Nothing
context("strippedDbFd")
test_that("strippedDbFd: use", {
skip_on_cran()
set.seed(42)
n_traits <- 3
n_plots <- 10
num_species <- 10;
x <- generate.Artificial.Data(n_species = num_species, n_traits = n_traits,
n_communities = n_plots,
occurence_distribution = 0.5,
average_richness = 10,
sd_richness = 1,
mechanism_random = TRUE)
data_species <- x$traits
data_species$trait1 <- 1:10
data_species$trait2 <- 1:10
data_species$trait3 <- 1:10
data_abundances <- x$abundances
species <- scaleSpeciesvalues(data_species,n_traits)
abundances <- data_abundances
row.names(abundances) <- c(1:n_plots)
abundances2 <- as.data.frame(abundances)
species2 <- species[,c(2:(n_traits + 1))] ;
#species2 <- cbind(names(abundances2),species2)
species2 <- as.matrix(species2)
row.names(species2) <- names(abundances2)
# calculate observed FD values
FD_output1 <- FD::dbFD(species2, abundances2, stand.x = F,messages=FALSE)
Ord <- ordinationAxes(x = species2, stand.x = FALSE)
res <- detMnbsp(Ord, abundances2)
FD_output2 <- strippedDbFd(Ord, abundances2, res[[1]], res[[2]])
expect_equal (
FD_output1$FRic,
FD_output2$FRic,
tolerance = 0.1
)
expect_equal (
FD_output1$FEve,
FD_output2$FEve,
tolerance = 0.1
)
set.seed(666)
n_traits <- 3
n_plots <- 10
num_species <- 10;
x <- generate.Artificial.Data(n_species = num_species, n_traits = n_traits,
n_communities = n_plots,
occurence_distribution = 0.5,
average_richness = 10,
sd_richness = 2,
mechanism_random = FALSE)
species <- scaleSpeciesvalues(x$traits,n_traits)
abundances <- x$abundances
row.names(abundances) <- c(1:n_plots)
abundances2 <- as.data.frame(abundances)
species2 <- species[,c(2:(n_traits + 1))] ;
#species2 <- cbind(names(abundances2),species2)
species2 <- as.matrix(species2)
row.names(species2) <- names(abundances2)
# calculate observed FD values
FD_output1 <- FD::dbFD(species2, abundances2, stand.x = F,messages=FALSE)
Ord <- ordinationAxes(x = species2, stand.x = FALSE)
res <- detMnbsp(Ord, abundances2)
FD_output2 <- strippedDbFd(Ord, abundances2, res[[1]], res[[2]])
expect_equal (
FD_output1$FRic,
FD_output2$FRic,
tolerance = 0.1
)
expect_equal (
FD_output1$FEve,
FD_output2$FEve,
tolerance = 0.1
)
expect_equal (
FD_output1$FDiv,
FD_output2$FDiv,
tolerance = 0.1
)
set.seed(666+42)
n_traits <- 3
n_plots <- 10
num_species <- 10;
x <- generate.Artificial.Data(n_species = num_species, n_traits = n_traits,
n_communities = n_plots,
occurence_distribution = 0.05,
average_richness = 0.3,
sd_richness = 0.1,
mechanism_random = FALSE)
species <- scaleSpeciesvalues(x$traits,n_traits)
abundances <- x$abundances
row.names(abundances) <- c(1:n_plots)
abundances2 <- as.data.frame(abundances)
species2 <- species[,c(2:(n_traits + 1))] ;
#species2 <- cbind(names(abundances2),species2)
species2 <- as.matrix(species2)
row.names(species2) <- names(abundances2)
# calculate observed FD values
FD_output1 <- FD::dbFD(species2, abundances2, stand.x = F,messages=FALSE)
Ord <- ordinationAxes(x = species2, stand.x = FALSE)
res <- detMnbsp(Ord, abundances2)
FD_output2 <- strippedDbFd(Ord, abundances2, res[[1]], res[[2]])
expect_equal (
FD_output1$FRic,
FD_output2$FRic,
tolerance = 0.1
)
expect_equal (
FD_output1$FEve,
FD_output2$FEve,
tolerance = 0.1
)
expect_equal (
FD_output1$FDiv,
FD_output2$FDiv,
tolerance = 0.1
)
})
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