ABCRef {SimBIID} | R Documentation |
Produces ABC reference table
Description
Produces reference table of simulated outcomes for use in various Approximate Bayesian Computation (ABC) algorithms.
Usage
ABCRef(
npart,
priors,
pars,
func,
sumNames,
parallel = FALSE,
mc.cores = NA,
...
)
Arguments
npart |
The number of particles (must be a positive integer). |
priors |
A |
pars |
A named vector or matrix of parameters to use for the simulations. If |
func |
Function that runs the simulator. The first argument must be |
sumNames |
A |
parallel |
A |
mc.cores |
Number of cores to use if using parallel processing. |
... |
Extra arguments to be passed to |
Details
Runs simulations for a large number of particles, either pre-specified or
sampled from the a set of given prior distributions. Returns a table of summary
statistics for each particle. Useful for deciding on initial tolerances during an
ABCSMC
run, or for producing a reference table to use in e.g. the
ABC with Random Forests approach of Raynal et al. (2017).
Value
An data.frame
object with npart
rows, where the first p
columns correspond to
the proposed parameters, and the remaining columns correspond to the simulated outputs.
References
Raynal, L, Marin J-M, Pudlo P, Ribatet M, Robert CP and Estoup A. (2017) <ArXiv:1605.05537>
Examples
## set up SIR simulation model
transitions <- c(
"S -> beta * S * I -> I",
"I -> gamma * I -> R"
)
compartments <- c("S", "I", "R")
pars <- c("beta", "gamma")
model <- mparseRcpp(
transitions = transitions,
compartments = compartments,
pars = pars
)
model <- compileRcpp(model)
## generate function to run simulators
## and produce final epidemic size and time
## summary statistics
simRef <- function(pars, model) {
## run model over a 100 day period with
## one initial infective in a population
## of 120 individuals
sims <- model(pars, 0, 100, c(119, 1, 0))
## return vector of summary statistics
c(finaltime = sims[2], finalsize = sims[5])
}
## set priors
priors <- data.frame(
parnames = c("beta", "gamma"),
dist = rep("gamma", 2),
stringsAsFactors = FALSE
)
priors$p1 <- c(10, 10)
priors$p2 <- c(10^4, 10^2)
## produce reference table by sampling from priors
## (add additional arguments to 'func' at the end)
refTable <- ABCRef(
npart = 100,
priors = priors,
func = simRef,
sumNames = c("finaltime", "finalsize"),
model = model
)
refTable