inst/reproduce/epidemiology.posterior2given1plugin.run.R

In pierrejacob/bettertogether: Better Together? Joint model versus moduarlized approach

library(bettertogether)
rm(list = setdiff(setdiff(ls(), "scriptfolder"), "resultsfolder"))
setmytheme()
registerDoParallel(cores = detectCores())

set.seed(16)
# nhpv considered as Y
nhpv <- c(7, 6, 10, 10, 1, 1, 10, 4, 35, 0, 10, 8, 4)
y1 <- matrix(nhpv, nrow = 1)
# Npart is put in the parameters
Npart <- c(111, 71, 162, 188, 145, 215, 166, 37, 173,
           143, 229, 696, 93)
J <- 13
# For module 2, ncases considered data
ncases <- c(16, 215, 362, 97, 76, 62, 710, 56, 133,28, 62, 413, 194)
y2 <- matrix(ncases, nrow = 1)
# Npop considered parameters
Npop <- c(26983, 250930, 829348, 157775, 150467, 352445, 553066,
          26751, 75815, 150302, 354993, 3683043, 507218)
Npop_normalized <- log(10**(-3) * Npop)
#
posterior_phi_alpha <- 1 + nhpv
posterior_phi_beta <- 1 + Npart - nhpv

# Find parameters given Y2
hyper2 <- list(theta2_mean_prior = 0, theta2_prior_sd = sqrt(1000))
dprior2 <- function(theta2s, hyper2){
  return(sapply(1:nrow(theta2s), function(index)
    sum(dnorm(theta2s[index,], mean = hyper2$theta2_mean_prior, sd = hyper2$theta2_prior_sd, log = TRUE))))
}
rprior2 <- function(n, hyper2){
  theta2s <- matrix(0, nrow = n, ncol = 2)
  for (j in 1:2){
    theta2s[,j] <- rnorm(n, mean = hyper2$theta2_mean_prior, sd = hyper2$theta2_prior_sd)
  }
  return(theta2s)
}

# posterior in the first module is given by
# 13 Beta distributions with parameters
posterior_phi_alpha <- 1 + nhpv
posterior_phi_beta <- 1 + Npart - nhpv
### plug-in approach
theta1hat <- posterior_phi_alpha / (posterior_phi_alpha + posterior_phi_beta)


target2given1plugin <- list(thetadim = 2, ydim = 1,
                            rprior = function(n, ...){
                              return(rprior2(n, hyper2))
                            },
                            dprior = function(thetas, ...){
                              evals <- dprior2(thetas, hyper2)
                              return(evals)
                            },
                            fullloglikelihood = function(thetas, ys, parameters){
                              return(plummer_module2_conditional(parameters$theta1hat, thetas, ncases, Npop_normalized))
                            },
                            conditionallikelihood = function(thetas, ys, idata, parameters){
                              return(plummer_module2_conditional(parameters$theta1hat, thetas, ncases, Npop_normalized))
                            },
                            parameters = list(theta1hat = theta1hat))

param_algo <- list(nthetas = 2^10, minimum_diversity = 0.8, nmoves = 100, proposal = mixture_rmixmod())

rep <- 5
filename <- paste0("epidemiology_module2givenmodule1plugin.N", param_algo$nthetas, ".K", param_algo$nmoves, ".rep", rep, ".RData")
results2given1plugin <- foreach(irep = 1:rep) %dorng% {
  res <- smcsampler(y2, target2given1plugin, param_algo)
  list(thetas2 = res$thetas_history[[2]], normw2 = res$normw_history[[2]], logevidence = res$logevidence,
       theta1hat = target2given1plugin$parameters$theta1hat)
}
save(results2given1plugin, file = filename)
# load(filename)