abcRun: Run a set of simulations initialised with parameters sampled...

In smfsb: Stochastic Modelling for Systems Biology

Description

Run a set of simulations initialised with parameters sampled from a given prior distribution, and compute statistics required for an ABC analaysis. Typically used to calculate "distances" of simulated synthetic data from observed data.

Usage

abcRun(n, rprior, rdist)

Arguments

n	An integer representing the number of simulations to run.
rprior	A function without arguments generating a single parameter (vector) from prior distribution.
rdist	A function taking a parameter (vector) as argument and returning the required statistic of interest. This will typically be computed by first using the parameter to run a forward model, then computing required summary statistics, then computing a distance. See the example for details.

Value

A list with elements 'param' and 'dist'. These will be returned as matrices or vectors depending on whether the parameters and distances are scalars or vectors.

See Also

pfMLLik, StepGillespie, abcSmc, simTs, stepLVc

Examples

data(LVdata)
rprior <- function() { exp(c(runif(1, -3, 3),runif(1,-8,-2),runif(1,-4,2))) }
rmodel <- function(th) { simTs(c(50,100), 0, 30, 2, stepLVc, th) }
sumStats <- identity
ssd = sumStats(LVperfect)
distance <- function(s) {
    diff = s - ssd
    sqrt(sum(diff*diff))
}
rdist <- function(th) { distance(sumStats(rmodel(th))) }
out = abcRun(10000, rprior, rdist)
q=quantile(out$dist, c(0.01, 0.05, 0.1))
print(q)
accepted = out$param[out$dist < q[1],]
print(summary(accepted))
print(summary(log(accepted)))

smfsb documentation built on May 2, 2019, 5:13 a.m.