# Test Functional Distinctiveness
# Initial data -----------------------------------------------------------------
# Empty Matrix
empty_mat = matrix(rep(0, 4), ncol = 2)
rownames(empty_mat) = paste0("s", 1:2)
colnames(empty_mat) = letters[1:2]
# Valid Presence-Absence Matrix
valid_mat = matrix(c(1, 0, 0, 0,
rep(1, 3), 0,
0, rep(1, 3),
0, 1, 0, 1),
ncol = 4)
dimnames(valid_mat) = list("site" = paste0("s", 1:4), "species" = letters[1:4])
# Community df
log_mat = (valid_mat == 1)
suppressWarnings({
com_df = lapply(rownames(log_mat), function(x) {
species = colnames(valid_mat)[log_mat[x, ]]
data.frame(site = rep(x, length(species)), species = species,
stringsAsFactors = FALSE)
})
com_df = do.call(rbind.data.frame, com_df)
})
# Traits df
trait_df = data.frame(tr1 = c("A", "A", "B", "B"), tr2 = c(rep(0, 3), 1),
tr3 = seq(4, 16, 4), stringsAsFactors = TRUE)
rownames(trait_df) = letters[1:4]
# Distance Matrix
dist_mat = compute_dist_matrix(trait_df)
## Correct Distinctiveness -----------------------------------------------------
# Final distinctiveness table for all communities
correct_dist = data.frame(
site = c("s1", "s1", "s2", "s2", "s2", "s3", "s3", "s4", "s4"),
species = c("a", "b", "b", "c", "d","b", "c", "c", "d"),
Di = c(1/9, 1/9, 6/9, 4/9, 6/9, 4/9, 4/9, 4/9, 4/9),
stringsAsFactors = FALSE
)
correct_dist_mat = table(correct_dist$site, correct_dist$species)
correct_dist_mat[which(correct_dist_mat == 0)] = NA_real_
correct_dist_mat[which(correct_dist_mat == 1)] = correct_dist$Di
correct_dist_mat[2, 3] = 4/9
correct_dist_mat[2, 4] = 6/9
names(dimnames(correct_dist_mat)) = c("site", "species")
# Distinctiveness with abundances
correct_dist_ab = correct_dist
## Undefined Di ----------------------------------------------------------------
# Undefined Distinctiveness site-species matrix
small_mat = matrix(c(1, 0, 0, 1), nrow = 2)
colnames(small_mat) = letters[1:2]
rownames(small_mat) = c("s1", "s2")
small_df = matrix_to_tidy(small_mat)
# Small tidy df with undefined Di
undef_dist = data.frame(site = c("s1", "s2"), species = c("a", "b"),
Di = rep(NaN, 2), stringsAsFactors = FALSE)
# Small matrix with undefined Di
undef_dist_mat = table(undef_dist$site, undef_dist$species)
undef_dist_mat[which(undef_dist_mat == 0)] = NA_real_
undef_dist_mat[which(undef_dist_mat == 1)] = undef_dist$Di
suppressWarnings({
suppressMessages({
undef_test = distinctiveness(small_mat, dist_mat)
})
})
## Abundances ---------f--------------------------------------------------------
# Define Abundance Matrix
com_df_ex = cbind(com_df, abund = c(0.3, 0.7, 0.2, 0.6,
0.2, 0.5, 0.5, 0.2,
0.8))
abund_mat = valid_mat
abund_mat[abund_mat == 1] = com_df_ex[order(com_df_ex$species), "abund"]
# Define Abundance tidy table
abund_com = matrix_to_stack(abund_mat, value_col = "abund", row_to_col = "site",
col_to_col = "species")
abund_com = subset(abund_com, abund > 0 & site == "s3")
abund_com$Di = c(4/9, 4/9)
## Sparse Matrices -------------------------------------------------------------
library(Matrix)
sparse_mat = as(valid_mat, "dgCMatrix")
dist_sparse_mat = as(as(correct_dist_mat, "matrix"), "dgeMatrix")
## Relative distinctiveness ----------------------------------------------------
# Define relative distinctiveness matrix
correct_rel_di = correct_dist_mat
correct_rel_di[1,] = correct_rel_di[1,] / (1/9)
correct_rel_di[2,] = correct_rel_di[2,] / (8/9)
correct_rel_di[3,] = correct_rel_di[3,] / (4/9)
correct_rel_di[4,] = correct_rel_di[4,] / (4/9)
correct_rel_di = as.matrix(as.data.frame.matrix(correct_rel_di))
# Relative Di df
correct_rel_di_df = structure(list(site = c("s1", "s1", "s2", "s2", "s2", "s3",
"s3", "s4", "s4"),
species = c("a", "b", "b", "c", "d","b", "c",
"c", "d"),
Di = c(1, 1, 3/4, 1/2, 3/4, 1, 1, 1, 1)),
.Names = c("site", "species", "Di"),
row.names = c(NA, -9L), class = c("data.frame"))
# Relative Di sparse
dist_sparse_mat_rel = dist_sparse_mat
dist_sparse_mat_rel[1,] = dist_sparse_mat_rel[1,] / (1/9)
dist_sparse_mat_rel[2,] = dist_sparse_mat_rel[2,] / (8/9)
dist_sparse_mat_rel[3,] = dist_sparse_mat_rel[3,] / (4/9)
dist_sparse_mat_rel[4,] = dist_sparse_mat_rel[4,] / (4/9)
# Tests for Distinctiveness ----------------------------------------------------
test_that("Invalid input types do not work", {
expect_error(distinctiveness_com("a", "species", NULL, "d"))
expect_error(distinctiveness_com(3, "species", NULL, "d"))
})
test_that("Correct Di computation with different comm. without abundance",{
# Good messages and warnings
expect_message(distinctiveness_stack(com_df, "species", "site",
abund = NULL, dist_mat))
expect_message(distinctiveness(valid_mat[-1, -1], dist_mat))
expect_message(distinctiveness(valid_mat[2:3, 1:4], dist_mat[-1, -1]))
# Conservation of dimensions names of indices matrix
expect_identical(dimnames(distinctiveness(valid_mat, dist_mat)),
dimnames(valid_mat))
# Good Distinctiveness computations without abundances
c_dist = distinctiveness_stack(com_df, "species", "site", abund = NULL,
dist_mat)
expect_equal(correct_dist_mat,
as.table(distinctiveness(valid_mat, dist_mat)))
expect_equal(c_dist, correct_dist)
# Undefined distinctiveness for species alone in communities
expect_equal(distinctiveness_com(com_df[1,], "species",
dist_matrix = dist_mat)[1,3], NaN)
# Distinctiveness with abundances
expect_equal(distinctiveness_com(abund_com[, -4], "species", "abund",
dist_mat), abund_com)
})
test_that("Di is undefined for a community with a single species", {
## Test for matrix version of distinctiveness
expect_equal(as(undef_dist_mat, "matrix"), undef_test, ignore_attr = TRUE)
# Check warning for NaN created in the matrix
expect_warning(distinctiveness(small_mat, dist_mat),
regexp = paste0("Some communities had a single species in ",
"them\nComputed value assigned to 'NaN'"))
## Test for data.frame version of distinctiveness
expect_warning(distinctiveness_stack(small_df, "col", "row",
"value", dist_mat),
regexp = paste0("Some communities had a single species in ",
"them\nComputed value assigned to 'NaN'"))
expect_equal(
suppressWarnings(
distinctiveness_stack(undef_dist[, 1:2], "species", "site",
dist_matrix = dist_mat)),
undef_dist
)
expect_equal(
suppressWarnings(
distinctiveness_stack(small_df, "col", "row", "value", dist_mat)),
data.frame(col = rep(c("a", "b"), 2),
row = rep(c("s1", "s2"), each = 2),
value = c(1, 0, 0, 1),
Di = c(NaN, NA, NA, NaN),
stringsAsFactors = FALSE))
})
test_that("Distinctiveness works with sparse matrices", {
expect_silent(distinctiveness(sparse_mat, dist_mat))
expect_equal(distinctiveness(sparse_mat, dist_mat), dist_sparse_mat)
})
# Test for relative distinctiveness --------------------------------------------
test_that("Relative distinctiveness argument should be logical", {
rel_error = "'relative' argument should be either TRUE or FALSE"
# Character argument
expect_error(distinctiveness(valid_mat, dist_mat, relative = "a"),
rel_error)
expect_error(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = "a"),
rel_error)
expect_error(distinctiveness_com(com_df, "species", "site",
dist_matrix = dist_mat, relative = "a"),
rel_error)
# Numeric argument
expect_error(distinctiveness(valid_mat, dist_mat, relative = 12),
rel_error)
expect_error(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = 12),
rel_error)
expect_error(distinctiveness_com(com_df, "species", "site",
dist_matrix = dist_mat, relative = 12),
rel_error)
expect_error(distinctiveness(valid_mat, dist_mat, relative = 1),
rel_error)
expect_error(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = 1),
rel_error)
expect_error(distinctiveness_com(com_df, "species", "site",
dist_matrix = dist_mat, relative = 1),
rel_error)
expect_error(distinctiveness(valid_mat, dist_mat, relative = 0),
rel_error)
expect_error(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = 0),
rel_error)
expect_error(distinctiveness_com(com_df, "species", "site",
dist_matrix = dist_mat, relative = 0),
rel_error)
# NA argument
expect_error(distinctiveness(valid_mat, dist_mat, relative = NA),
rel_error)
expect_error(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = NA),
rel_error)
expect_error(distinctiveness_com(com_df, "species", "site",
dist_matrix = dist_mat, relative = NA),
rel_error)
# NaN argument
expect_error(distinctiveness(valid_mat, dist_mat, relative = NaN),
rel_error)
expect_error(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = NaN),
rel_error)
expect_error(distinctiveness_com(com_df, "species", "site",
dist_matrix = dist_mat, relative = NaN),
rel_error)
# Logical argument
expect_silent(distinctiveness(valid_mat, dist_mat, relative = TRUE))
expect_silent(suppressMessages(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = TRUE)))
expect_silent(distinctiveness_com(com_df[1:2,], "species",
dist_matrix = dist_mat, relative = TRUE))
expect_silent(distinctiveness(valid_mat, dist_mat, relative = FALSE))
expect_silent(suppressMessages(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = FALSE)))
expect_silent(distinctiveness_com(com_df[1:2,], "species",
dist_matrix = dist_mat, relative = FALSE))
})
test_that("Relative distinctiveness can be computed", {
## Only presence-absences
# Matrix
expect_equal(distinctiveness(valid_mat, dist_mat, relative = TRUE),
correct_rel_di,
ignore_attr = TRUE)
# Df
expect_equal(distinctiveness_tidy(com_df, "species", "site",
dist_matrix = dist_mat, relative = TRUE),
correct_rel_di_df)
# Sparse mat
expect_equal(distinctiveness(sparse_mat, dist_mat, relative = TRUE),
dist_sparse_mat_rel)
# Single community
expect_equal(distinctiveness_com(com_df[1:2, ], "species",
dist_matrix = dist_mat, relative = TRUE),
correct_rel_di_df[1:2, ])
## With abundances
# Matrix
# Df
# Sparse mat
})
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