 1], muB[index.complete, 1])

 ##		X <- cbind(1, muA[index.complete[[1]], 3], muB[index.complete[[1]], 3])

 ##		Y <- cbind(1, muA[index.complete[[1]], 1], muB[index.complete[[1]], 1])

 		betahat <- solve(crossprod(X), crossprod(X,Y))

 		##now with the incomplete SNPs

-		X <- cbind(1, muA[index1, 3], muB[index1, 3])

+		##X <- cbind(1, muA[index1, 3], muB[index1, 3])

+		X <- cbind(1, muA[index1, 2], muB[index1, 2])

 		mus <- X %*% betahat

 		muA[index1, 1] <- mus[, 2]

 		muB[index1, 1] <- mus[, 3]

 	}

-	index1 <- which(Ns[, 3] == 0)

+	index1 <- which(Ns[, 2] == 0)

 	if(length(index1) > 0){

 		X <- cbind(1, muA[index.complete, 1], muB[index.complete, 1])

-		Y <- cbind(1, muA[index.complete, 3], muB[index.complete, 3])

+		Y <- cbind(1, muA[index.complete, 2], muB[index.complete, 2])

 ##		X <- cbind(1, muA[index.complete[[2]], 1], muB[index.complete[[2]], 1])

 ##		Y <- cbind(1, muA[index.complete[[2]], 3], muB[index.complete[[2]], 3])

 		betahat <- solve(crossprod(X), crossprod(X,Y))

 		##now with the incomplete SNPs

 		X <- cbind(1, muA[index1, 1], muB[index1, 1])

 		mus <- X %*% betahat

-		muA[index1, 3] <- mus[, 2]

-		muB[index1, 3] <- mus[, 3]

+		muA[index1, 2] <- mus[, 2]

+		muB[index1, 2] <- mus[, 3]

 	}

 	list(muA, muB)

 }

@@ -1311,11 +1331,12 @@ genotypeSummary <- function(object,

 			    marker.index,

 			    is.lds){

 	if(type == "X.SNP" | type=="X.NP"){

-		gender <- object$gender

-		if(sum(gender == 2) < 3) {

-			message("too few females to estimate within genotype summary statistics on CHR X")

-			return(object)

-		}

+##		gender <- object$gender

+##		## the number of women in each batch could be less than 3...

+##		if(sum(gender == 2) < 3) {

+##			message("too few females to estimate within genotype summary statistics on CHR X")

+##			return(object)

+##		}

 		CHR.X <- TRUE

 	} else CHR.X <- FALSE

 	if(missing(marker.index)){

@@ -1436,6 +1457,9 @@ summarizeSnps <- function(strata,

 ##	} else {

 ##		batches <- split(seq_along(batch(object)), as.character(batch(object)))

 ##	}

+	if(CHR.X){

+		if(verbose) message("        biallelic cluster medians are estimated using only the women for SNPs on chr. X")

+	}

 	batches <- split(seq_along(batch(object)), as.character(batch(object)))

 	batchnames <- batchNames(object)

 	nr <- length(index)

@@ -1451,23 +1475,17 @@ summarizeSnps <- function(strata,

 	FL <- as.matrix(flags(object)[index, ])

 	if(verbose) message("        Computing summaries...")

 	for(k in seq_along(batches)){

-		B <- batches[[k]]

-		this.batch <- unique(as.character(batch(object)[B]))

+		##note that the genotype frequency for AA would include 'A' on chromosome X for men

+		sample.index <- batches[[k]]

+		this.batch <- unique(as.character(batch(object)[sample.index]))

 		j <- match(this.batch, batchnames)

-		G <- GG[, B]

+		G <- GG[, sample.index]

 		##NORM <- normal.index[, k]

-		xx <- CP[, B]

+		xx <- CP[, sample.index]

 		highConf <- (1-exp(-xx/1000)) > GT.CONF.THR

 		G <- G*highConf

-		## Some markers may have genotype confidences scores that are ALL below the threshold

-		## For these markers, provide statistical summaries based on all the samples and flag

-		## Provide summaries for these markers and flag to indicate the problem

-		ii <- which(rowSums(G) == 0)

-		G[ii, ] <- GG[ii, B]

-		## table(rowSums(G==0))

-		##G <- G*highConf*NORM

-		A <- AA[, B]

-		B <- BB[, B]

+		A <- AA[, sample.index]

+		B <- BB[, sample.index]

 		## this can be time consuming...do only once

 		G.AA <- G==1

 		G.AA[G.AA==FALSE] <- NA

@@ -1478,6 +1496,11 @@ summarizeSnps <- function(strata,

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

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

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

+		if(CHR.X){

+			gender <- object$gender[sample.index]

+			sample.index <- sample.index[gender == 2]

+			if(length(sample.index) == 0) next()

+		}

 		summaryStats <- function(X, INT, FUNS){

 			tmp <- matrix(NA, nrow(X), length(FUNS))

 			for(j in seq_along(FUNS)){

@@ -1486,12 +1509,29 @@ summarizeSnps <- function(strata,

 			}

 			tmp

 		}

-		statsA.AA[[k]] <- summaryStats(G.AA, A, FUNS=c("rowMedians", "rowMAD"))

-		statsA.AB[[k]] <- summaryStats(G.AB, A, FUNS=c("rowMedians", "rowMAD"))

-		statsA.BB[[k]] <- summaryStats(G.BB, A, FUNS=c("rowMedians", "rowMAD"))

-		statsB.AA[[k]] <- summaryStats(G.AA, B, FUNS=c("rowMedians", "rowMAD"))

-		statsB.AB[[k]] <- summaryStats(G.AB, B, FUNS=c("rowMedians", "rowMAD"))

+		stats <- summaryStats(G.AA, A, FUNS=c("rowMedians", "rowMAD"))

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

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

+		stats <- summaryStats(G.AB, A, FUNS=c("rowMedians", "rowMAD"))

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

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

+

+		stats <- summaryStats(G.BB, A, FUNS=c("rowMedians", "rowMAD"))

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

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

+

+		stats <- summaryStats(G.AA, B, FUNS=c("rowMedians", "rowMAD"))

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

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

+

+		stats <- summaryStats(G.AB, B, FUNS=c("rowMedians", "rowMAD"))

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

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

+

 		statsB.BB[[k]] <- summaryStats(G.BB, B, FUNS=c("rowMedians", "rowMAD"))

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

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

+

 		## log2 Transform Intensities

 		A <- log2(A); B <- log2(B)

 		tau2A.AA[, k] <- summaryStats(G.AA, A, FUNS="rowMAD")^2

@@ -1510,19 +1550,19 @@ summarizeSnps <- function(strata,

 	corrAA(object)[index,] <- corrAA

 	corrAB(object)[index,] <- corrAB

 	corrBB(object)[index,] <- corrBB

-	medianA.AA(object)[index,] <- do.call(cbind, lapply(statsA.AA, function(x) x[, 1]))

-	medianA.AB(object)[index,] <- do.call(cbind, lapply(statsA.AB, function(x) x[, 1]))

-	medianA.BB(object)[index,] <- do.call(cbind, lapply(statsA.BB, function(x) x[, 1]))

-	medianB.AA(object)[index,] <- do.call(cbind, lapply(statsB.AA, function(x) x[, 1]))

-	medianB.AB(object)[index,] <- do.call(cbind, lapply(statsB.AB, function(x) x[, 1]))

-	medianB.BB(object)[index,] <- do.call(cbind, lapply(statsB.BB, function(x) x[, 1]))

-

-	madA.AA(object)[index,] <- do.call(cbind, lapply(statsA.AA, function(x) x[, 2]))

-	madA.AB(object)[index,] <- do.call(cbind, lapply(statsA.AB, function(x) x[, 2]))

-	madA.BB(object)[index,] <- do.call(cbind, lapply(statsA.BB, function(x) x[, 2]))

-	madB.AA(object)[index,] <- do.call(cbind, lapply(statsB.AA, function(x) x[, 2]))

-	madB.AB(object)[index,] <- do.call(cbind, lapply(statsB.AB, function(x) x[, 2]))

-	madB.BB(object)[index,] <- do.call(cbind, lapply(statsB.BB, function(x) x[, 2]))

+##	medianA.AA(object)[index,] <- do.call(cbind, lapply(statsA.AA, function(x) x[, 1]))

+##	medianA.AB(object)[index,] <- do.call(cbind, lapply(statsA.AB, function(x) x[, 1]))

+##	medianA.BB(object)[index,] <- do.call(cbind, lapply(statsA.BB, function(x) x[, 1]))

+##	medianB.AA(object)[index,] <- do.call(cbind, lapply(statsB.AA, function(x) x[, 1]))

+##	medianB.AB(object)[index,] <- do.call(cbind, lapply(statsB.AB, function(x) x[, 1]))

+##	medianB.BB(object)[index,] <- do.call(cbind, lapply(statsB.BB, function(x) x[, 1]))

+##

+##	madA.AA(object)[index,] <- do.call(cbind, lapply(statsA.AA, function(x) x[, 2]))

+##	madA.AB(object)[index,] <- do.call(cbind, lapply(statsA.AB, function(x) x[, 2]))

+##	madA.BB(object)[index,] <- do.call(cbind, lapply(statsA.BB, function(x) x[, 2]))

+##	madB.AA(object)[index,] <- do.call(cbind, lapply(statsB.AA, function(x) x[, 2]))

+##	madB.AB(object)[index,] <- do.call(cbind, lapply(statsB.AB, function(x) x[, 2]))

+##	madB.BB(object)[index,] <- do.call(cbind, lapply(statsB.BB, function(x) x[, 2]))

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

 ##	tau2A.AB(object)[index, ] <- tau2A.AB

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

@@ -356,6 +356,7 @@ C3 <- function(object, allele, marker.index, batch.index, sample.index){

 		l <- batch.index[k]

 		##j <- which(as.character(batch(object))[sample.index] == batchNames(object)[l])

 		jj <- sample.index[as.character(batch(object))[sample.index] == batchNames(object)[l]]

+		##phiA2 and phiB2 are not always estimable  -- need both men and women

 		phiA2 <- phiPrimeA(object)[marker.index, l]

 		phiB2 <- phiPrimeB(object)[marker.index, l]

 		phiA <- phiA(object)[marker.index, l]

@@ -364,28 +365,28 @@ C3 <- function(object, allele, marker.index, batch.index, sample.index){

 		nuB <- nuB(object)[marker.index, l]

 		IA <- A(object)[marker.index, jj]

 		IB <- B(object)[marker.index, jj]

+		## I = nu + acn * phi

+		## acn = 1/phi* (I-nu)

 		phistar <- phiB2/phiA

 		tmp <- (IB - nuB - phistar*IA + phistar*nuA)/phiB

 		CB <- tmp/(1-phistar*phiA2/phiB)

-		##CB <- 1/(1-phiA2*phiB2/(phiA*phiB)) * 1/phiB * (IA-nuB-phiB2/phiA*(IA-nuA))

-		CA <- (IA-nuA-phiA2*CB)/phiA

+		index <- which(is.na(phiB2) | is.na(phiA2))

+		if(length(index) > 0){

+			cb <- 1/phiB[index] * (IB[index, ] - nuB[index])

+			CB[index, ] <- cb

+		}

 		if(allele == "B"){

 			acn[, match(jj, sample.index)] <- CB

 			##acn[[k]] <- CB

 		}

 		if(allele == "A"){

-			acn[, match(jj, sample.index)] <- (IA-nuA-phiA2*CB)/phiA

-		}

-		if(allele == "AandB"){

-			CA <- tmp/(1-phistar*phiA2/phiB)

-			CB <- (IA-nuA-phiA2*CB)/phiA

-			acn[, match(jj, sample.index)] <- (IA-nuA-phiA2*CB)/phiA

+			CA <- (IA-nuA-phiA2*CB)/phiA

+			if(length(index) > 0){

+				ca <- 1/phiA[index] * (IA[index, ] - nuA[index])

+ 				CA[index, ] <- ca

+			}

+			acn[, match(jj, sample.index)] <- CA

 		}

-##		if(allele=="AandB")

-##			CA <- tmp/(1-phistar*phiA2/phiB)

-##			CB <- (IA-nuA-phiA2*CB)/phiA

-##			acn[[k]] <- CA+CB

-##		}

 	}

 ##	if(length(acn) > 1){

 ##		acn <- do.call("cbind", acn)

@@ -1,3 +1,4 @@

-rsync -avuzb --exclude '~*' --exclude '.git*' -e ssh ~/madman/Rpacks/git/crlmm/ enigma2.jhsph.edu:~/madman/Rpacks/release/crlmm

-rsync -avuzb --exclude '.git*' -e ssh enigma2.jhsph.edu:~/madman/Rpacks/release/crlmm/inst/scripts/copynumber.pdf ~/madman/Rpacks/git/crlmm/inst/scripts/copynumber.pdf

+rsync -avuzb --exclude '*~' --exclude '.git*' -e ssh ~/madman/Rpacks/git/crlmm/ enigma2.jhsph.edu:~/madman/Rpacks/git/crlmm

+rsync -avuzb --exclude '*~' --exclude '.git*' -e ssh enigma2.jhsph.edu:~/madman/Rpacks/release/crlmm/inst/scripts/*opynumber.pdf ~/madman/Rpacks/git/crlmm/inst/scripts/

+