@@ -1393,7 +1393,7 @@ fit.lm4 <- function(idxBatch,

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

 	batches <- batches[sapply(batches, length) >= MIN.SAMPLES]

 	nuA <- phiA <- sig2A <- tau2A <- matrix(NA, length(snps), length(unique(batch(object))))

-	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 <- snpflags[ii, , drop=FALSE]

 	noflags <- rowSums(flags, na.rm=TRUE) == 0

@@ -1500,18 +1500,17 @@ fit.lm4 <- function(idxBatch,

 		}

 		CT1 <- 1/phi1*(A.M-nu1)

 		CT2 <- 1/phi2*(A.F-nu2)

-		tmp <- cA[, k]

-		tmp[, gend==1] <- CT1

-		tmp[, gend==2] <- CT2

-		cA[, k] <- tmp

+##		tmp <- cA[, k]

+##		tmp[, gend==1] <- CT1

+##		tmp[, gend==2] <- CT2

+##		cA[, k] <- tmp

 		rm(tmp, CT1, CT2, A.F, normal.f, G, AA, BB, Y, X, Ns)

 		gc()

 	}

-

-	cA[cA < 0.05] <- 0.05

-	cA[cA > 5] <-  5

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

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

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

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

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

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

 	open(lM(object))

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

 	tmp[snps, ] <- nuA