SimBIID (version 0.2.2)

summary.ABCSMC: Summarises ABCSMC objects

Description

Summary method for ABCSMC objects.

Usage

# S3 method for ABCSMC
summary(object, gen = NA, transfunc = NA, ...)

Value

A data.frame with weighted posterior means and variances.

Arguments

object

An ABCSMC object.

gen

The generation of ABC that you want to extract. If left missing then defaults to final generation.

transfunc

Is a function object where the arguments to the function must match all or a subset of the parameters in the model. This function needs to return a data.frame object with columns containing the transformed parameters.

...

Not used here.

See Also

ABCSMC, print.ABCSMC, plot.ABCSMC

Examples

# \donttest{
## set up SIR simulationmodel
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 return summary statistics
simSIR <- function(pars, data, tols, u, model) {

    ## run model
    sims <- model(pars, 0, data[2] + tols[2], u)
    
    ## this returns a vector of the form:
    ## completed (1/0), t, S, I, R (here)
    if(sims[1] == 0) {
        ## if simulation rejected
        return(NA)
    } else {
        ## extract finaltime and finalsize
        finaltime <- sims[2]
        finalsize <- sims[5]
    }
    
    ## return vector if match, else return NA
    if(all(abs(c(finalsize, finaltime) - data) <= tols)){
        return(c(finalsize, finaltime))
    } else {
        return(NA)
    }
}

## 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)

## define the targeted summary statistics
data <- c(
    finalsize = 30, 
    finaltime = 76
)

## set initial states (1 initial infection 
## in population of 120)
iniStates <- c(S = 119, I = 1, R = 0)

## set initial tolerances
tols <- c(
    finalsize = 50,
    finaltime = 50
)

## run 2 generations of ABC-SMC
## setting tolerance to be 50th
## percentile of the accepted 
## tolerances at each generation
post <- ABCSMC(
    x = data, 
    priors = priors, 
    func = simSIR, 
    u = iniStates, 
    tols = tols, 
    ptol = 0.2, 
    ngen = 2, 
    npart = 50,
    model = model
)
post

## run one further generation
post <- ABCSMC(post, ptols = 0.5, ngen = 1)
post
summary(post)

## plot posteriors
plot(post)

## plot outputs
plot(post, "output")
# }