surveillance (version 1.7-0)

simulate.ah4: Simulates data based on the model proposed by Paul and Held (2011)

Description

Simulates a multivariate time series of counts based on the Poisson/Negative Binomial model as described in Paul and Held (2011).

Usage

## S3 method for class 'ah4':
simulate(object, nsim = 1, seed = NULL, y.start = NULL,
         subset = 1:nrow(object$stsObj), coefs = coef(object),
         components=c("ar","ne","end"), simplify=nsim>1, ...)

Arguments

object

an object of class "ah4".

nsim

number of time series to simulate. Defaults to 1.

seed

an object specifying how the random number generator should be initialized for simulation (via set.seed). The initial state will also be stored as an attribute "seed" of the result

y.start

vector or matrix (with ncol(object$stsObj) columns) with starting counts for the epidemic components. If NULL, the observed means in the respective units of the data in object during subset

subset

time period in which to simulate data. Defaults to the whole period.

coefs

coefficients used for simulation from the model in object. Default is to use the fitted parameters. Note that the coefs-vector must be in the same order and scaling as coef(object).

components

character vector indicating which components of the fitted model object should be active during simulation. For instance, a simulation with components="end" is solely based on the fitted endemic mean.

simplify

logical indicating if only the simulated counts (TRUE) or the full sts object (FALSE) should be returned for every replicate. Defaults to counts only if nsim>1, such that the result will be an array of di

...

unused (argument of the generic).

Value

• An object of class "sts" in case nsim = 1, and a list of such objects otherwise.

encoding

latin1

Details

Simulates data from a Poisson or a Negative Binomial model with mean $$\mu_{it} = \lambda_{it} y_{i,t-1} + \phi_{it} \sum_{j \neq i} w_{ji} y_{j,t-1} + \nu_{it}$$ where $\lambda_{it}>0$, $\phi_{it}>0$, and $\nu_{it}>0$ are parameters which are modelled parametrically. The function uses the model and parameter estimates of the fitted object to simulate the time series.

With the argument coefs it is possible to simulate from the model as specified in object, but with different parameter values.

References

Paul, M. and Held, L. (2011) Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts. Statistics in Medicine, 30, 1118--1136

See Also

hhh4, simHHH

Examples

data(influMen)
# convert to sts class and extract meningococcal disease time series
meningo <- disProg2sts(influMen)[,2]

# fit model
fit <- hhh4(meningo, control = list(ar = list(f = ~ 1),
            end = list(f = addSeason2formula(S = 1, period = 52)),
            family = "NegBin1"))
plot(fit)

# simulate from model
simData <- simulate(fit, seed=1234)

# plot simulated data
plot(simData, main = "simulated data", legend.opts = NULL, xaxis.years = FALSE)

# consider a Poisson instead of a NegBin model
coefs <- coef(fit)
coefs["overdisp"] <- 0

simData2 <- simulate(fit, seed=123, coefs = coefs)
plot(simData2, main = "simulated data: Poisson model", 
     legend.opts = NULL, xaxis.years = FALSE)

# consider a model with higher autoregressive parameter
coefs <- coef(fit)
coefs[1] <- log(0.5)

simData3 <- simulate(fit, seed=321, coefs = coefs)
plot(simData3, main = "simulated data: lambda = 0.5", 
     legend.opts = NULL, xaxis.years = FALSE)