Gilbert_etal_2025_Ecography/code/14_disease_faith_plot_model.R at main · zipkinlab/Gilbert_etal_2025_Ecography · GitHub

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# This script is mildly computationally intensive
# It was run on a supercomputer with 3 cores and 3GB of memory per core, and took ~6 hours
# It would probably be able to run on a desktop in a pinch
library(here)
library(tidyverse)
library(parallel)
library(nimble)

setwd(here::here("data"))
final <- readr::read_csv("disease_with_biodiversity_metrics_v01.csv") %>%
  dplyr::group_by(scientificName) %>%
  dplyr::mutate(sp_disease = cur_group_id())

data <- list(
  y = final$positive,
  faith_plot_mean = final$faith_plot_mean,
  faith_plot_sd = final$faith_plot_sd)

constants <- list(
  nsp = length(unique(final$sp_disease)),
  nsite = length(unique(final$site)),
  nind = nrow(final),
  site = final$site,
  sp = final$sp_disease)

code <- nimbleCode({
  mu_gamma0 ~ dnorm(0, sd = 1)
  sd_gamma0 ~ dexp(1)
  sd_epsilon ~ dexp(1)
  gamma1 ~ dnorm(0, sd = 1)
  for(i in 1:nsp){
    gamma0[i] ~ dnorm( mu_gamma0, sd = sd_gamma0 )
  }
  for( i in 1:nsite){
    epsilon[i] ~ dnorm( 0, sd = sd_epsilon )
  }
  mean_faith <- mean( faith_plot[1:nind] )
  sd_faith <- sd( faith_plot[1:nind] )
  for( i in 1:nind ) {
    faith_plot[i] ~ T( dnorm( faith_plot_mean[i], sd = faith_plot_sd[i] ), 0, )
    faith_plot_scaled[i] <- ( faith_plot[i] - mean_faith ) / sd_faith
    y[i] ~ dbern( kappa[i] )
    logit( kappa[i] ) <- gamma0[ sp[i] ] + gamma1 * faith_plot_scaled[i] + epsilon[site[i]]
  }
})

inits <- function(){
  list(
    mu_gamma0 = rnorm(1, 0, 0.1),
    faith_plot = data$faith_plot_mean,
    mean_faith = mean( data$faith_plot_mean),
    sd_faith = mean( data$faith_plot_sd),
    sd_gamma0 = runif(1, 0, 1),
    gamma1 = rnorm(1, 0, 0.25),
    gamma0 = rnorm(constants$nsp, 0, 1),
    sd_epsilon = rexp(1),
    epsilon = rnorm(constants$nsite, 0, 1)
  )
}

params <- c("mu_gamma0", "sd_gamma0", "gamma1", "gamma0", "sd_epsilon",
            "epsilon",  "mean_faith", "sd_faith")

nc <- 3
nb <- 15000
ni <- nb + 10000
nt <- 10

start <- Sys.time()
cl <- makeCluster(nc)

parallel::clusterExport(cl, c("code",
                              "inits",
                              "data",
                              "constants",
                              "params",
                              "nb",
                              "ni",
                              "nt"))

for(j in seq_along(cl)) {
  set.seed(j)
  init <- inits()
  clusterExport(cl[j], "init")
}

out <- clusterEvalQ(cl, {
  library(nimble)
  library(coda)

  model <- nimbleModel(code = code,
                       name = "code",
                       constants = constants,
                       data = data,
                       inits = init)

  Cmodel <- compileNimble(model)
  modelConf <- configureMCMC(model)
  modelConf$addMonitors(params)
  modelMCMC <- buildMCMC(modelConf)
  CmodelMCMC <- compileNimble(modelMCMC, project = model)
  out1 <- runMCMC(CmodelMCMC,
                  nburnin = nb,
                  niter = ni,
                  thin = nt)

  return(as.mcmc(out1))
})
end <- Sys.time()
print(end - start)
stopCluster(cl)

save( code, data, constants, out,
      file = paste0("rodent_pathogen_faith_plot_", Sys.Date(), ".RData"))