simHHH {surveillance} | R Documentation |
Simulates a multivariate time series of counts based on the Poisson/Negative Binomial model as described in Held et al. (2005).
## Default S3 method: simHHH(model=NULL, control = list(coefs = list(alpha=1, gamma = 0, delta = 0, lambda = 0, phi = NULL, psi = NULL, period = 52), neighbourhood = NULL, population = NULL, start = NULL), length) ## S3 method for class 'ah': simHHH(model, control = model$control, length)
control |
list with
|
model |
Result of a model fit with
algo.hhh , the estimated parameters
are used to simulate data |
length |
number of time points to simulate |
Simulates data from a Poisson or a Negative Binomial model with mean
μ_{it} = λ y_{i,t-1} + phi sum_{j sim i} y_{j,t-1} + n_{it} nu_{it}
where
log nu_{it} = α_i + sum_{s=1}^{S}(gamma_s sin(omega_s t) + delta_s cos(omega_s t))
omega_s = 2sπ/period
are Fourier frequencies
and n_{it} are possibly standardized population sizes.
Returns a list with elements
data |
disProgObj of simulated data |
mean |
matrix with mean μ_{i,t} that was used to simulate the data |
endemic |
matrix with only the endemic part nu_{i,t} |
coefs |
list with parameters of the model |
The model does not contain a linear trend.
Held, L., Höhle, M., Hofmann, M. (2005). A statistical framework for the analysis of multivariate infectious disease surveillance counts. Statistical Modelling, 5, p. 187-199.