@@ -836,7 +836,7 @@ fit.lm1 <- function(idxBatch,

 	flags <- nuA <- nuB <- phiA <- phiB <- corrAB

 	normal.snps <- normal[snps, ]

-	cB <- cA <- matrix(NA, length(snps), ncol(object))

+	##cB <- cA <- matrix(NA, length(snps), ncol(object))

 	GG <- as.matrix(calls(object)[snps, ])

 	CP <- as.matrix(snpCallProbability(object)[snps, ])

 	AA <- as.matrix(A(object)[snps, ])

@@ -1044,8 +1044,7 @@ fit.lm2 <- function(idxBatch,

 	open(snpflags)

 	open(normal)

-	

-	cA <- matrix(NA, length(snps), ncol(object))

+##	cA <- matrix(NA, length(snps), ncol(object))

 	ii <- isSnp(object) & chromosome(object) < 23 & !is.na(chromosome(object))

 	flags <- as.matrix(snpflags[,])  

 	noflags <- rowSums(flags, na.rm=TRUE) == 0  ##NA's for unevaluated batches

@@ -1158,7 +1157,7 @@ fit.lm3 <- function(idxBatch,

 	corrAB <- corrBB <- corrAA <- sig2B <- sig2A <- tau2B <- tau2A <- matrix(NA, length(snps), length(unique(batch(object))))

 	phiA2 <- phiB2 <- tau2A

 	flags <- nuA <- nuB <- phiA <- phiB <- corrAB

-	cB <- cA <- matrix(NA, length(snps), ncol(object))

+##	cB <- cA <- matrix(NA, length(snps), ncol(object))

 	gender <- object$gender

 	IX <- matrix(gender, length(snps), ncol(object))

 	NORM <- normal[snps,]

@@ -1298,28 +1297,27 @@ fit.lm3 <- function(idxBatch,

 			phiB2[phiB2[, J] < MIN.PHI, J] <- MIN.PHI

 		}

 		phistar <- phiB2[, J]/phiA[, J]

-		tmp <- (B-nuB[, J] - phistar*A + phistar*nuA[, J])/phiB[, J]

-		cB[, k] <- tmp/(1-phistar*phiA2[, J]/phiB[, J])

-		cA[, k] <- (A-nuA[, J]-phiA2[, J]*cB[, k])/phiA[, J]

+##		tmp <- (B-nuB[, J] - phistar*A + phistar*nuA[, J])/phiB[, J]

+##		cB[, k] <- tmp/(1-phistar*phiA2[, J]/phiB[, J])

+##		cA[, k] <- (A-nuA[, J]-phiA2[, J]*cB[, k])/phiA[, J]

 		##some of the snps are called for the men, but not the women

 		rm(YA, YB, wA, wB, res, tmp, phistar, A, B, G, index)

 		gc()

 	}

-

-	cA[cA < 0.05] <- 0.05

-	cB[cB < 0.05] <- 0.05

-	cA[cA > 5] <-  5

-	cB[cB > 5] <- 5

+##	cA[cA < 0.05] <- 0.05

+##	cB[cB < 0.05] <- 0.05

+##	cA[cA > 5] <-  5

+##	cB[cB > 5] <- 5

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

 	##RS: need to fix.  why are there NA's by coercion

-	cA <- matrix(as.integer(cA*100), nrow(cA), ncol(cA))

+##	cA <- matrix(as.integer(cA*100), nrow(cA), ncol(cA))

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

 	##ii <- rowSums(is.na(cA)) > 0

 	##these often arise at SNPs with low confidence scores

-	cB <- matrix(as.integer(cB*100), nrow(cB), ncol(cB))

-	CA(object)[snps, ] <- cA

-	CB(object)[snps, ] <- cB

+##	cB <- matrix(as.integer(cB*100), nrow(cB), ncol(cB))

+##	CA(object)[snps, ] <- cA

+##	CB(object)[snps, ] <- cB

 	snpflags[snps, ] <- flags

 	tmp <- physical(lM(object))$tau2A

 	tmp[snps, ] <- tau2A