SimBIID (version 0.2.2)

predict.PMCMC: Predicts future course of outbreak from PMCMC objects

Description

Predict method for PMCMC objects.

Usage

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

Value

A SimBIID_runs object.

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.

See Also

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

Examples

# \donttest{
## 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))
# }