View source: R/bootstrap_traits_multivariate.R
trait_multivariate_bootstrap | R Documentation |
Function for nonparametric bootstrap resampling to calculate community weighted trait correlations, other bivariate or multivariate statistics
trait_multivariate_bootstrap(
filled_traits,
nrep = 100,
sample_size = 200,
raw = FALSE,
id = "ID",
fun
)
filled_traits |
output from the trait_fill function. |
nrep |
number of bootstrap replicates |
sample_size |
bootstrap size |
raw |
logical; argument to extract the raw data of the trait
distributions.
The default is |
id |
column name of unique identifiers of each leaf |
fun |
bivariate or multivariate function to apply |
The observed and filled leaves are re-sampled in proportion to
their weights, e.g. the abundance of a species or the biomass.
Values across all individuals in a community are
resampled sample_size
times to incorporate the full
spectrum of trait variation, generating nrep
trait distributions.
The function fun
is applied to the trait distribution at the finest level
of the filled trait hierarchy.
Unexpected columns in filled_traits
are deleted with a warning.
Note that due to the flexibility of this function,
the output CAN NOT be summarized using
trait_summarise_boot_moments
.
a tibble with columns for the elements of the scale_hierarchy,
and a list column result which includes the output of fun
.
require(dplyr)
require(tidyr)
require(ggplot2)
require(purrr)
data(community)
data(trait)
filled_traits <- trait_fill(
comm = community |>
filter(
PlotID %in% c("A", "B"),
Site == 1
),
traits = trait,
scale_hierarchy = c("Site", "PlotID"),
taxon_col = "Taxon", value_col = "Value",
trait_col = "Trait", abundance_col = "Cover",
complete_only = TRUE, leaf_id = "ID"
)
# Note that more replicates and a greater sample size are advisable
# Here we set them low to make the example run quickly
boot_traits <- trait_multivariate_bootstrap(filled_traits,
fun = cor,
nrep = 10,
sample_size = 100
)
boot_traits_long <- boot_traits |>
mutate(correlations = map(result, ~ cor_to_df(.x))) |>
select(-result) |>
unnest(correlations)
boot_traits_long |>
ggplot(aes(x = paste(row, "v", col), y = value)) +
geom_violin() +
facet_grid(Site ~ PlotID) +
coord_flip() +
labs(y = "Correlation", x = "")
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