diff --git a/tests/testthat/test-rankThenAggregate.R b/tests/testthat/test-rankThenAggregate.R
index 1158fea..40fa7f6 100644
--- a/tests/testthat/test-rankThenAggregate.R
+++ b/tests/testthat/test-rankThenAggregate.R
@@ -1,61 +1,73 @@
 test_that("rank-then-aggregate by mean works with two algorithms for one case, small values are better", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind(
     "A1" = data.frame(rank_mean = 1, rank = 1),
     "A2" = data.frame(rank_mean = 2, rank = 2))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms (reverse order) for one case, small values are better", {
   data <- rbind(
             data.frame(algo = "A2", value = 0.8, case = "C1"),
             data.frame(algo = "A1", value = 0.6, case = "C1"))
 
   challenge <- as.challenge(data, algorithm = "algo", case = "case", value = "value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind("A2" = data.frame(rank_mean = 2, rank = 2),
                            "A1" = data.frame(rank_mean = 1, rank = 1))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms for one case, large values are better", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
   challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = FALSE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind(
     "A1" = data.frame(rank_mean = 2, rank = 2),
     "A2" = data.frame(rank_mean = 1, rank = 1))
 
   expect_equal(ranking$mat, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms (reverse order) for one case, large values are better", {
   data <- rbind(
     data.frame(algo = "A2", value = 0.8, case = "C1"),
     data.frame(algo = "A1", value = 0.6, case = "C1"))
 
   challenge <- as.challenge(data, algorithm = "algo", case = "case", value = "value", smallBetter = FALSE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind("A2" = data.frame(rank_mean = 1, rank = 1),
                            "A1" = data.frame(rank_mean = 2, rank = 2))
 
   expect_equal(ranking$mat, expectedRanking)
 })
+
+test_that("rank-then-aggregate raises error for invalid aggregation function", {
+  data <- rbind(
+    data.frame(algo="A1", value=0.6, case="C1"),
+    data.frame(algo="A2", value=0.6, case="C1"))
+
+  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+
+  expect_error(challenge%>%rankThenAggregate(FUN = meanx),
+               "object 'meanx' not found", fixed = TRUE)
+})
+