diff --git a/R/rankingHeatmap.R b/R/rankingHeatmap.R
index ee290cd..5c3465a 100644
--- a/R/rankingHeatmap.R
+++ b/R/rankingHeatmap.R
@@ -1,75 +1,75 @@
 rankingHeatmap <- function(x,...) UseMethod("rankingHeatmap")
 rankingHeatmap.default <- function(x, ...) stop("not implemented for this class")
 
 rankingHeatmap.ranked=function (x,ties.method="min",...) {
   ordering=rownames(x$mat)[order(x$mat$rank)]
   #dd=x$data  
   # dd will be same as x$data, except that na.treat is handled if aggregateThenRank
   dd=as.challenge(x$data,
                   value=attr(x$data,"value"), 
                   algorithm=attr(x$data,"algorithm") ,
                   case=attr(x$data,"case"),
                   annotator = attr(x$data,"annotator"),
                   smallBetter = !attr(x$data,"largeBetter"),
                   na.treat=x$call[[1]][[1]]$na.treat)
   
   rankingHeatmap(dd,                 
                  ordering=ordering,
                  ties.method=ties.method,...)
 }
 
 
 rankingHeatmap.ranked.list=function (x,ties.method="min",...) {
   
   xx=x$data
   
   a=lapply(names(x$matlist),function(subt){
     ordering=rownames(x$matlist[[subt]])[order(x$matlist[[subt]]$rank)]
     
     dd=as.challenge(xx[[subt]],value=attr(xx,"value"), 
                     algorithm=attr(xx,"algorithm") ,
                     case=attr(xx,"case"),
                     annotator = attr(xx,"annotator"),
                     smallBetter = !attr(xx,"largeBetter"),
                     na.treat=x$call[[1]][[1]]$na.treat)
     
     rankingHeatmap(dd,
                    ordering=ordering,
-                   ties.method=ties.method,...)
+                   ties.method=ties.method,...) + ggtitle(subt) 
   })
   a
 }
 
 
 rankingHeatmap.challenge=function(x,
                                   ordering,
                                   ties.method="min",...){
   ranking=x%>%rank( ties.method = ties.method )
   
   dat=as.data.frame(table(ranking$mat[[attr(x,"algorithm")]],
                           ranking$mat$rank,
                           dnn=c("algorithm","rank")),
                     responseName = "Count")
   dat$algorithm=factor(dat$algorithm, levels=ordering)
   # dat$Count=as.factor(dat$Count)
   # dat$Count[dat$Count==0]=NA
   ncases=length(unique(x[[attr(x,"case")]]))
   ggplot(dat)+
     geom_raster(aes(algorithm,rank, fill= Count))+
     geom_hline(yintercept = seq(1.5,max(ranking$mat$rank)-.5,by=1),
                color=grey(.8),size=.3)+
     geom_vline(xintercept = seq(1.5,length(unique(dat$algorithm))-.5,by=1),
                color=grey(.8),size=.3)+
     scale_fill_viridis_c(direction = -1,
                          limits=c(0,ncases),
                          # limits=c(1,ncases),
                          # breaks=function(a) round(seq(a[1],a[2],length.out=5)),
                          # na.value = "white"
     )+
     theme(axis.text.x = element_text(angle = 90),
           aspect.ratio=1)+
     xlab("Algorithm")+
     ylab("Rank")
   #scale_y_discrete(name="Rank",breaks=rev(ranking$matlist[[subt]]$rank))
   
 }