tests/testthat/test-metacommunity.R
In boydorr/rdiversity: Measurement and Partitioning of Similarity-Sensitive Biodiversity
context("Testing metacommunity()")
test_that("metacommunity() works for a single community", {
pop <- c(1:5)
names(pop) <- paste0("sp", 1:5)
expect_message(meta <- metacommunity(pop))
ans <- matrix(c(1/15, 2/15, 1/5, 4/15, 1/3))
rownames(ans) <- names(pop)
expect_equal(meta@type_abundance, ans)
expect_equivalent(meta@similarity, diag(1, 5))
expect_equal(meta@similarity_components, list())
expect_equal(meta@similarity_parameters,
list(transform = NA, k = NA, normalise = NA, max_d = NA))
expect_equivalent(meta@ordinariness, ans)
expect_equal(meta@subcommunity_weights, 1)
expect_equivalent(meta@type_weights, ans)
expect_true(meta@dat_id == "naive")
expect_true(length(meta@raw_abundance) == 0)
expect_true(length(meta@raw_structure) == 0)
expect_true(length(meta@parameters) == 0)
})
test_that("metacommunity() works in the naive-type case", {
pop <- matrix(1:10, ncol = 2)
row.names(pop) <- paste0("sp", 1:5)
colnames(pop) <- c("A", "B")
expect_message(meta <- metacommunity(pop))
ans <- matrix(c(1/55, 2/55, 3/55, 4/55, 1/11, 6/55, 7/55, 8/55, 9/55, 2/11),
ncol = 2)
rownames(ans) <- row.names(pop)
colnames(ans) <- colnames(pop)
ans2 <- matrix(c(1/15, 2/15, 1/5, 4/15, 1/3, 3/20, 7/40, 1/5, 9/40, 1/4),
ncol = 2)
rownames(ans2) <- row.names(pop)
colnames(ans2) <- colnames(pop)
expect_equal(meta@type_abundance, ans)
expect_equivalent(meta@similarity, diag(1, 5))
expect_equal(meta@similarity_components, list())
expect_equal(meta@similarity_parameters,
list(transform = NA, k = NA, normalise = NA, max_d = NA))
expect_equivalent(meta@ordinariness, ans)
expect_equal(meta@subcommunity_weights, colSums(pop)/sum(pop))
expect_equivalent(meta@type_weights, ans2)
expect_true(length(meta@raw_abundance) == 0)
expect_true(length(meta@raw_structure) == 0)
expect_true(length(meta@parameters) == 0)
})
test_that("metacommunity() works for taxonomic diversity", {
# Generate lookup table
Species <- c("tenuifolium", "asterolepis", "simplex var.grandiflora",
"simplex var.ochnacea")
Genus <- c("Protium", "Quararibea", "Swartzia", "Swartzia")
Family <- c("Burseraceae", "Bombacaceae", "Fabaceae", "Fabaceae")
Subclass <- c("Sapindales", "Malvales", "Fabales", "Fabales")
lookup <- cbind.data.frame(Species, Genus, Family, Subclass)
# Assign values for each level (Shimatani's taxonomic distance)
tax_distance <- c(Species = 0, Genus = 1, Family = 2, Subclass = 3, Other = 4)
partition <- matrix(c(6, 2, 1, 7, 5, 8, 3, 4), ncol = 2)
row.names(partition) <- Species
colnames(partition) <- LETTERS[1:2]
# Precompute pairwise distances
pc_dist <- tax2dist(lookup, tax_distance, TRUE)
pc_sim <- dist2sim(pc_dist, "linear")
expect_message(pc_meta <- metacommunity(partition, pc_sim))
# Don't precompute pairwise distances
dc_dist <- tax2dist(lookup, tax_distance, FALSE)
dc_sim <- dist2sim(dc_dist, "linear")
expect_message(dc_meta <- metacommunity(partition, dc_sim))
# Test slots in dc_meta
expect_true(length(dc_meta@similarity) == 0)
expect_true(is.matrix(dc_meta@similarity))
expect_equal(dc_meta@similarity_components$precompute, FALSE)
expect_equal(dc_meta@similarity_components$ordinariness, "taxvec")
expect_equal(dc_meta@similarity_components$tax_distance, tax_distance)
expect_equivalent(dc_meta@similarity_components$tax_similarity,
c(1, 0.75, 0.5, 0.25, 0))
expect_equivalent(dc_meta@similarity_components$tax_id, c(198, 17, 104, 168))
expect_equivalent(lapply(dc_meta@similarity_components$tax_mask, as.numeric),
list(Species = 255, Genus = 63, Family = 15, Subclass = 3))
expect_equivalent(dc_meta@similarity_components$tax_bits, c(2, 2, 2, 2))
expect_equal(dc_meta@similarity_parameters, list(transform = "linear",
k = 1,
normalise = TRUE,
max_d = 4))
tmp <- matrix(c(1/6, 1/18, 25/144, 31/144, 5/36, 2/9, 1/6, 25/144),
ncol = 2)
rownames(tmp) <- Species
colnames(tmp) <- LETTERS[1:2]
expect_equal(dc_meta@ordinariness, tmp)
expect_equivalent(dc_meta@subcommunity_weights, c(4/9, 5/9))
tmp <- matrix(c(3/8, 1/8, 1/16, 7/16, 1/4, 2/5, 3/20, 1/5),
ncol = 2)
rownames(tmp) <- Species
colnames(tmp) <- LETTERS[1:2]
expect_equal(dc_meta@type_weights, tmp)
expect_equal(dc_meta@dat_id, "taxonomic")
expect_true(length(dc_meta@raw_abundance) == 0)
expect_true(is.matrix(dc_meta@raw_abundance))
expect_true(length(dc_meta@raw_structure) == 0)
expect_true(is.matrix(dc_meta@raw_structure))
expect_true(length(dc_meta@parameters) == 0)
expect_true(is.data.frame(dc_meta@parameters))
# Test slots in pc_meta
expect_equal(pc_meta@type_abundance, dc_meta@type_abundance)
tmp <- matrix(c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0.75, 0, 0, 0.75, 1),
ncol = 4)
rownames(tmp) <- Species
colnames(tmp) <- Species
expect_equal(pc_meta@similarity, tmp)
expect_equal(pc_meta@similarity_components,
list(precompute = TRUE, tax_distance = tax_distance))
expect_equal(pc_meta@similarity_parameters, dc_meta@similarity_parameters)
expect_equal(pc_meta@ordinariness, dc_meta@ordinariness)
expect_equal(pc_meta@subcommunity_weights, dc_meta@subcommunity_weights)
expect_equal(pc_meta@type_weights, dc_meta@type_weights)
expect_equal(pc_meta@dat_id, dc_meta@dat_id)
expect_equal(pc_meta@raw_abundance, dc_meta@raw_abundance)
expect_equal(pc_meta@raw_structure, dc_meta@raw_structure)
expect_equal(pc_meta@parameters, dc_meta@parameters)
})
test_that("metacommunity() works for phydist diversity", {
set.seed(123)
tree <- ape::rtree(5)
tree$tip.label <- paste0("sp", 1:5)
# Precompute pairwise distances
partition <- matrix(rep(1, 10), nrow = 5)
row.names(partition) <- paste0("sp", 1:5)
partition <- partition / sum(partition)
distance <- phy2dist(tree, precompute_dist = TRUE)
similarity <- dist2sim(distance, "linear")
meta <- metacommunity(partition, similarity)
# Don't precompute pairwise distances
d <- phy2dist(tree, FALSE)
s <- dist2sim(d, "linear")
m <- metacommunity(partition, s)
tmp <- matrix(rep(0.1, 10), ncol = 2)
rownames(tmp) <- row.names(partition)
expect_equal(meta@type_abundance, tmp)
tmp <- matrix(c(1.0000000, 0.4474568, 0.5890454, 0.2424060, 0.3744924,
0.4474568, 1.0000000, 0.6034479, 0.0000000, 0.1320864,
0.5890454, 0.6034479, 1.0000000, 0.1415886, 0.2736750,
0.2424060, 0.0000000, 0.1415886, 1.0000000, 0.8100268,
0.3744924, 0.1320864, 0.2736750, 0.8100268, 1.0000000),
ncol = 5, byrow = TRUE)
rownames(tmp) <- row.names(partition)
colnames(tmp) <- row.names(partition)
expect_equal(meta@similarity, tmp, tolerance = 1e-05)
expect_equal(meta@similarity_components, list())
expect_equal(meta@similarity_parameters$transform, "linear")
expect_equal(meta@similarity_parameters$k, 1)
expect_equal(meta@similarity_parameters$normalise, TRUE)
expect_equal(meta@similarity_parameters$max_d, 3.556071, tolerance = 1e-05)
expect_equal(meta@ordinariness, m@ordinariness)
expect_equal(meta@subcommunity_weights, c(0.5, 0.5))
tmp <- matrix(rep(0.2, 10), ncol = 2)
rownames(tmp) <- row.names(partition)
expect_equal(meta@type_weights, tmp)
expect_true(meta@dat_id == "phylogenetic")
expect_true(length(meta@raw_abundance) == 0)
expect_true(length(meta@raw_structure) == 0)
expect_true(length(meta@parameters) == 0)
})
test_that("metacommunity() works for phybranch diversity", {
set.seed(123)
tree <- ape::rtree(3)
tree$tip.label <- paste0("sp", 1:3)
partition <- matrix(rep(1, 9), nrow = 3)
row.names(partition) <- paste0("sp", 1:3)
partition <- partition / sum(partition)
similarity <- phy2branch(tree, partition)
meta <- metacommunity(partition, similarity)
expect_true(is.list(meta@similarity_components))
expect_true(length(meta@similarity_components) == 0)
expect_equal(meta@similarity_parameters, list(transform = NA,
k = NA,
normalise = NA,
max_d = NA))
expect_true(meta@dat_id == "phybranch")
expect_true(length(meta@raw_abundance) != 0)
expect_true(length(meta@raw_structure) != 0)
expect_true(length(meta@parameters) != 0)
})
boydorr/rdiversity documentation built on May 6, 2022, 10:56 a.m.
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context("Testing metacommunity()")
test_that("metacommunity() works for a single community", {
pop <- c(1:5)
names(pop) <- paste0("sp", 1:5)
expect_message(meta <- metacommunity(pop))
ans <- matrix(c(1/15, 2/15, 1/5, 4/15, 1/3))
rownames(ans) <- names(pop)
expect_equal(meta@type_abundance, ans)
expect_equivalent(meta@similarity, diag(1, 5))
expect_equal(meta@similarity_components, list())
expect_equal(meta@similarity_parameters,
list(transform = NA, k = NA, normalise = NA, max_d = NA))
expect_equivalent(meta@ordinariness, ans)
expect_equal(meta@subcommunity_weights, 1)
expect_equivalent(meta@type_weights, ans)
expect_true(meta@dat_id == "naive")
expect_true(length(meta@raw_abundance) == 0)
expect_true(length(meta@raw_structure) == 0)
expect_true(length(meta@parameters) == 0)
})
test_that("metacommunity() works in the naive-type case", {
pop <- matrix(1:10, ncol = 2)
row.names(pop) <- paste0("sp", 1:5)
colnames(pop) <- c("A", "B")
expect_message(meta <- metacommunity(pop))
ans <- matrix(c(1/55, 2/55, 3/55, 4/55, 1/11, 6/55, 7/55, 8/55, 9/55, 2/11),
ncol = 2)
rownames(ans) <- row.names(pop)
colnames(ans) <- colnames(pop)
ans2 <- matrix(c(1/15, 2/15, 1/5, 4/15, 1/3, 3/20, 7/40, 1/5, 9/40, 1/4),
ncol = 2)
rownames(ans2) <- row.names(pop)
colnames(ans2) <- colnames(pop)
expect_equal(meta@type_abundance, ans)
expect_equivalent(meta@similarity, diag(1, 5))
expect_equal(meta@similarity_components, list())
expect_equal(meta@similarity_parameters,
list(transform = NA, k = NA, normalise = NA, max_d = NA))
expect_equivalent(meta@ordinariness, ans)
expect_equal(meta@subcommunity_weights, colSums(pop)/sum(pop))
expect_equivalent(meta@type_weights, ans2)
expect_true(length(meta@raw_abundance) == 0)
expect_true(length(meta@raw_structure) == 0)
expect_true(length(meta@parameters) == 0)
})
test_that("metacommunity() works for taxonomic diversity", {
# Generate lookup table
Species <- c("tenuifolium", "asterolepis", "simplex var.grandiflora",
"simplex var.ochnacea")
Genus <- c("Protium", "Quararibea", "Swartzia", "Swartzia")
Family <- c("Burseraceae", "Bombacaceae", "Fabaceae", "Fabaceae")
Subclass <- c("Sapindales", "Malvales", "Fabales", "Fabales")
lookup <- cbind.data.frame(Species, Genus, Family, Subclass)
# Assign values for each level (Shimatani's taxonomic distance)
tax_distance <- c(Species = 0, Genus = 1, Family = 2, Subclass = 3, Other = 4)
partition <- matrix(c(6, 2, 1, 7, 5, 8, 3, 4), ncol = 2)
row.names(partition) <- Species
colnames(partition) <- LETTERS[1:2]
# Precompute pairwise distances
pc_dist <- tax2dist(lookup, tax_distance, TRUE)
pc_sim <- dist2sim(pc_dist, "linear")
expect_message(pc_meta <- metacommunity(partition, pc_sim))
# Don't precompute pairwise distances
dc_dist <- tax2dist(lookup, tax_distance, FALSE)
dc_sim <- dist2sim(dc_dist, "linear")
expect_message(dc_meta <- metacommunity(partition, dc_sim))
# Test slots in dc_meta
expect_true(length(dc_meta@similarity) == 0)
expect_true(is.matrix(dc_meta@similarity))
expect_equal(dc_meta@similarity_components$precompute, FALSE)
expect_equal(dc_meta@similarity_components$ordinariness, "taxvec")
expect_equal(dc_meta@similarity_components$tax_distance, tax_distance)
expect_equivalent(dc_meta@similarity_components$tax_similarity,
c(1, 0.75, 0.5, 0.25, 0))
expect_equivalent(dc_meta@similarity_components$tax_id, c(198, 17, 104, 168))
expect_equivalent(lapply(dc_meta@similarity_components$tax_mask, as.numeric),
list(Species = 255, Genus = 63, Family = 15, Subclass = 3))
expect_equivalent(dc_meta@similarity_components$tax_bits, c(2, 2, 2, 2))
expect_equal(dc_meta@similarity_parameters, list(transform = "linear",
k = 1,
normalise = TRUE,
max_d = 4))
tmp <- matrix(c(1/6, 1/18, 25/144, 31/144, 5/36, 2/9, 1/6, 25/144),
ncol = 2)
rownames(tmp) <- Species
colnames(tmp) <- LETTERS[1:2]
expect_equal(dc_meta@ordinariness, tmp)
expect_equivalent(dc_meta@subcommunity_weights, c(4/9, 5/9))
tmp <- matrix(c(3/8, 1/8, 1/16, 7/16, 1/4, 2/5, 3/20, 1/5),
ncol = 2)
rownames(tmp) <- Species
colnames(tmp) <- LETTERS[1:2]
expect_equal(dc_meta@type_weights, tmp)
expect_equal(dc_meta@dat_id, "taxonomic")
expect_true(length(dc_meta@raw_abundance) == 0)
expect_true(is.matrix(dc_meta@raw_abundance))
expect_true(length(dc_meta@raw_structure) == 0)
expect_true(is.matrix(dc_meta@raw_structure))
expect_true(length(dc_meta@parameters) == 0)
expect_true(is.data.frame(dc_meta@parameters))
# Test slots in pc_meta
expect_equal(pc_meta@type_abundance, dc_meta@type_abundance)
tmp <- matrix(c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0.75, 0, 0, 0.75, 1),
ncol = 4)
rownames(tmp) <- Species
colnames(tmp) <- Species
expect_equal(pc_meta@similarity, tmp)
expect_equal(pc_meta@similarity_components,
list(precompute = TRUE, tax_distance = tax_distance))
expect_equal(pc_meta@similarity_parameters, dc_meta@similarity_parameters)
expect_equal(pc_meta@ordinariness, dc_meta@ordinariness)
expect_equal(pc_meta@subcommunity_weights, dc_meta@subcommunity_weights)
expect_equal(pc_meta@type_weights, dc_meta@type_weights)
expect_equal(pc_meta@dat_id, dc_meta@dat_id)
expect_equal(pc_meta@raw_abundance, dc_meta@raw_abundance)
expect_equal(pc_meta@raw_structure, dc_meta@raw_structure)
expect_equal(pc_meta@parameters, dc_meta@parameters)
})
test_that("metacommunity() works for phydist diversity", {
set.seed(123)
tree <- ape::rtree(5)
tree$tip.label <- paste0("sp", 1:5)
# Precompute pairwise distances
partition <- matrix(rep(1, 10), nrow = 5)
row.names(partition) <- paste0("sp", 1:5)
partition <- partition / sum(partition)
distance <- phy2dist(tree, precompute_dist = TRUE)
similarity <- dist2sim(distance, "linear")
meta <- metacommunity(partition, similarity)
# Don't precompute pairwise distances
d <- phy2dist(tree, FALSE)
s <- dist2sim(d, "linear")
m <- metacommunity(partition, s)
tmp <- matrix(rep(0.1, 10), ncol = 2)
rownames(tmp) <- row.names(partition)
expect_equal(meta@type_abundance, tmp)
tmp <- matrix(c(1.0000000, 0.4474568, 0.5890454, 0.2424060, 0.3744924,
0.4474568, 1.0000000, 0.6034479, 0.0000000, 0.1320864,
0.5890454, 0.6034479, 1.0000000, 0.1415886, 0.2736750,
0.2424060, 0.0000000, 0.1415886, 1.0000000, 0.8100268,
0.3744924, 0.1320864, 0.2736750, 0.8100268, 1.0000000),
ncol = 5, byrow = TRUE)
rownames(tmp) <- row.names(partition)
colnames(tmp) <- row.names(partition)
expect_equal(meta@similarity, tmp, tolerance = 1e-05)
expect_equal(meta@similarity_components, list())
expect_equal(meta@similarity_parameters$transform, "linear")
expect_equal(meta@similarity_parameters$k, 1)
expect_equal(meta@similarity_parameters$normalise, TRUE)
expect_equal(meta@similarity_parameters$max_d, 3.556071, tolerance = 1e-05)
expect_equal(meta@ordinariness, m@ordinariness)
expect_equal(meta@subcommunity_weights, c(0.5, 0.5))
tmp <- matrix(rep(0.2, 10), ncol = 2)
rownames(tmp) <- row.names(partition)
expect_equal(meta@type_weights, tmp)
expect_true(meta@dat_id == "phylogenetic")
expect_true(length(meta@raw_abundance) == 0)
expect_true(length(meta@raw_structure) == 0)
expect_true(length(meta@parameters) == 0)
})
test_that("metacommunity() works for phybranch diversity", {
set.seed(123)
tree <- ape::rtree(3)
tree$tip.label <- paste0("sp", 1:3)
partition <- matrix(rep(1, 9), nrow = 3)
row.names(partition) <- paste0("sp", 1:3)
partition <- partition / sum(partition)
similarity <- phy2branch(tree, partition)
meta <- metacommunity(partition, similarity)
expect_true(is.list(meta@similarity_components))
expect_true(length(meta@similarity_components) == 0)
expect_equal(meta@similarity_parameters, list(transform = NA,
k = NA,
normalise = NA,
max_d = NA))
expect_true(meta@dat_id == "phybranch")
expect_true(length(meta@raw_abundance) != 0)
expect_true(length(meta@raw_structure) != 0)
expect_true(length(meta@parameters) != 0)
})
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