algo.hhh {surveillance} | R Documentation |
Fits a Poisson/Negative Binomial model with mean μ_{it} (as described in Held/Höhle/Hofmann, 2005) to a multivariate time series of counts.
algo.hhh(disProgObj, control=list(linear=FALSE, nseason=0, period=52, neighbours=FALSE, negbin=FALSE, lambda=TRUE), thetastart=NULL, verbose=TRUE)
disProgObj |
object of class disProg |
control |
Control object:
|
thetastart |
vector with starting values for all parameters specified
in the control object (for optim ). |
verbose |
if true information about convergence is printed |
Note that for the time being this function is not a surveillance algorithm, but only a modelling approach as described in the Held et. al (2005) paper.
list |
Returns a list with estimated parameters, standard errors, covariance matrix and the value of the loglikelihood as elements. |
M. Paul, L. Held, M. Höhle
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.
# univariate time series: salmonella agona cases data(salmonella.agona) salmonella <- create.disProg(week=1:length(salmonella.agona), observed=salmonella.agona$observed, state=salmonella.agona$state) model1 <- list(lambda=TRUE, linear=TRUE, nseason=1, negbin=TRUE) algo.hhh(salmonella, control=model1) # multivariate time series: measels cases data(measels.weser) # same model as above algo.hhh(measels.weser, control=model1) # different starting values for (lambda, beta, gamma, delta, psi) startValues <- c(0.1, rep(0, 3), 1) algo.hhh(measels.weser, control=model1, thetastart=startValues) # include autoregressive parameter phi for adjacent "Kreise" model2 <- list(lambda=TRUE, neighbours=TRUE, linear=TRUE, nseason=1, negbin=TRUE) algo.hhh(measels.weser, control=model2)