trait_multivariate_bootstrap: Bootstrap traits

In richardjtelford/traitstrap: Bootstrap Trait Values to Calculate Moments

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
)

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.

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.

Value

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

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 = "")

richardjtelford/traitstrap documentation built on July 3, 2024, 1:46 a.m.