{ # Labels in different order to confound as.Splits treeSym8 <- ape::read.tree(text="((e, (f, (g, h))), (((a, b), c), d));") treeBal8 <- ape::read.tree(text="(((e, f), (g, h)), ((a, b), (c, d)));") treeOpp8 <- ape::read.tree(text="(((a, f), (c, h)), ((g, b), (e, d)));") treesSBO8 <- structure(list(treeSym8, treeBal8, treeOpp8), class = "multiPhylo") treesSSBB8 <- structure(list(treeSym8, treeSym8, treeBal8, treeBal8), class = "multiPhylo") treeCat8 <- ape::read.tree(text="((((h, g), f), e), (d, (c, (b, a))));") treeTac8 <- ape::read.tree(text="((((e, c), g), a), (h, (b, (d, f))));") treeStar8 <- ape::read.tree(text="(e, c, g, h, b, a, d, f);") treeAb.Cdefgh <- ape::read.tree(text="((a, b), (c, d, e, f, g, h));") treeAbc.Defgh <- ape::read.tree(text="((a, b, c), (d, e, f, g, h));") treeAcd.Befgh <- ape::read.tree(text="((a, c, d), (b, e, f, g, h));") treeAbcd.Efgh <- ape::read.tree(text="((a, b, c, d), (e, f, g, h));") treeTwoSplits <- ape::read.tree(text="(((a, b), c, d), (e, f, g, h));") testTrees <- c(treesSBO8, treeCat8, treeTac8, treeStar8, treeAb.Cdefgh, treeAbc.Defgh, treeAbcd.Efgh, treeAcd.Befgh, treeTwoSplits) } test_that("Split compatibility is correctly established", { expect_true(SplitsCompatible(as.logical(c(0,0,1,1,0)), as.logical(c(0,0,1,1,0)))) expect_true(SplitsCompatible( as.logical(c(0,0,1,1,0)), !as.logical(c(0,0,1,1,0)))) expect_true(SplitsCompatible(as.logical(c(0,0,1,1,0)), as.logical(c(1,0,1,1,0)))) expect_true(SplitsCompatible(!as.logical(c(0,0,1,1,0)), as.logical(c(1,0,1,1,0)))) expect_false(SplitsCompatible(as.logical(c(0,0,1,1,0)), as.logical(c(1,1,0,1,0)))) }) methodsToTest <- list( SharedPhylogeneticInfo, DifferentPhylogeneticInfo, MatchingSplitInfo, MatchingSplitInfoDistance, MutualClusteringInfo, ClusteringInfoDistance, NyeSimilarity, JaccardRobinsonFoulds, MatchingSplitDistance, RobinsonFoulds, InfoRobinsonFoulds, KendallColijn # List last: requires rooted trees. ) NormalizationTest <- function(FUNC, ...) { expect_equal(c(1L, 1L), FUNC(treesSSBB8, normalize = TRUE, ...)[c(1, 6)], tolerance = 1e-7) } test_that("Size mismatch causes error", { treeSym7 <- ape::read.tree(text="((e, (f, g)), (((a, b), c), d));") splits7 <- as.Splits(treeSym7) splits8 <- as.Splits(treeSym8) expect_error(MeilaVariationOfInformation(splits7, splits8), "Split lengths differ") Test <- function(Func) { expect_error(Func(splits8, as.Splits(BalancedTree(9)), 8)) } Test(cpp_robinson_foulds_distance) Test(cpp_robinson_foulds_info) Test(cpp_matching_split_distance) Test(cpp_jaccard_similarity) Test(cpp_msi_distance) Test(cpp_mutual_clustering) Test(cpp_shared_phylo) }) test_that("Metrics handle polytomies", { polytomy8 <- ape::read.tree(text="(a, b, c, d, e, f, g, h);") lapply(list(SharedPhylogeneticInfo, MutualClusteringInfo, MatchingSplitDistance, NyeSimilarity), function(Func) expect_equal(0, Func(treeSym8, polytomy8))) }) test_that(".AllTipsSame()", { expect_true(.AllTipsSame(1:2, NULL)) expect_true(.AllTipsSame(list(1:2, 1:2), NULL)) expect_false(.AllTipsSame(list(1:2, 3:2), NULL)) expect_true(.AllTipsSame(list(1:2, 1:2, 1:2), NULL)) expect_false(.AllTipsSame(list(1:2, 3:2, 1:2), NULL)) expect_false(.AllTipsSame(list(3:2, 1:2, 1:2), NULL)) expect_true(.AllTipsSame(1:2, 1:2)) expect_false(.AllTipsSame(1:2, 1)) expect_true(.AllTipsSame(list(1:2), list(1:2))) expect_true(.AllTipsSame(1:2, list(1:2))) expect_true(.AllTipsSame(1:2, 1:2)) expect_true(.AllTipsSame(list(1:2), 1:2)) expect_false(.AllTipsSame(list(1:2), list(3:2))) expect_false(.AllTipsSame(1:2, list(3:2))) expect_false(.AllTipsSame(1:2, 3:2)) expect_false(.AllTipsSame(list(1:2), 3:2)) expect_true(.AllTipsSame(1:2, list(1:2, 1:2))) expect_true(.AllTipsSame(list(1:2), list(1:2, 1:2))) expect_true(.AllTipsSame(list(1:2, 1:2), list(1:2, 1:2))) expect_true(.AllTipsSame(list(1:2, 1:2), 1:2)) expect_false(.AllTipsSame(1:3, list(1:2, 1:3))) expect_false(.AllTipsSame(1:3, list(1:3, 1:2))) expect_false(.AllTipsSame(list(1:3), list(1:3, 1:2))) expect_true(.AllTipsSame(1:4, list(a = 1:4, b = 4:1, c = c(4L, 1L, 2L, 3L)))) }) test_that(".MaxValue() succeeds", { list1 <- list(sym = treeSym8, bal = treeBal8) list2 <- list(sym = treeSym8, abc = treeAbc.Defgh, abcd = treeAbcd.Efgh) dimNames <- list(names(list1), names(list2)) expect_equal( MutualClusteringInfo(list1, list2[[2]], normalize = FALSE), c(sym = MutualClusteringInfo(treeSym8, treeAbc.Defgh, normalize = FALSE), bal = MutualClusteringInfo(treeBal8, treeAbc.Defgh, normalize = FALSE)) ) expect_equal( as.double(t(MutualClusteringInfo(list1, list2, normalize = FALSE))), as.double(c(MutualClusteringInfo(list1[[1]], list2, normalize = FALSE), MutualClusteringInfo(list1[[2]], list2, normalize = FALSE))) ) expect_equal( .MaxValue(list1, list2[[2]], Value = ClusteringEntropy), c(sym = .MaxValue(treeSym8, treeAbc.Defgh, Value = ClusteringEntropy), bal = .MaxValue(treeBal8, treeAbc.Defgh, Value = ClusteringEntropy)) ) expect_equal( as.double(t(.MaxValue(list1, list2, ClusteringEntropy))), as.double(c(.MaxValue(list1[[1]], list2, ClusteringEntropy), .MaxValue(list1[[2]], list2, ClusteringEntropy))) ) expect_equal(.MaxValue(list1[[1]], NULL, ClusteringEntropy), double(0)) expect_equal(.MaxValue(list1, NULL, ClusteringEntropy), sum(ClusteringEntropy(list1))) expect_equal(.MaxValue(list2, NULL, ClusteringEntropy), c(.MaxValue(list2[-3], NULL, ClusteringEntropy), .MaxValue(list2[-2], NULL, ClusteringEntropy), .MaxValue(list2[-1], NULL, ClusteringEntropy) )) }) #Func <- ClusteringInfoDistance # FUNC = test_that("Output dimensions are correct", { list1 <- list(sym = treeSym8, bal = treeBal8) list2 <- list(sym = treeSym8, abc = treeAbc.Defgh, abcd = treeAbcd.Efgh) dimNames <- list(names(list1), names(list2)) Test <- function(Func) { allPhylo <- matrix( c(Func(treeSym8, treeSym8), Func(treeBal8, treeSym8), Func(treeSym8, treeAbc.Defgh), Func(treeBal8, treeAbc.Defgh), Func(treeSym8, treeAbcd.Efgh), Func(treeBal8, treeAbcd.Efgh)), 2L, 3L, dimnames = dimNames) phylo1 <- matrix(c(Func(treeSym8, list2), Func(treeBal8, list2)), byrow = TRUE, 2L, 3L, dimnames = dimNames) phylo2 <- matrix(c(Func(list1, treeSym8), Func(list1, treeAbc.Defgh), Func(list1, treeAbcd.Efgh)), 2L, 3L, dimnames = dimNames) noPhylo <- Func(list1, list2) expect_equal(allPhylo, phylo1) expect_equal(allPhylo, phylo2) expect_equal(allPhylo, noPhylo) } lapply(methodsToTest, Test) }) test_that("RF Distance is correctly calculated", { skip_if_not_installed("phangorn") PhangRF2 <- function(t1, t2) phangorn::RF.dist(reorder(t1, "cladewise"), reorder(t2, "cladewise")) RFTest <- function(t1, t2) { expect_equal(suppressMessages(PhangRF2(t1, t2)), RobinsonFoulds(t1, t2)) expected <- RobinsonFoulds(t1, t2, reportMatching = TRUE, similarity = TRUE) attr(expected, "pairScores") <- attr(expected, "pairScores") == 0L expect_equal(expected, RobinsonFouldsMatching(t1, t2)) } RFTest(treeSym8, treeSym8) RFTest(treeSym8, treeStar8) RFTest(treeStar8, treeStar8) RFTest(treeAb.Cdefgh, treeAbc.Defgh) RFTest(treeAb.Cdefgh, treeAbcd.Efgh) # at 2020-10, RF uses Day algorithm if tree2 = null; old algo if tree2 = tree1. expect_equal(RobinsonFoulds(testTrees, testTrees), as.matrix(RobinsonFoulds(testTrees)), ignore_attr = TRUE) # Invariant to tree description order sq_pectinate <- ape::read.tree(text="((((((1, 2), 3), 4), 5), 6), (7, (8, (9, (10, 11)))));") shuffle1 <- ape::read.tree(text="(((((1, 5), 2), 6), (3, 4)), ((8, (7, 9)), (10, 11)));") shuffle2 <- ape::read.tree(text="(((8, (7, 9)), (10, 11)), ((((1, 5), 2), 6), (3, 4)));") RFTest(shuffle1, sq_pectinate) RFTest(sq_pectinate, shuffle1) RFTest(shuffle1, shuffle2) RFTest(shuffle1, sq_pectinate) RFTest(shuffle2, sq_pectinate) }) test_that("Shared Phylogenetic Info is correctly calculated", { expect_equal(5.529821, tolerance = 1e-7, cpp_shared_phylo( as.Splits(as.logical(c(1, 1, 1, 1, 0, 0, 0, 0))), as.Splits(as.logical(c(1, 1, 1, 1, 0, 0, 0, 0))), 8L)$score) expect_equal(0.2895066, tolerance = 1e-7, cpp_shared_phylo( as.Splits(as.logical(c(1, 1, 0, 0, 0, 0, 0, 0))), as.Splits(as.logical(c(0, 0, 1, 1, 0, 0, 0, 0))), 8L)$score) expect_equal(1.137504, tolerance = 1e-6, cpp_shared_phylo( as.Splits(as.logical(c(1, 1, 0, 0, 0, 0, 0, 0))), as.Splits(as.logical(c(1, 1, 1, 1, 0, 0, 0, 0))), 8L)$score) expect_equal(3.45943, tolerance = 1e-6, cpp_shared_phylo( as.Splits(as.logical(c(1, 1, 0, 0, 0, 0, 0, 0))), as.Splits(as.logical(c(1, 1, 0, 0, 0, 0, 0, 0))), 8L)$score) expect_equal(22.53747, tolerance = 1e-05, SharedPhylogeneticInfo(treeSym8, treeSym8, normalize = FALSE)) expect_equal(1, tolerance = 1e-05, SharedPhylogeneticInfo(treeSym8, treeSym8, normalize = TRUE)) expect_equal(0, SharedPhylogeneticInfo(treeSym8, treeStar8, normalize = TRUE)) expect_equal(0, SharedPhylogeneticInfo(treeStar8, treeStar8, normalize = FALSE)) expect_equal(NaN, # Division by zero SharedPhylogeneticInfo(treeStar8, treeStar8, normalize = TRUE)) expect_equal(13.75284, SharedPhylogeneticInfo(treeSym8, treeBal8), tolerance=1e-05) expect_equal(DifferentPhylogeneticInfo(treeSym8, treeAcd.Befgh), DifferentPhylogeneticInfo(treeAcd.Befgh, treeSym8), tolerance=1e-05) expect_equal(0, DifferentPhylogeneticInfo(treeSym8, treeSym8, normalize = TRUE)) infoSymBal <- SplitwiseInfo(treeSym8) + SplitwiseInfo(treeBal8) expect_equal(infoSymBal - 13.75284 - 13.75284, tolerance = 1e-05, DifferentPhylogeneticInfo(treeSym8, treeBal8, normalize = TRUE) * infoSymBal) expect_equal(22.53747 + SharedPhylogeneticInfo(treeAcd.Befgh, treeAcd.Befgh) - (2 * SharedPhylogeneticInfo(treeSym8, treeAcd.Befgh)), DifferentPhylogeneticInfo(treeSym8, treeAcd.Befgh), tolerance=1e-06) expect_equal(-log2(945/10395), SharedPhylogeneticInfo(treeSym8, treeAb.Cdefgh), tolerance = 1e-06) expect_equal(22.53747 + SharedPhylogeneticInfo(treeBal8, treeBal8) - 13.75284 - 13.75284, DifferentPhylogeneticInfo(treeSym8, treeBal8), tolerance=1e-05) expect_equal(-log2(945/10395), SharedPhylogeneticInfo(treeSym8, treeAb.Cdefgh), tolerance = 1e-06) expect_equal(-log2(315/10395), SharedPhylogeneticInfo(treeSym8, treeAbc.Defgh), tolerance = 1e-06) expect_equal(0, DifferentPhylogeneticInfo(treeSym8, treeSym8)) expect_equal(SplitwiseInfo(treeSym8) - SplitwiseInfo(treeAcd.Befgh), DifferentPhylogeneticInfo(treeSym8, treeAbc.Defgh), tolerance = 1e-06) # Test symmetry of small vs large splits expect_equal(SharedPhylogeneticInfo(treeSym8, treeAbc.Defgh), SharedPhylogeneticInfo(treeAbc.Defgh, treeSym8)) expect_equal(-log2(225/10395), SharedPhylogeneticInfo(treeSym8, treeAbcd.Efgh)) expect_equal(-log2(225/10395) - log2(945/10395), SharedPhylogeneticInfo(treeSym8, treeTwoSplits), tolerance = 1e-7) expect_equal(SplitSharedInformation(8, 4, 3), SharedPhylogeneticInfo(treeTwoSplits, treeAbc.Defgh), tolerance = 1e-7) expect_equal(SplitInformation(4, 4) + SplitInformation (3, 5) - (2 * SplitSharedInformation(8, 4, 3)), SplitDifferentInformation(8, 4, 3), tolerance=1e-07) expect_equal(SharedPhylogeneticInfo(treeSym8, list(treeSym8, treeBal8)), SharedPhylogeneticInfo(list(treeSym8, treeBal8), treeSym8), tolerance = 1e-7) # Test tree too large to cache set.seed(101) t1 <- ape::rtree(101) t2 <- ape::rtree(101, rooted = FALSE) expect_equal(SharedPhylogeneticInfo(t1, t2), SharedPhylogeneticInfo(t2, t1)) }) test_that("MatchingSplitInfo() is correctly calculated", { BinaryToSplit <- function(binary) matrix(as.logical(binary)) expect_equal(log2(3), MatchingSplitInfoSplits( as.Splits(c(rep(TRUE, 2), rep(FALSE, 6))), as.Splits(c(FALSE, FALSE, rep(TRUE, 2), rep(FALSE, 4)))), tolerance = 1e-7) expect_equal(log2(3), MatchingSplitInfoSplits( as.Splits(c(rep(FALSE, 6), rep(TRUE, 2))), as.Splits(c(FALSE, FALSE, rep(TRUE, 2), rep(FALSE, 4)))), tolerance = 1e-7) expect_equal(log2(3), cpp_msi_distance( as.Splits(c(rep(TRUE, 2), rep(FALSE, 6))), as.Splits(c(FALSE, FALSE, rep(TRUE, 2), rep(FALSE, 4))), 8L)$score, tolerance = 1e-7) expect_equal(log2(3), cpp_msi_distance( as.Splits(rep(c(FALSE, TRUE), each = 4L)), as.Splits(rep(c(FALSE, TRUE), 4L)), 8L)$score, tolerance = 1e-7) expect_equal(SharedPhylogeneticInfo(treeSym8, treeSym8), MatchingSplitInfo(treeSym8, treeSym8), tolerance = 1e-05) expect_equal(0, MatchingSplitInfo(treeSym8, treeStar8)) expect_equal(0, MatchingSplitInfo(treeStar8, treeStar8)) expect_equal(MatchingSplitInfo(treeAb.Cdefgh, treeAbc.Defgh), MatchingSplitInfo(treeAbc.Defgh, treeAb.Cdefgh)) expect_equal(MatchingSplitInfo(treeAbcd.Efgh, treeAb.Cdefgh), MatchingSplitInfo(treeAb.Cdefgh, treeAbcd.Efgh)) expect_equal(-(TreeTools::Log2TreesMatchingSplit(2, 5) - Log2Unrooted.int(7)), MatchingSplitInfo(treeAb.Cdefgh, treeAbc.Defgh), tolerance = 1e-7) expect_true(MatchingSplitInfo(treeSym8, treeBal8) > MatchingSplitInfo(treeSym8, treeOpp8)) expect_equal(0, MatchingSplitInfoDistance(treeSym8, treeSym8)) NormalizationTest(MatchingSplitInfo) }) test_that("Shared Phylogenetic Information is correctly estimated", { exp <- ExpectedVariation(treeSym8, treeAbc.Defgh, samples = 1000L) tol <- exp[, "Std. Err."] * 2 # Expected values calculated with 100k samples expect_equal(1.175422, exp["SharedPhylogeneticInfo", "Estimate"], tolerance = tol[1]) expect_equal(3.099776, exp["MatchingSplitInfo", "Estimate"], tolerance = tol[2]) expect_equal(25.231023, exp["DifferentPhylogeneticInfo", "Estimate"], tolerance = tol[3]) expect_equal(21.382314, exp["MatchingSplitInfoDistance", "Estimate"], tolerance = tol[4]) expect_equal(exp[, "sd"], exp[, "Std. Err."] * sqrt(exp[, "n"])) }) test_that("Clustering information is correctly calculated", { expect_equal(Entropy(c(3, 5) / 8) * 2 - Entropy(c(0, 0, 3, 5) / 8), cpp_mutual_clustering( as.Splits(as.logical(c(1, 1, 1, 0, 0, 0, 0, 0))), as.Splits(as.logical(c(1, 1, 1, 0, 0, 0, 0, 0))), 8L)$score, tolerance = 1e-7) expect_equal(Entropy(c(2, 6) / 8) * 2 - Entropy(c(0, 2, 2, 4) / 8), cpp_mutual_clustering( as.Splits(as.logical(c(1, 1, 0, 0, 0, 0, 0, 0))), as.Splits(as.logical(c(0, 0, 1, 1, 0, 0, 0, 0))), 8L)$score, tolerance = 1e-7) expect_equal(Entropy(c(5, 4) / 9) + Entropy(c(3, 6) / 9) - Entropy(c(3, 2, 0, 4) / 9), cpp_mutual_clustering( as.Splits(as.logical(c(1, 1, 1, 1, 1, 0, 0, 0, 0))), as.Splits(as.logical(c(0, 0, 1, 1, 1, 0, 0, 0, 0))), 9L)$score, tolerance = 1e-7) expect_equal(Entropy(c(4, 4) / 8) * 2 - Entropy(c(2, 2, 2, 2) / 8), cpp_mutual_clustering( as.Splits(as.logical(c(1, 1, 1, 1, 0, 0, 0, 0))), as.Splits(as.logical(c(1, 0, 1, 0, 1, 0, 1, 0))), 8L)$score, tolerance = 1e-7) expect_equal(Entropy(c(4, 4) / 8) * 2 - Entropy(c(0, 0, 4, 4) / 8), cpp_mutual_clustering( as.Splits(as.logical(c(1, 1, 1, 1, 0, 0, 0, 0))), as.Splits(as.logical(c(1, 1, 1, 1, 0, 0, 0, 0))), 8L)$score, tolerance = 1e-7) expect_equal(ClusteringEntropy(treeSym8), MutualClusteringInfo(treeSym8, treeSym8), tolerance = 1e-05) expect_equal(8 * ClusteringEntropy(treeSym8), ClusteringInfo(treeSym8)) expect_equal(0, MutualClusteringInfo(treeSym8, treeStar8)) expect_equal(0, MutualClusteringInfo(treeStar8, treeStar8)) expect_equal(TreeDistance(treeSym8, treeBal8), ClusteringInfoDistance(treeSym8, treeBal8, normalize = TRUE)) expect_equal(1, MutualClusteringInfo(treeSym8, treeSym8, normalize = TRUE), tolerance = 1e-7) expect_true(MutualClusteringInfo(treeSym8, treeBal8, normalize = pmin) > MutualClusteringInfo(treeSym8, treeBal8, normalize = pmax)) expect_equal(ClusteringEntropy(treeSym8) + ClusteringEntropy(treeBal8) - (2 * MutualClusteringInfo(treeBal8, treeSym8)), ClusteringInfoDistance(treeSym8, treeBal8), tolerance = 1e-05) expect_equal(MutualClusteringInfo(treeAb.Cdefgh, treeAbc.Defgh), MutualClusteringInfo(treeAbc.Defgh, treeAb.Cdefgh), tolerance = 1e-05) zeroTree <- TreeTools::ZeroTaxonTree() expect_equal(MutualClusteringInfo(zeroTree, zeroTree), 0) expect_equal(ClusteringInfoDistance(zeroTree, zeroTree), 0) # Different resolution randomBif20 <- structure(list( edge = structure(c(21L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 32L, 31L, 30L, 29L, 33L, 34L, 34L, 33L, 28L, 35L, 36L, 36L, 35L, 27L, 26L, 37L, 37L, 25L, 38L, 38L, 39L, 39L, 24L, 23L, 22L, 1L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 2L, 14L, 7L, 10L, 33L, 34L, 4L, 6L, 8L, 35L, 36L, 13L, 16L, 18L, 17L, 37L, 5L, 15L, 38L, 11L, 39L, 12L, 19L, 9L, 3L, 20L), .Dim = c(38L, 2L)), Nnode = 19L, tip.label = c("t1", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9", "t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18", "t19", "t20"), br = NULL), class = "phylo") # NOT in Preorder. Preordering the tree will change the matching. threeAwayPoly <- structure( list(edge = structure(c(21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 29L, 28L, 27L, 26L, 30L, 30L, 30L, 26L, 31L, 31L, 25L, 32L, 33L, 33L, 32L, 25L, 25L, 24L, 34L, 34L, 34L, 23L, 22L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 2L, 8L, 14L, 10L, 30L, 13L, 16L, 18L, 31L, 4L, 6L, 32L, 33L, 15L, 20L, 5L, 7L, 17L, 34L, 11L, 12L, 19L, 9L, 3L, 1L), .Dim = c(33L, 2L)), tip.label = c("t1", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9", "t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18", "t19", "t20"), Nnode = 14L), class = "phylo") expect_equal( MutualClusteringInfo(threeAwayPoly, randomBif20), MutualClusteringInfo(randomBif20, threeAwayPoly)) match <- MutualClusteringInfo(randomBif20, threeAwayPoly, reportMatching = TRUE) expect_equal(c(NA, NA, 1, 2, NA, 3, 7, 11, 10, 4, 6, 9, 8, NA, 5, 12, NA), attr(match, "matching")) # Multiple bins, calculated expectation library("TreeTools", quietly = TRUE) b65m <- lapply(c(1, 2, 70), AddTip, tree = BalancedTree(64)) self <- ClusteringEntropy(b65m) diff <- ClusteringEntropy(b65m[[1]], sum = FALSE)["72"] # Copied from C: ic_element <- function(nkK, nk, nK, n) { if (nkK && nk && nK) { if (nkK == nk && nkK == nK && nkK + nkK == n) return (nkK); numerator = nkK * n denominator = nk * nK if (numerator == denominator) return (0); nkK * (log2(numerator) - log2(denominator)); } else 0; } expect_equal(diff, (ic_element(63, 63, 63, 65) + ic_element(00, 63, 02, 65) + ic_element(00, 02, 63, 65) + ic_element(02, 02, 02, 65)) / 65, ignore_attr = TRUE) new <- (ic_element(65-3, 63, 63, 65) + ic_element(1, 63, 02, 65) + ic_element(1, 02, 63, 65) + ic_element(1, 02, 02, 65)) / 65 other <- self[1] - diff[1] + new # Calc'd = 20.45412 expect_equal(other, MutualClusteringInfo(b65m[[1]], b65m[[2]]), ignore_attr = TRUE) expectation <- matrix(other, 3, 3) diag(expectation) <- self expect_equal(expectation, MutualClusteringInfo(b65m), ignore_attr = TRUE) expect_equal(ClusteringEntropy(BalancedTree(64)), MutualClusteringInfo(BalancedTree(64), BalancedTree(64))) expect_equal(ClusteringEntropy(BalancedTree(644)), MutualClusteringInfo(BalancedTree(644), BalancedTree(644))) expect_gt(ClusteringEntropy(BalancedTree(64)), MutualClusteringInfo(BalancedTree(64), PectinateTree(64))) expect_gt(ClusteringEntropy(BalancedTree(644)), MutualClusteringInfo(BalancedTree(644), PectinateTree(644))) NormalizationTest(MutualClusteringInfo) }) test_that("Matchings are correct", { # Different resolution: used to cause memory leak randomBif20 <- structure(list( edge = structure(c(21L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 32L, 31L, 30L, 29L, 33L, 34L, 34L, 33L, 28L, 35L, 36L, 36L, 35L, 27L, 26L, 37L, 37L, 25L, 38L, 38L, 39L, 39L, 24L, 23L, 22L, 1L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 2L, 14L, 7L, 10L, 33L, 34L, 4L, 6L, 8L, 35L, 36L, 13L, 16L, 18L, 17L, 37L, 5L, 15L, 38L, 11L, 39L, 12L, 19L, 9L, 3L, 20L), .Dim = c(38L, 2L)), Nnode = 19L, tip.label = c("t1", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9", "t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18", "t19", "t20"), br = NULL), class = "phylo") threeAwayPoly <- structure( list(edge = structure(c(21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 29L, 28L, 27L, 26L, 30L, 30L, 30L, 26L, 31L, 31L, 25L, 32L, 33L, 33L, 32L, 25L, 25L, 24L, 34L, 34L, 34L, 23L, 22L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 2L, 8L, 14L, 10L, 30L, 13L, 16L, 18L, 31L, 4L, 6L, 32L, 33L, 15L, 20L, 5L, 7L, 17L, 34L, 11L, 12L, 19L, 9L, 3L, 1L), .Dim = c(33L, 2L)), tip.label = c("t1", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9", "t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18", "t19", "t20"), Nnode = 14L), class = "phylo") expect_equal( MutualClusteringInfo(threeAwayPoly, randomBif20), MutualClusteringInfo(randomBif20, threeAwayPoly)) library("TreeTools", quietly = TRUE) t1 <- PectinateTree(letters[1:11]) t2 <- ape::read.tree(text = "(a, (c, (b, (d, e, ((g, h, f), (k, (j, i)))))));") t3 <- CollapseNode(PectinateTree(c(letters[11], letters[1:10])), 16:19) s0 <- as.Splits(ZeroTaxonTree()) s1 <- as.Splits(t1) s2 <- as.Splits(t2, t1) s3 <- as.Splits(t3, t1) n1 <- dim(s1)[1] n2 <- dim(s2)[1] n3 <- dim(s3)[1] n <- NTip(s1) # Plot # par(mfrow = 2:1, cex = 0.9, mar = rep(0,4)) # JRF2T <- function(...) JaccardRobinsonFoulds(..., k = 2) # JRF2F <- function(...) JaccardRobinsonFoulds(..., k = 2, allowConflict = FALSE) # VisualizeMatching(MatchingSplitDistance, t1, t2, setPar=F) # LabelSplits(t2, setNames(1:6, names(s2)), adj = 2) # VisualizeMatching(MatchingSplitDistance, t2, t1, setPar=F) # LabelSplits(t1, setNames(1:8, names(s1)), adj = 2) Test <- function(CppFn, x12, x21, ...) { r0 <- CppFn(s0, s0, 0, ...) expect_equal(r0$score, 0) r12 <- CppFn(s1, s2, n, ...) r21 <- CppFn(s2, s1, n, ...) r13 <- CppFn(s1, s3, n, ...) r31 <- CppFn(s3, s1, n, ...) expect_equal(r12$score, r21$score) expect_equal(r13$score, r31$score) m12 <- r12$matching m21 <- r21$matching expect_equal(n1, length(m12)) expect_equal(length(m12[!is.na(m12)]), length(unique(m12[!is.na(m12)]))) expect_equal(n2, length(m21)) expect_equal(length(m21[!is.na(m21)]), length(unique(m21[!is.na(m21)]))) expect_lte(dim(s1)[1] - dim(s2)[1], sum(is.na(m12))) m13 <- r13$matching m31 <- r31$matching expect_equal(n1, length(m13)) expect_equal(length(m13[!is.na(m13)]), length(unique(m13[!is.na(m13)]))) expect_equal(n3, length(m31)) expect_equal(length(m31[!is.na(m31)]), length(unique(m31[!is.na(m31)]))) expect_lte(dim(s1)[1] - dim(s3)[1], sum(is.na(m13))) for (i in seq_along(m12)) expect_true(m12[i] %in% x12[[i]]) for (i in seq_along(m21)) expect_true(m21[i] %in% x21[[i]]) } Test(TreeDist:::cpp_robinson_foulds_distance, list(NA, 2, NA, 3, NA, NA, 5, NA), list(NA, 2, 4, NA, 7, NA) ) Test(TreeDist:::cpp_robinson_foulds_info, list(NA, 2, NA, 3, NA, NA, 5, NA), list(NA, 2, 4, NA, 7, NA) ) Test(TreeDist:::cpp_matching_split_distance, list(1, 2, 4, 3, NA, NA, 5, 6), list(1, 2, 5, 4, 7, 6) ) Test(TreeDist:::cpp_jaccard_similarity, list(NA, 2, 1, 3, 4, 6, 5, NA), list(3, 2, 4, 5, 7, 6), k = 2, allowConflict = TRUE) Test(TreeDist:::cpp_jaccard_similarity, list(NA, 2, 1, 3, NA, 6, 5, 4), list(3, 2, 4, 1, 7, 6), k = 2, allowConflict = FALSE) Test(TreeDist:::cpp_msi_distance, list(NA, 2, 1, 4, 3, 6, 5, NA), list(3, 2, c(4, 5), c(4, 5), c(6, 7), c(7, 6)) ) Test(TreeDist:::cpp_shared_phylo, list(NA, 2, 4, 3, 1, 6, 5, NA), list(5, 2, 4, 3, 7, 6) ) Test(TreeDist:::cpp_mutual_clustering, list(4, 2, NA, 3, 6, NA, 5, 1), list(8, 2, 4, 5, 7, 1) ) }) test_that("Matching Split Distance is correctly calculated", { expect_equal(MatchingSplitDistance(treeSym8, treeSym8), 0L) expect_equal(MatchingSplitDistance(treeStar8, treeSym8), 0L) expect_equal(MatchingSplitDistance(treeStar8, treeStar8), 0L) match0 <- MatchingSplitDistance(treeStar8, treeStar8, reportMatching = TRUE) expect_equal(c(match0, vapply(attributes(match0), length, 0)), rep(0L, 5), ignore_attr = TRUE) expect_equal(MatchingSplitDistance(treeAb.Cdefgh, treeAbc.Defgh), 1L) expect_equal(MatchingSplitDistance(treeAb.Cdefgh, treeAbcd.Efgh), 2L) splitAB <- as.Splits(c(rep(TRUE, 2), rep(FALSE, 7))) splitABC <- as.Splits(c(rep(TRUE, 3), rep(FALSE, 6))) splitAEF <- as.Splits(c(TRUE, rep(FALSE, 3), TRUE, TRUE, rep(FALSE, 3))) splitABCD <- as.Splits(c(rep(TRUE, 4), rep(FALSE, 5))) splitABCDE <- as.Splits(c(rep(TRUE, 5), rep(FALSE, 4))) splitAI <- as.Splits(c(TRUE, rep(FALSE, 7), TRUE)) expect_equal(MatchingSplitDistanceSplits(splitAB, splitAI), 2L) expect_equal(MatchingSplitDistanceSplits(splitAB, splitABCD), 2L) expect_equal(MatchingSplitDistanceSplits(splitAB, splitABCDE), 3L) expect_equal(MatchingSplitDistanceSplits(splitABC, splitAEF), 4L) expect_equal(MatchingSplitDistanceSplits(splitABC, splitAEF), MatchingSplitDistanceSplits(splitAEF, splitABC)) # Invariant to tree description order sq_pectinate <- ape::read.tree( text = "((((((1, 2), 3), 4), 5), 6), (7, (8, (9, (10, 11)))));" ) shuffle1 <- ape::read.tree( text = "(((((1, 5), 2), 6), (3, 4)), ((8, (7, 9)), (10, 11)));" ) shuffle2 <- ape::read.tree( text = "(((8, (7, 9)), (10, 11)), ((((1, 5), 2), 6), (3, 4)));" ) expect_equal(MatchingSplitDistance(shuffle1, sq_pectinate), MatchingSplitDistance(sq_pectinate, shuffle1)) expect_equal(MatchingSplitDistance(shuffle1, shuffle2), 0L) expect_equal(MatchingSplitDistance(shuffle1, sq_pectinate), MatchingSplitDistance(shuffle2, sq_pectinate)) }) test_that("NyeSimilarity is correctly calculated, and matches JRF", { listBalSym <- list(treeBal8, treeSym8) JRF <- function(..., sim = TRUE) JaccardRobinsonFoulds(..., k = 1, similarity = sim, allowConflict = TRUE) expect_equal(5L, NyeSimilarity(as.Splits(treeSym8), treeSym8)) expect_equal(1, NyeSimilarity(treeSym8, treeSym8, normalize = TRUE)) expect_equal(1, JRF(treeSym8, treeSym8, normalize = TRUE)) expect_equal(0, NyeSimilarity(treeSym8, treeStar8, normalize = FALSE)) expect_equal(0, NyeSimilarity(treeSym8, treeStar8, normalize = TRUE)) expect_equal(0, JRF(treeSym8, treeStar8, normalize = TRUE)) expect_equal(0, NyeSimilarity(treeStar8, treeStar8, normalize = FALSE)) expect_equal(NaN, NyeSimilarity(treeStar8, treeStar8, normalize = TRUE, normalizeMax = FALSE)) expect_equal(c(3.8, 5), NyeSimilarity(treeSym8, listBalSym)) expect_equal(2 / 3, NyeSimilarity(treeAb.Cdefgh, treeAbc.Defgh), tolerance = 1e-7) expect_equal(2 * (1 / 3), tolerance = 1e-7, NyeSimilarity(treeAb.Cdefgh, treeAbc.Defgh, similarity = FALSE)) expect_equal(1L, NyeSimilarity(treeSym8, treeAbcd.Efgh, normalize = FALSE)) expect_equal(1L / 5L, NyeSimilarity(treeSym8, treeAbcd.Efgh, normalize = 5L)) expect_equal(0.2, JRF(treeSym8, treeAbcd.Efgh, normalize = 5L * 2L)) expect_equal(1/3, NyeSimilarity(treeSym8, treeAbcd.Efgh, normalize = TRUE)) expect_equal(1/3, JRF(treeSym8, treeAbcd.Efgh, normalize = TRUE)) expect_equal(2/3, NyeSimilarity(treeSym8, treeAbcd.Efgh, similarity = FALSE, normalize = TRUE)) expect_equal(2/3, JRF(treeSym8, treeAbcd.Efgh, sim = FALSE, normalize = TRUE)) expect_equal(1L / ((5L + 1L) / 2L), NyeSimilarity(treeSym8, treeAbcd.Efgh, normalize = TRUE)) expect_true(NyeSimilarity(treeSym8, treeBal8) > NyeSimilarity(treeSym8, treeOpp8)) NormalizationTest(NyeSimilarity) }) test_that("Jaccard RF extremes tend to equivalent functions", { expect_equal(JaccardRobinsonFoulds(treeSym8, list(treeBal8, treeSym8), similarity = TRUE, k = 1L, allowConflict = TRUE), NyeSimilarity(treeSym8, list(treeBal8, treeSym8)) * 2L) expect_equal(JaccardRobinsonFoulds(treeSym8, list(treeBal8, treeSym8), similarity = FALSE, k = Inf), RobinsonFoulds(treeSym8, list(treeBal8, treeSym8))) expect_equal(JaccardRobinsonFoulds(treeSym8, list(treeBal8, treeSym8), similarity = FALSE, k = 999999), RobinsonFoulds(treeSym8, list(treeBal8, treeSym8))) }) test_that("Jaccard RF is correctly calculated", { expect_equal(5L * 2L, JaccardRobinsonFoulds(treeSym8, treeSym8, k = 2, similarity = TRUE)) expect_equal(c(3.32, 5) * 2L, JaccardRobinsonFoulds(treeSym8, list(treeBal8, treeSym8), similarity = TRUE, k = 2)) expect_equal(2 * 2, 3 * JaccardRobinsonFoulds(treeAb.Cdefgh, treeAbc.Defgh, similarity = TRUE), tolerance = 1e-7) expect_equal(1, JaccardRobinsonFoulds(treeSym8, treeSym8, similarity = TRUE, normalize = TRUE)) expect_equal(0, JaccardRobinsonFoulds(treeSym8, treeSym8, similarity = FALSE, normalize = TRUE)) expect_equal(1L * 2L, JaccardRobinsonFoulds(treeSym8, treeAbcd.Efgh, similarity = TRUE, normalize = FALSE, k = 2)) expect_equal(1L * 2L / 6L, JaccardRobinsonFoulds(treeSym8, treeAbcd.Efgh, similarity = TRUE, normalize = TRUE, k = 2)) expect_lt(JaccardRobinsonFoulds(treeSym8, treeBal8, k = 2), JaccardRobinsonFoulds(treeSym8, treeOpp8, k = 2)) expect_lt(JaccardRobinsonFoulds(treeSym8, treeBal8, k = 3L), JaccardRobinsonFoulds(treeSym8, treeBal8, k = 4L)) expect_lt(JaccardRobinsonFoulds(treeCat8, treeTac8, allowConflict = TRUE), JaccardRobinsonFoulds(treeCat8, treeTac8, allowConflict = FALSE)) library("TreeTools", quietly = TRUE) expect_equal(0, JaccardRobinsonFoulds(BalancedTree(64), BalancedTree(64))) expect_lt(0, JaccardRobinsonFoulds(BalancedTree(64), PectinateTree(64))) expect_equal(0, JaccardRobinsonFoulds(BalancedTree(264), BalancedTree(264))) expect_lt(0, JaccardRobinsonFoulds(BalancedTree(264), PectinateTree(264))) }) test_that("RobinsonFoulds() is correctly calculated", { skip_if_not_installed("phangorn") RF <- function(tree1, tree2) { suppressMessages(phangorn::RF.dist(reorder(tree1, "cladewise"), reorder(tree2, "cladewise"))) } RFTest <- function(tree1, tree2) { expect_equal(RF(tree1, tree2), RobinsonFoulds(tree1, tree2)) } RFTest(treeSym8, treeSym8) RFTest(treeBal8, treeSym8) expect_equal(c(4, 0), RobinsonFoulds(treeSym8, list(treeBal8, treeSym8))) RFTest(treeAb.Cdefgh, treeAbc.Defgh) expect_equal(0, RobinsonFoulds(treeSym8, treeSym8, normalize = TRUE)) expect_equal(4L / 6L, RobinsonFoulds(treeSym8, treeAbcd.Efgh, normalize = TRUE)) RFTest(treeSym8, treeOpp8) RFNtipTest <- function(nTip) { backLeaves <- paste0("t", rev(seq_len(nTip))) RFTest(TreeTools::PectinateTree(backLeaves), TreeTools::BalancedTree(nTip)) } RFNtipTest(10) RFNtipTest(32) RFNtipTest(50) RFNtipTest(64) RFNtipTest(67) RFNtipTest(128) RFNtipTest(1024) RFNtipTest(1027) NormalizationTest(RobinsonFoulds, similarity = TRUE) #TODO we may wish to revise this test once we implement diag = TRUE to #allow similarities to be calculated on the diagonal. expect_equal(numeric(0), RobinsonFoulds(treeSym8, normalize = TRUE)) }) test_that("Robinson Foulds Info is correctly calculated", { expect_equal(22.53747 * 2L, tolerance = 1e-05, InfoRobinsonFoulds(treeSym8, treeSym8, similarity = TRUE, normalize = FALSE)) expect_equal(0, tolerance = 1e-05, InfoRobinsonFoulds(treeSym8, treeSym8, normalize = TRUE)) expect_equal(1, tolerance = 1e-05, InfoRobinsonFoulds(treeSym8, treeSym8, similarity = TRUE, normalize = TRUE)) expect_equal(24.9, tolerance = 0.01, InfoRobinsonFoulds(treeSym8, treeBal8, similarity = TRUE)) expect_equal(SplitwiseInfo(treeSym8) + SplitwiseInfo(treeBal8) - InfoRobinsonFoulds(treeSym8, treeBal8, similarity = FALSE), InfoRobinsonFoulds(treeSym8, treeBal8, similarity = TRUE)) expect_equal(-log2(945/10395) * 2, InfoRobinsonFoulds(treeSym8, treeAb.Cdefgh, similarity = TRUE)) expect_equal(-log2(945/10395) * 2, InfoRobinsonFoulds(treeSym8, treeAb.Cdefgh, similarity = TRUE)) expect_equal(-log2(315/10395) * 2, InfoRobinsonFoulds(treeSym8, treeAbc.Defgh, similarity = TRUE)) # Test symmetry of small vs large splits expect_equal(InfoRobinsonFoulds(treeSym8, treeAbc.Defgh), InfoRobinsonFoulds(treeAbc.Defgh, treeSym8)) expect_equal(-log2(225/10395) * 2, InfoRobinsonFoulds(treeSym8, treeAbcd.Efgh, similarity = TRUE)) expect_equal((-log2(225/10395) - log2(945/10395)) * 2, InfoRobinsonFoulds(treeSym8, treeTwoSplits, similarity = TRUE)) expect_equal(InfoRobinsonFoulds(treeSym8, list(treeSym8, treeBal8)), RobinsonFouldsInfo(list(treeSym8, treeBal8), treeSym8)) # Check that large trees work library("TreeTools", quietly = TRUE) expect_equal(0, InfoRobinsonFoulds(BalancedTree(64), BalancedTree(64))) expect_lt(0, InfoRobinsonFoulds(BalancedTree(64), PectinateTree(64))) expect_equal(0, InfoRobinsonFoulds(BalancedTree(129), BalancedTree(129))) expect_lt(0, InfoRobinsonFoulds(BalancedTree(129), PectinateTree(129))) }) test_that("Kendall-Colijn distance is correctly calculated", { # Expected values calculated using treespace::treeDist(treeSym8, treeBal8) expect_equal(2.828427, KendallColijn(treeSym8, treeBal8), tolerance=1e-06) expect_equal(2.828427, KendallColijn(treeCat8, treeBal8), tolerance=1e-06) expect_equal(7.211103, KendallColijn(treeSym8, treeOpp8), tolerance=1e-06) expect_equal(matrix(c(0L, 8L), nrow=2, ncol=2, byrow=TRUE), KendallColijn(list(treeSym8, treeCat8), list(treeCat8, treeTac8)), tolerance=1e-06) expect_equal(8L, KendallColijn(treeCat8, treeTac8), tolerance=1e-06) expect_equal(0L, KendallColijn(treeSym8, treeCat8), tolerance=1e-06) expect_equal(8L, KendallColijn(treeSym8, treeTac8), tolerance=1e-06) expect_equal(8L, KendallColijn(treeCat8, treeTac8), tolerance=1e-06) expect_equal(5.291503, KendallColijn(treeSym8, treeAb.Cdefgh), tolerance=1e-06) expect_equal(4.358899, KendallColijn(treeSym8, treeAbc.Defgh), tolerance=1e-06) expect_equal(5L, KendallColijn(treeSym8, treeAcd.Befgh), tolerance=1e-06) expect_equal(3.464102, KendallColijn(treeSym8, treeAbcd.Efgh), tolerance=1e-06) expect_equal(3L, KendallColijn(treeSym8, treeTwoSplits), tolerance=1e-06) expect_equal(2.828427, KendallColijn(treeAbc.Defgh, treeTwoSplits), tolerance=1e-06) }) test_that("Multiple comparisons are correctly ordered", { nTrees <- 6L nTip <- 16L set.seed(0) trees <- lapply(rep(nTip, nTrees), ape::rtree, br = NULL) trees[[1]] <- TreeTools::BalancedTree(nTip) trees[[nTrees - 1L]] <- TreeTools::PectinateTree(nTip) class(trees) <- "multiPhylo" PhangRF <- function (trees) { phangorn::RF.dist(reorder(trees, "cladewise")) } expect_equal(PhangRF(trees), RobinsonFoulds(trees), ignore_attr = TRUE) # Test CompareAll expect_equal(as.matrix(PhangRF(trees)), as.matrix(CompareAll(reorder(trees, "cladewise"), phangorn::RF.dist, 0L)), ignore_attr = TRUE) NNILoose <- function(x, y) NNIDist(x, y)["loose_upper"] expect_equal(CompareAll(trees, NNILoose), CompareAll(trees, NNIDist)$loose_upper, ignore_attr = TRUE) }) test_that("Normalization occurs as documented", { library("TreeTools", quietly = TRUE) tree1 <- BalancedTree(8) tree2 <- CollapseNode(PectinateTree(8), 12:13) info1 <- SplitwiseInfo(tree1) # 19.367 info2 <- SplitwiseInfo(tree2) # 11.963 ent1 <- ClusteringEntropy(tree1) # 4.245 ent2 <- ClusteringEntropy(tree2) # 2.577 # Phylogenetic information spi <- SharedPhylogeneticInfo(tree1, tree2, normalize = FALSE) # 9.64 dpi <- DifferentPhylogeneticInfo(tree1, tree2, normalize = FALSE) # 12.04 expect_equal(spi + spi + dpi, info1 + info2) expect_equal(SharedPhylogeneticInfo(tree1, tree2, normalize = TRUE), (spi + spi) / (info1 + info2)) expect_equal(PhylogeneticInfoDistance(tree1, tree2, normalize = TRUE), dpi / (info1 + info2)) # Matching split information mmsi <- MatchingSplitInfo(tree1, tree2, normalize = FALSE) msid <- MatchingSplitInfoDistance(tree1, tree2, normalize = FALSE) expect_equal(mmsi + mmsi + msid, info1 + info2) expect_equal(MatchingSplitInfo(tree1, tree2, normalize = TRUE), (mmsi + mmsi) / (info1 + info2)) expect_equal(MatchingSplitInfoDistance(tree1, tree2, normalize = TRUE), msid / (info1 + info2)) # Clustering information mci <- MutualClusteringInfo(tree1, tree2, normalize = FALSE) cid <- ClusteringInfoDistance(tree1, tree2, normalize = FALSE) expect_equal(mci + mci + cid, ent1 + ent2) expect_equal(MutualClusteringInfo(tree1, tree2, normalize = TRUE), (mci + mci) / (ent1 + ent2)) expect_equal(ClusteringInfoDistance(tree1, tree2, normalize = TRUE), cid / (ent1 + ent2)) }) test_that("Independent of root position", { library("TreeTools") bal8 <- BalancedTree(8) pec8 <- PectinateTree(8) trees <- lapply(list(bal8, RootTree(bal8, "t4"), pec8, RootTree(pec8, "t4")), UnrootTree) lapply(methodsToTest[-length(methodsToTest)], function(Method) { dists <- as.matrix(Method(trees)) expect_equal(dists[1, 1], dists[1, 2]) expect_equal(dists[1, 3], dists[1, 4]) expect_equal(dists[1, 3], dists[2, 4]) expect_equal(dists[2, 3], dists[2, 4]) expect_equal(dists[3, 3], dists[3, 4]) }) Test <- function(Method, score = 0L, ...) { expect_equal(Method(trees[[1]], trees[[1]], ...), score) expect_equal(Method(trees[[1]], trees[[2]], ...), score) expect_equal(Method(trees[[3]], trees[[3]], ...), score) } Test(MASTSize, 8L, rooted = FALSE) # Tested further for NNIDist in test-tree_distance_nni.R Test(NNIDist, c(lower = 0, best_lower = 0, tight_upper = 0, best_upper = 0, loose_upper = 0, fack_upper = 0, li_upper = 0)) Test(SPRDist, 0) })