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(lambda=TRUE, neighbours=FALSE, linear=FALSE, nseason = 0, negbin=c("none", "single", "multiple"), proportion=c("none", "single", "multiple"),lag.range=NULL), 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.
ahg |
Returns an object of class ah with 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$observed), observed=salmonella.agona$observed, state=salmonella.agona$state) model1 <- list(lambda=TRUE, linear=TRUE, nseason=1, negbin="single") algo.hhh(salmonella, control=model1) # multivariate time series: # measles cases in Lower Saxony, Germany data(measles.weser) # same model as above algo.hhh(measles.weser, control=model1) # different starting values for # theta = (lambda, beta, gamma_1, gamma_2, psi) startValues <- c(0.1, rep(0, 3), 1) algo.hhh(measles.weser, control=model1, thetastart=startValues) # include autoregressive parameter phi for adjacent "Kreise" model2 <- list(lambda=TRUE, neighbours=TRUE, linear=FALSE, nseason=1, negbin="single") algo.hhh(measles.weser, control=model2) ## weekly counts of influenza and meningococcal infections ## in Germany, 2001-2006 data(influMen) # specify model with two autoregressive parameters lambda_i, overdispersion # parameters psi_i, an autoregressive parameter phi for meningococcal infections # (i.e. nu_flu,t = lambda_flu * y_flu,t-1 # and nu_men,t = lambda_men * y_men,t-1 + phi_men*y_flu,t-1 ) # and S=(3,1) Fourier frequencies model <- list(lambda=c(TRUE,TRUE), neighbours=c(FALSE,TRUE), linear=FALSE,nseason=c(3,1),negbin="multiple") # run algo.hhh algo.hhh(influMen, control=model) # now meningococcal infections in the same week should enter as covariates # (i.e. nu_flu,t = lambda_flu * y_flu,t-1 # and nu_men,t = lambda_men * y_men,t-1 + phi_men*y_flu,t ) model2 <- list(lambda=c(1,1), neighbours=c(NA,0), linear=FALSE,nseason=c(3,1),negbin="multiple") algo.hhh(influMen, control=model2)