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04_fitting_priors.R
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##Here, we fit prior stergm outputs to case data
#First, lets load in libraries
library(here)
library(dplyr)
library(tidyverse)
library(janitor)
library(lubridate)
library(readxl)
library(here)
library(httr)
set.seed(12345)
nycdata <- read.csv("NYC_data_through_jan25.csv")
#We fit via two procedures. In the first, we eliminate runs that differ
#in final cumulative cases by more than 50 percent
#compared to empirical data at end of the for the fitting timeline
#so set those limits here
lower_cum_case <- 2300
upper_cum_case <- 4300
#OK, Now load in simulations
prior.index <- 0
runs.per <- 6
datasets <- 166
likelihood.results <- matrix(NA,1000,2)
likelihood.results <- as.data.frame(likelihood.results)
colnames(likelihood.results) <- c("set","likelihood")
row.index <- 1
all.cases <- c()
for(data.index in 1:datasets){
current.data.set <- readRDS(paste(
paste("prior_runs_real/abc_nc_prior_output",
prior.index + ((data.index-1) * runs.per) + 1,
prior.index + ((data.index-1) * runs.per) + runs.per,
sep = '_'),"RDATA",sep="."))
for(run.index in 1:runs.per){
param.index = prior.index + ((data.index-1) * runs.per) + run.index
model.cases <- current.data.set[[run.index]]$epi$test.flow$sim1[1:length(ncdata$mean.cases)]
model.cases[1] <- 0
disp <- mean(model.cases)/(var(model.cases)/mean(model.cases) - 1)
likelihood.results$set[row.index] <- param.index
likelihood.results$likelihood[row.index] <- sum((gamma(disp + nycdata$mean.cases)/(factorial(nycdata$mean.cases) * gamma(disp))) *
((model.cases/(model.cases + disp))^nycdata$mean.cases) *
(1 + model.cases/disp)^(-disp))
if(sum(model.cases) < lower_cum_case | sum(model.cases) > upper_cum_case){
likelihood.results$likelihood[row.index] <- 0
}
all.cases <- cbind(all.cases,model.cases)
row.index <- row.index + 1
}
}
prior.index <- 996
runs.per <- 4
datasets <- 1
for(data.index in 1:datasets){
current.data.set <- readRDS(paste(
paste("prior_runs_real/abc_nyc_prior_output",
prior.index + ((data.index-1) * runs.per) + 1,
prior.index + ((data.index-1) * runs.per) + runs.per,
sep = '_'),"RDATA",sep="."))
for(run.index in 1:runs.per){
param.index = prior.index + ((data.index-1) * runs.per) + run.index
model.cases <- current.data.set[[run.index]]$epi$test.flow$sim1[1:length(nycdata$mean.cases)]
model.cases[1] <- 0
disp <- mean(model.cases)/(var(model.cases)/mean(model.cases) - 1)
likelihood.results$set[row.index] <- param.index
likelihood.results$likelihood[row.index] <- sum((gamma(disp + nycdata$mean.cases)/(factorial(nycdata$mean.cases) * gamma(disp))) *
((model.cases/(model.cases + disp))^nycdata$mean.cases) *
(1 + model.cases/disp)^(-disp))
if(sum(model.cases) < lower_cum_case | sum(model.cases) > upper_cum_case){
likelihood.results$likelihood[row.index] <- 0
}
all.cases <- cbind(all.cases,model.cases)
row.index <- row.index + 1
}
}
write.csv(all.cases,paste(
paste("incident_cases",
1,
prior.index + datasets * runs.per,
sep = '_'),"csv",sep="."))
write.csv(likelihood.results,paste(
paste("likelihoods",
1,
prior.index + datasets * runs.per,
sep = '_'),"csv",sep="."))
###############
Likelihood_data <- read.csv("likelihoods_1_1000.csv")
Likelihood_data <- Likelihood_data[,-1]
parameter_posterior <- Likelihood_data[sample(seq_len(nrow(Likelihood_data)),100,prob=Likelihood_data$likelihood,replace = TRUE),]
parameter_posterior$sample <- 0
parameter_posterior$trans <- 0
parameter_posterior$eventtrans <- 0
parameter_posterior$behaveadapt <- 0
parameter_posterior$eventtime <- 0
parameter_posterior$import <- 0
parameter_priors <- read.csv("params_set.csv")
for(i in 1:length(parameter_posterior$trans)){
parameter_posterior$sample[i] <- i
parameter_posterior$trans[i] <- parameter_priors$trans[parameter_posterior$set[i]]
parameter_posterior$eventtrans[i] <- parameter_priors$eventtrans[parameter_posterior$set[i]]
parameter_posterior$behaveadapt[i] <- parameter_priors$behaveadapt[parameter_posterior$set[i]]
parameter_posterior$eventtime[i] <- parameter_priors$eventtime[parameter_posterior$set[i]]
parameter_posterior$import[i] <- parameter_priors$import[parameter_posterior$set[i]]
}
write.csv(parameter_posterior,"posterior_runs_real/parameter_posterior.csv")