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isdbayes: Bayesian hierarchical modeling of size spectra

Jeff Wesner

Overview

This package allows the estimation of power law exponents using the truncated (upper and lower) Pareto distribution (Wesner et al. 2023). Specifically, it allows users to fit Bayesian (non)-linear hierarchical models with a truncated Pareto likelihood using brms (Bürkner 2017). The motivation for the package was to estimate power law exponents of ecological size spectra using individual-level body size data in a generalized mixed model framework. The likelihood for the truncated Pareto used here was described in (Edwards et al. 2020). This package translates that likelihood into brms.

Installation

This package requires installation of brms and rstan, which itself requires installation of a C++ toolchain.

  1. Go to https://mc-stan.org/users/interfaces/rstan.html and follow the instructions to install rstan and configure the C++ toolchain.

  2. Install the latest version of brms with install.packages(“brms”).

  3. Install isdbayes from github using devtools:

# requires an installation of devtools

devtools::install_github("jswesner/isdbayes")

Examples

# load these packages

library(dplyr)
library(tidyr)
library(here)
library(ggplot2)
library(tidybayes)
library(brms)
library(isdbayes)

Fit individual samples

First, simulate some power law data using rparetocounts(). The code below simulates 300 body sizes from a power law with exponent lambda = -1.2, xmin = 1, and xmax = 1000.

# simulate data

dat = tibble(x = rparetocounts(n = 300,  lambda = -1.2,  xmin = 1, xmax = 1000)) |> 
  mutate(xmin = min(x),
         xmax = max(x),
         counts = 1)

The code above simulates data from a doubly-truncated Pareto and then estimates xmin and xmax. It also adds a column for counts. If the data all represent unique individual masses, then this column takes a value of 1 for every body size. If the data have repeated sizes, then this column can take an integer or double of the counts or densities of those sizes. For example, data that are x = {1.9, 1.9, 1.8, 2.8, 2.8} could either be analyzed with each body size assumed to be unique where counts = {1, 1, 1, 1, 1} or it could be analyzed as x = {1.9, 1.8, 2.8} and counts = {2, 1, 2}. The latter is a common format when there is a density estimate associated with counts or a sampling effort.

Next estimate the power law exponent using brms. The model below (fit1) is an intercept only model, where x are the body sizes and counts, xmin, and xmax are included in vreal(). The use of vreal has nothing to do with the model per se. It is simply required wording from brms when including custom families. Similarly, stanvars is required wording that contains the custom likelihood parameters. As long as isdbayes is loaded, then stanvars = stanvars will work. It will stay the same regardless of changes to the model structure (like new predictors or varyaing intercepts).

fit1 = brm(x | vreal(counts, xmin, xmax) ~ 1, 
          data = dat,
          stanvars = stanvars,    # required for truncated Pareto
          family = paretocounts(),# required for truncated Pareto
          chains = 1, iter = 1000)

This example fits an intercept-only model to estimate the power-law exponent. For more complex examples with fixed and hierarchical predictors, see below.

Simulate multiple size distributions

x1 = rparetocounts(lambda = -1.8) # `lambda` is required wording from brms. in this case it means the lambda exponent of the ISD
x2 = rparetocounts(lambda = -1.5)
x3 = rparetocounts(lambda = -1.2)

isd_data = tibble(x1 = x1,
                  x2 = x2,
                  x3 = x3) |> 
  pivot_longer(cols = everything(), names_to = "group", values_to = "x") |> 
  group_by(group) |> 
  mutate(xmin = min(x),
         xmax = max(x)) |> 
  group_by(group, x) |> 
  add_count(name = "counts")

Fit multiple size distributions with a fixed factor

fit2 = brm(x | vreal(counts, xmin, xmax) ~ group, 
           data = isd_data,
           stanvars = stanvars,
           family = paretocounts(),
           chains = 1, iter = 1000)

Plot group posteriors

posts_group = fit2$data |> 
  distinct(group, xmin, xmax) |> 
  mutate(counts = 1) |> 
  add_epred_draws(fit2, re_formula = NA) 

posts_group |> 
  ggplot(aes(x = group, y = .epred)) + 
  stat_halfeye(scale = 0.2) + 
  geom_hline(yintercept = c(-1.8, -1.5, -1.2)) # known lambdas

Fit multiple size distributions with a varying intercept

fit3 = brm(x | vreal(counts, xmin, xmax) ~ (1|group), 
           data = isd_data,
           stanvars = stanvars,
           family = paretocounts(),
           chains = 1, iter = 1000)

Plot varying intercepts

posts_varint = fit3$data |> 
  distinct(group, xmin, xmax) |> 
  mutate(counts = 1) |> 
  add_epred_draws(fit3, re_formula = NULL) 

posts_varint |> 
  ggplot(aes(x = group, y = .epred)) + 
  stat_halfeye(scale = 0.2) + 
  geom_hline(yintercept = c(-1.8, -1.5, -1.2)) # known lambdas

Posterior predictive checks

After the model is fit, you can use built-in functions in brms to perform model checking.

pp_check(fit2, type = "dens_overlay_grouped", group = "group") +
  scale_x_log10()
#> Using 10 posterior draws for ppc type 'dens_overlay_grouped' by default.
#> Warning in self$trans$transform(x): NaNs produced

#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Removed 6 rows containing missing values (`geom_segment()`).

References

Bürkner, P. C. 2017. “Brms: An r Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software 80: 1–28.

Edwards, A. M., J. P. W. Robinson, J. L. Blanchard, J. K. Baum, and M. J. Plank. 2020. “Accounting for the Bin Structure of Data Removes Bias When Fitting Size Spectra.” Marine Ecology Progress Series 636 (February): 19–33. https://doi.org/10.3354/meps13230.

Wesner, J. S, J. P. F. Pomeranz, J. R. Junker, and V. Gjoni. 2023. “Bayesian Hierarchical Modeling of Size Spectra.” bioRxiv, 2023–02.