@@ -609,7 +609,7 @@ summarizeMaleXNps <- function(marker.index,

 }

-summarizeMaleXGenotypes <- function(marker.index,

+summarizeXGenotypes <- function(marker.index,

 				    batches,

 				    object,

 				    GT.CONF.THR,

@@ -617,22 +617,26 @@ summarizeMaleXGenotypes <- function(marker.index,

 				    MIN.SAMPLES,

 				    verbose,

 				    is.lds,

-				    DF.PRIOR,...){

+				    DF.PRIOR,

+				    gender="male", ...){

+	if(gender == "male"){

+		sample.index <- which(gender==1)

+	} else sample.index <- which(gender==2)

 	nr <- length(marker.index)

 	nc <- length(batchNames(object))

 ##	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])

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

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

+	GG <- as.matrix(calls(object)[marker.index, sample.index])

+	CP <- as.matrix(snpCallProbability(object)[marker.index, sample.index])

+	AA <- as.matrix(A(object)[marker.index, sample.index])

+	BB <- as.matrix(B(object)[marker.index, sample.index])

 	for(k in seq_along(batches)){

-		B <- batches[[k]]

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

-		gender <- object$gender[B]

-		if(sum(gender==1) < MIN.SAMPLES) next()

-		sns.batch <- sampleNames(object)[B]

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

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

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

+		if(length(sample.index) < MIN.SAMPLES) next()

+		sns.batch <- sampleNames(object)[sample.index]

 		##subset GG apppriately

 		sns <- colnames(GG)

 		J <- sns%in%sns.batch

@@ -648,9 +652,10 @@ summarizeMaleXGenotypes <- function(marker.index,

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

 		G.BB <- G==3

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

-		N.AA.M <- rowSums(G.AA, na.rm=TRUE)

-		N.AB.M <- rowSums(G.AB, na.rm=TRUE)

-		N.BB.M <- rowSums(G.BB, na.rm=TRUE)

+		N.AA <- rowSums(G.AA, na.rm=TRUE)

+		if(gender == "female")

+			N.AB <- rowSums(G.AB, na.rm=TRUE)

+		N.BB <- rowSums(G.BB, na.rm=TRUE)

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

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

 			for(j in seq_along(FUNS)){

@@ -660,44 +665,68 @@ 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"))

+		if(gender == "female")

+			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"))

+		if(gender == "female")

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

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

-		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)

+		if(gender=="male"){

+			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])

+			NN <- cbind(N.AA, N.BB)

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

+		} else {

+			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])

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

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

+		}

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

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

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

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

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

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

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

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

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

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

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

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

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

+		if(gender=="male"){

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

+		} else {

+			unobservedAA <- NN[, 1] < MIN.OBS

+			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]]

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

 	}

 	return(list(madA=shrink.madA,

 		    madB=shrink.madB,

-		    NN.M=NN.Mlist,

+		    NN=NN.Mlist,

 		    medianA=medianA,

 		    medianB=medianB))

 }

@@ -734,7 +763,7 @@ 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,

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

 					       batches=batches,

 					       object=object,

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

@@ -742,7 +771,8 @@ fit.lm3 <- function(strata,

 					       MIN.OBS=MIN.OBS,

 					       verbose=verbose,

 					       is.lds=is.lds,

-					       DF.PRIOR=DF.PRIOR/2)

+					       DF.PRIOR=DF.PRIOR/2,

+					   gender="male")

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

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

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

@@ -752,6 +782,16 @@ fit.lm3 <- function(strata,

 		## Need N, median, mad

 	}

 	if(enough.females){

+		res <- summarizeXGenotypes(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,

+						 DF.PRIOR=DF.PRIOR/2,

+					   gender="female")

 		N.AA.F <- as.matrix(N.AA(object)[marker.index, ])

 		N.AB.F <- as.matrix(N.AB(object)[marker.index, ])

 		N.BB.F <- as.matrix(N.BB(object)[marker.index, ])