diff --git a/R/subset.R b/R/subset.R
index a1481be..67f5762 100644
--- a/R/subset.R
+++ b/R/subset.R
@@ -1,111 +1,111 @@
 subset <- function(x,...) UseMethod("subset")
 subset.default <- function(x, ...) base::subset(x, ...)
 
 
 subset.comparedRanks.list=function(x,
                                    tasks,...){
   res=x[tasks]
   class(res)="comparedRanks.list"
   res
 }
 
 subset.list=function(x,
                      tasks,...){
   x[tasks]
 }
 
 subset.aggregated.list=function(x,
                                 tasks,...){
   call=match.call(expand.dots = T)
   if (!is.null(as.list(call$top))) stop("Subset of algorithms only sensible for single task challenges.")
   matlist=x$matlist[tasks]
   res=list(matlist=matlist,
            call=list(x$call,call),
            data=x$data,
            FUN =  . %>% (x$FUN) %>%  (call)
   )
 
   class(res)=class(x)
   res
 
 }
 
 which.top=function(object,
                    top){
   mat=object$mat[object$mat$rank<=top,]
   rownames(mat)#[order(mat$rank)]
 }
 
 
 
 subset.ranked.list <- function(x,
                                top,
                                tasks,...) {
   
-  if (!missing(tasks) & length(x$matlist) == 1) stop("Subset of tasks only sensible for multi task challenges.")
+#  if (!missing(tasks) & length(x$matlist) == 1) stop("Subset of tasks only sensible for multi task challenges.")
   if (!missing(top) & length(x$matlist) != 1)  stop("Subset of algorithms only sensible for single-task challenges. Otherwise no consensus ranking is possible.")
   
   if (!missing(top)){
     taskMat <- x$matlist[[1]]
     taskData <- x$data[[1]]
     objectTop=x
     objectTop$matlist[[1]]=taskMat[taskMat$rank<=top,]
     
     taskMatRowNames <- rownames(objectTop$matlist[[1]])
     attribute <- attr(objectTop$data,"algorithm")
     
     selectedRowNames <- taskData[[attribute]] %in% taskMatRowNames
     objectTop$data[[1]] <- taskData[selectedRowNames,]
     if (is.factor(objectTop$data[[1]][[attribute]])) objectTop$data[[1]][[attribute]] <- droplevels(objectTop$data[[1]][[attribute]])
     
     objectTop$fulldata=x$data
     return(objectTop)
-  } else if (!missing(task)){
+  } else if (!missing(tasks)){
     res=list(matlist=x$matlist[tasks],
              data=x$data[tasks],
              call=x$call,
              FUN=x$FUN,
              FUN.list=x$FUN.list
     )
     
     attrib=attributes(x$data)
     attrib$names=attr(res$data,"names")
     attributes(res$data)=attrib
     class(res)=c("ranked.list","list")
     return(res)
   }
 }
 
 
 
 
 subset.bootstrap.list=function(x,
                                top,
                                tasks, ...) {
   
-  if (!missing(tasks) & length(x$matlist) == 1) stop("Subset of tasks only sensible for multi task challenges.")
+ # if (!missing(tasks) & length(x$matlist) == 1) stop("Subset of tasks only sensible for multi task challenges.")
   if (!missing(top) & length(x$matlist) != 1)  stop("Subset of algorithms only sensible for single-task challenges. Otherwise no consensus ranking is possible.")
   
   if (!missing(top)){
     objectTop <- subset.ranked.list(x, top = top)
     
     objectTop$bootsrappedRanks[[1]] <- objectTop$bootsrappedRanks[[1]][rownames(objectTop$matlist[[1]]),]
     objectTop$bootsrappedAggregate[[1]] <- objectTop$bootsrappedAggregate[[1]][rownames(objectTop$matlist[[1]]),]
     return(objectTop)
-  } else if (!missing(task)){
+  } else if (!missing(tasks)){
     res=list(bootsrappedRanks=x$bootsrappedRanks[tasks],
              bootsrappedAggregate=x$bootsrappedAggregate[tasks],
              matlist=x$matlist[tasks],
              data=x$data[tasks],
              FUN=x$FUN
     )
     
     attrib=attributes(x$data)
     attrib$names=attr(res$data,"names")
     attributes(res$data)=attrib
     class(res)="bootstrap.list"
     return(res)
   }
 }
 
 
diff --git a/tests/testthat/test-taskSubset.R b/tests/testthat/test-taskSubset.R
index 21dd4cf..2efd8ab 100644
--- a/tests/testthat/test-taskSubset.R
+++ b/tests/testthat/test-taskSubset.R
@@ -1,184 +1,185 @@
 test_that("extraction of task subset works for multi-task data set", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                        data.frame(algo="A1", value=0.8, case="C1"),
                        data.frame(algo="A2", value=0.6, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.2, case="C2"),
                        data.frame(algo="A2", value=0.1, case="C2"),
                        data.frame(algo="A3", value=0.0, case="C2")
                      ))
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=0.2, case="C1"),
                        data.frame(algo="A2", value=0.3, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.7, case="C2"),
                        data.frame(algo="A2", value=0.8, case="C2"),
                        data.frame(algo="A3", value=0.9, case="C2")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
-  rankingSubset <- taskSubset(ranking, tasks=c("T2"))
+  rankingSubset <- subset(ranking, tasks=c("T2"))
 
   expect_equal(length(rankingSubset$matlist), 1)
   expect_is(rankingSubset$matlist$T2, "data.frame")
 
   expect_equal(length(rankingSubset$data), 1)
   expect_is(rankingSubset$data$T2, "data.frame")
 })
 
 test_that("extraction of task subset works for single-task data set", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"),
     data.frame(algo="A3", value=0.4, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.1, case="C2"),
     data.frame(algo="A3", value=0.0, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
-  rankingSubset <- taskSubset(ranking, tasks=c("T1"))
+  rankingSubset <- subset(ranking, tasks=c("T1"))
 
   expect_equal(length(rankingSubset$matlist), 1)
   expect_is(rankingSubset$matlist$T1, "data.frame")
 
   expect_equal(length(rankingSubset$data), 1)
   expect_is(rankingSubset$data$T1, "data.frame")
 })
 
 test_that("extraction of task subset does not raise an error for invalid task name", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"),
     data.frame(algo="A3", value=0.4, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.1, case="C2"),
     data.frame(algo="A3", value=0.0, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
-  rankingSubset <- taskSubset(ranking, tasks=c("T1x"))
+  rankingSubset <- subset(ranking, tasks=c("T1x"))
 
   expect_equal(length(rankingSubset$matlist), 1)
   expect_equal(rankingSubset$matlist$T1, NULL)
 
   expect_equal(length(rankingSubset$data), 1)
   expect_equal(rankingSubset$data$T1, NULL)
 })
 
 test_that("extraction of task subset from bootstrap ranking works for multi-task data set", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                        data.frame(algo="A1", value=0.8, case="C1"),
                        data.frame(algo="A2", value=0.6, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.2, case="C2"),
                        data.frame(algo="A2", value=0.1, case="C2"),
                        data.frame(algo="A3", value=0.0, case="C2")
                      ))
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=0.2, case="C1"),
                        data.frame(algo="A2", value=0.3, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.7, case="C2"),
                        data.frame(algo="A2", value=0.8, case="C2"),
                        data.frame(algo="A3", value=0.9, case="C2")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
   set.seed(1)
   rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
 
-  rankingBootstrappedSubset <- taskSubset(rankingBootstrapped, tasks=c("T2"))
+  rankingBootstrappedSubset <- subset(rankingBootstrapped, tasks=c("T2"))
 
   expect_equal(length(rankingBootstrappedSubset$matlist), 1)
   expect_is(rankingBootstrappedSubset$matlist$T2, "data.frame")
 
   expect_equal(length(rankingBootstrappedSubset$data), 1)
   expect_is(rankingBootstrappedSubset$data$T2, "data.frame")
 
   expect_equal(length(rankingBootstrappedSubset$bootsrappedRanks), 1)
   expect_is(rankingBootstrappedSubset$bootsrappedRanks$T2, "data.frame")
 
   expect_equal(length(rankingBootstrappedSubset$bootsrappedAggregate), 1)
   expect_is(rankingBootstrappedSubset$bootsrappedAggregate$T2, "data.frame")
 })
 
 test_that("extraction of task subset from bootstrap ranking works for single-task data set", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"),
     data.frame(algo="A3", value=0.4, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.1, case="C2"),
     data.frame(algo="A3", value=0.0, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
   set.seed(1)
   rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
 
-  rankingBootstrappedSubset <- taskSubset(rankingBootstrapped, tasks=c("T1"))
+  rankingBootstrappedSubset <- subset(rankingBootstrapped, tasks=c("T1"))
 
   expect_equal(length(rankingBootstrappedSubset$matlist), 1)
   expect_is(rankingBootstrappedSubset$matlist$T1, "data.frame")
 
   expect_equal(length(rankingBootstrappedSubset$data), 1)
   expect_is(rankingBootstrappedSubset$data$T1, "data.frame")
 
   expect_equal(length(rankingBootstrappedSubset$bootsrappedRanks), 1)
   expect_is(rankingBootstrappedSubset$bootsrappedRanks$T1, "data.frame")
 
   expect_equal(length(rankingBootstrappedSubset$bootsrappedAggregate), 1)
   expect_is(rankingBootstrappedSubset$bootsrappedAggregate$T1, "data.frame")
 })
 
 test_that("extraction of task subset from bootstrap ranking does not raise an error for invalid task name", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"),
     data.frame(algo="A3", value=0.4, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.1, case="C2"),
     data.frame(algo="A3", value=0.0, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=mean, ties.method="min")
 
   set.seed(1)
   rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
 
-  rankingBootstrappedSubset <- taskSubset(rankingBootstrapped, tasks=c("T1x"))
+  rankingBootstrappedSubset <- subset(rankingBootstrapped, tasks=c("T1x"))
 
   expect_equal(length(rankingBootstrappedSubset$matlist), 1)
   expect_equal(rankingBootstrappedSubset$matlist$T1, NULL)
 
   expect_equal(length(rankingBootstrappedSubset$data), 1)
   expect_equal(rankingBootstrappedSubset$data$T1, NULL)
 
   expect_equal(length(rankingBootstrappedSubset$bootsrappedRanks), 1)
   expect_equal(rankingBootstrappedSubset$bootsrappedRanks$T1, NULL)
 
   expect_equal(length(rankingBootstrappedSubset$bootsrappedAggregate), 1)
   expect_equal(rankingBootstrappedSubset$bootsrappedAggregate$T1, NULL)
 })
+