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main.R
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main.R
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# MIT License
#
# Copyright (c) 2023 Ivan Specht
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
### Execute large-scale outbreak reconstruction algorithm
set.seed(232)
## Libraries
library(ape)
library(Rcpp)
library(ggplot2)
library(igraph)
library(ggraph)
library(cowplot)
library(parallel)
source("likelihood.R")
source("moves.R")
source("prior.R")
source("subroutines.R")
sourceCpp("cpp_subroutines.cpp")
source("initialize.R")
source("global_mcmc.R")
source("local_mcmc.R")
### M-H algo
run_mcmc <- function(mcmc, data, noisy = F){
output <- list()
liks <- c()
for (r in 1:data$n_global) {
# For reproducible results
#set.seed(r)
# Make global moves
mcmc <- global_mcmc(mcmc, data)
# Chop up the tree into pieces
breakdowns <- breakdown(mcmc, data)
mcmcs <- breakdowns[[1]]
datas <- breakdowns[[2]]
if(noisy){
message(paste("Parallelizing over", length(mcmcs), "cores..."))
}
all_res <- parallel::mclapply(
1:length(mcmcs),
function(i, mcmcs, datas){
local_mcmc(mcmcs[[i]], datas[[i]])
},
mcmcs = mcmcs,
datas = datas,
mc.set.seed = F,
mc.cores = length(mcmcs)
)
#...or run in series
# all_res <- list()
# for (j in 1:length(mcmcs)) {
# all_res[[j]] <- local_mcmc(mcmcs[[j]], datas[[j]])
# }
# Amalgamate results of parallel MCMC run
amalgam <- amalgamate(all_res, mcmcs, datas, mcmc, data)
# Record amalgamated results, filtering to parameters of interest
for (i in 1:length(amalgam)) {
output <- c(output, list(
amalgam[[i]][data$record]
))
}
# "mcmc" is now the most recent result
mcmc <- amalgam[[length(amalgam)]]
#print(r)
liks <- c(liks, mcmc$e_lik + sum(mcmc$g_lik[2:mcmc$n]) + mcmc$prior)
if(noisy){
message(paste(r, "global iterations complete. Log-likelihood =", round(liks[r], 2)))
print(plot_current(mcmc$h, data$n_obs))
print(mcmc$w)
print(mcmc$mu)
print(mcmc$p)
print(mcmc$a_g)
print(mcmc$rho * (1-mcmc$psi) / mcmc$psi)
print(mcmc$prior)
#print(mcmc$rho * (1 - mcmc$psi) / mcmc$psi)
# print(length(unlist(mcmc$m01)) + length(unlist(mcmc$m10)))
# print(length(unlist(mcmc$mx1)))
#print(data$s - mcmc$t[1:data$n_obs])
#print(mcmc$lambda)
#print(mcmc$h)
# print(mcmc$a_g)
}
# if(r == 10){
# data$n_subtrees <- 3
# }
}
return(list(
liks, output
))
}