diff --git a/tests/testthat/test-subset.R b/tests/testthat/test-subset.R
index e4c1712..d94c1eb 100644
--- a/tests/testthat/test-subset.R
+++ b/tests/testthat/test-subset.R
@@ -1,168 +1,194 @@
 test_that("top 2 performing algorithms are extracted and data set is reduced respectively", {
   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 <- subset(ranking, top=2)
 
   expectedRankingSubset <- rbind(
     "A1" = data.frame(value_FUN = 0.5, rank = 1),
     "A2" = data.frame(value_FUN = 0.35, rank = 2))
 
   expect_equal(rankingSubset$matlist$T1, expectedRankingSubset)
 
   expect_equal(as.vector(rankingSubset$data$T1$algo), c("A1", "A2", "A1", "A2"))
   expect_equal(as.vector(rankingSubset$data$T1$value), c(0.8, 0.6, 0.2, 0.1))
   expect_equal(as.vector(rankingSubset$data$T1$case), c("C1", "C1", "C2", "C2"))
   expect_equal(as.vector(rankingSubset$data$T1$task), c("T1", "T1", "T1", "T1"))
 })
 
 test_that("extraction of subset raises error 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")
 
   expect_error(subset(ranking, top=2),
                "Subset of algorithms only sensible for single-task challenges.", fixed=TRUE)
 })
 
 test_that("extraction of subset returns all algorithms even when more are requested", {
   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 <- subset(ranking, top=4)
 
   expectedRankingSubset <- rbind(
     "A1" = data.frame(value_FUN = 0.5, rank = 1),
     "A2" = data.frame(value_FUN = 0.35, rank = 2),
     "A3" = data.frame(value_FUN = 0.2, rank = 3))
 
   expect_equal(rankingSubset$matlist$T1, expectedRankingSubset)
 })
 
 test_that("extraction of subset returns more algorithms then requested when ties are present", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"),
     data.frame(algo="A3", value=0.8, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.2, case="C2"),
     data.frame(algo="A3", value=0.2, 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 <- subset(ranking, top=2)
 
   expectedRankingSubset <- rbind(
     "A1" = data.frame(value_FUN = 0.5, rank = 1),
     "A2" = data.frame(value_FUN = 0.5, rank = 1),
     "A3" = data.frame(value_FUN = 0.5, rank = 1))
 
   expect_equal(rankingSubset$matlist$T1, expectedRankingSubset)
 })
 
 test_that("top 2 performing algorithms are extracted from bootstrap ranking and data set is reduced respectively", {
   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 <- subset(rankingBootstrapped, top=2)
 
   expectedRankingSubset <- rbind(
     "A1" = data.frame(value_FUN = 0.5, rank = 1),
     "A2" = data.frame(value_FUN = 0.35, rank = 2))
 
   expect_equal(rankingBootstrappedSubset$matlist$T1, expectedRankingSubset)
 
   expect_equal(as.vector(rankingBootstrappedSubset$data$T1$algo), c("A1", "A2", "A1", "A2"))
   expect_equal(as.vector(rankingBootstrappedSubset$data$T1$value), c(0.8, 0.6, 0.2, 0.1))
   expect_equal(as.vector(rankingBootstrappedSubset$data$T1$case), c("C1", "C1", "C2", "C2"))
   expect_equal(as.vector(rankingBootstrappedSubset$data$T1$task), c("T1", "T1", "T1", "T1"))
 
   expect_equal(dim(rankingBootstrappedSubset$bootsrappedRanks$T1), c(2, 10))
   expect_equal(dim(rankingBootstrappedSubset$bootsrappedAggregate$T1), c(2, 10))
 })
 
 test_that("extraction of bootstrap ranking subset raises error 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)
 
   expect_error(subset(subset(rankingBootstrapped, top=2), top=2),
                "Subset of algorithms only sensible for single-task challenges.", fixed=TRUE)
 })
+
+test_that("extraction of bootstrap ranking subset returns all algorithms even when more are requested", {
+  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 <- subset(rankingBootstrapped, top=4)
+
+  expectedRankingSubset <- rbind(
+    "A1" = data.frame(value_FUN = 0.5, rank = 1),
+    "A2" = data.frame(value_FUN = 0.35, rank = 2),
+    "A3" = data.frame(value_FUN = 0.2, rank = 3))
+
+  expect_equal(rankingBootstrappedSubset$matlist$T1, expectedRankingSubset)
+})
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