predict.PMCMC {SimBIID}

Predicts future course of outbreak from PMCMC objects

Description

Predict method for PMCMC objects.

Usage

## S3 method for class 'PMCMC'
predict(object, tspan, npart = 50, ...)

Arguments

object

A PMCMC object.

tspan

A vector of times over which to output predictions.

npart

The number of particles to use in the bootstrap filter.

...

Not used here.

Value

A SimBIID_runs object.

See Also

PMCMC, print.PMCMC, plot.PMCMC, summary.PMCMC window.PMCMC

Examples

## set up data to pass to PMCMC
flu_dat <- data.frame(
    t = 1:14,
    Robs = c(3, 8, 26, 76, 225, 298, 258, 233, 189, 128, 68, 29, 14, 4)
)

## set up observation process
obs <- data.frame(
    dataNames = "Robs",
    dist = "pois",
    p1 = "R + 1e-5",
    p2 = NA,
    stringsAsFactors = FALSE
)

## set up model (no need to specify tspan
## argument as it is set in PMCMC())
transitions <- c(
    "S -> beta * S * I / (S + I + R + R1) -> I", 
    "I -> gamma * I -> R",
    "R -> gamma1 * R -> R1"
)
compartments <- c("S", "I", "R", "R1")
pars <- c("beta", "gamma", "gamma1")
model <- mparseRcpp(
    transitions = transitions, 
    compartments = compartments,
    pars = pars,
    obsProcess = obs
)

## set priors
priors <- data.frame(
    parnames = c("beta", "gamma", "gamma1"), 
    dist = rep("unif", 3), 
    stringsAsFactors = FALSE)
priors$p1 <- c(0, 0, 0)
priors$p2 <- c(5, 5, 5)

## define initial states
iniStates <- c(S = 762, I = 1, R = 0, R1 = 0)

## run PMCMC algorithm for first three days of data
post <- PMCMC(
    x = flu_dat[1:3, ], 
    priors = priors, 
    func = model, 
    u = iniStates, 
    npart = 75, 
    niter = 10000, 
    nprintsum = 1000
)

## plot traces
plot(post, "trace")

## run predictions forward in time
post_pred <- predict(
    window(post, start = 2000, thin = 8), 
    tspan = 4:14
)

## plot predictions
plot(post_pred, quant = c(0.6, 0.75, 0.95))