A model fitting toolkit for simulation models with the odin package
In R, you can install from github
devtools::install_github("TimeWz667/odin2data", upgrade = FALSE)Load the package before use it
library(odin2data)### Load the model file
f <- system.file("models/SIR.txt", package = "odin2data")
test_m <- odin::odin(f, verbose=F)
### Generate a prior and set up it as a list
r_prior <- function() {
list(beta = runif(1, 0.1, 5), gamma = runif(1, 0.1, 0.3))
}
d_prior <- function(pars) {
dunif(pars$beta, 0.1, 5, log = T) + dunif(pars$gamma, 0.1, 0.3, log = T)
}
times = seq(0, 10, 0.2)
y0 <- c(995, 5, 0)
### Compile all elements as a simulation model
sim <- odin2data::compile_model(d_prior, r_prior, y0, ts_sim = times, m_sim = test_m)
test_data <- data.frame(
t = 1:5,
incidence = c(20, 49, 109, 184, 206) / 1000
)
### Compile the model with data
lf <- odin2data::compile_model_likefree(test_data, sim)odin2data provides three types of fitting algorithm currently
- ABC
- ABC-SMC
- ABC-PMC
post <- odin2data::fit(lf, 1000, method = "abc", target_acc = 0.01)
post <- odin2data::fit(lf, 1000, method = "abcsmc", max_round = 20, alpha = 0.80)
post <- odin2data::fit(lf, 1000, method = "abcpmc", max_round = 20)
summary(post)Chu-Chang Ku,
Department of Infectious Disease Epidemiology, Imperial College London, UK (Health Economic and Decision Science, University of Sheffield, UK)
Email: c.ku@ic.ac.uk
MIT (c) 2020 Chu-Chang Ku