traitstrap (version 0.1.0)

trait_multivariate_bootstrap: Bootstrap traits

Description

Function for nonparametric bootstrap resampling to calculate community weighted trait correlations, other bivariate or multivariate statistics

Usage

trait_multivariate_bootstrap(
  filled_traits,
  nrep = 100,
  sample_size = 200,
  raw = FALSE,
  id = "ID",
  fun
)

Value

a tibble with columns for the elements of the scale_hierarchy, and a list column result which includes the output of fun.

Arguments

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 raw = FALSE. If raw = TRUE, nrep is restricted to 1 to avoid memory issues.

id

column name of unique identifiers of each leaf

fun

bivariate or multivariate function to apply

Details

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.

Note that due to the flexibility of this function, the output CAN NOT be summarized using trait_summarise_boot_moments.

Examples

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 = "")