algo.hhh {surveillance}R Documentation

Model fit based on the Held, Höhle, Hofmann paper

Description

Fits a Poisson/Negative Binomial model with mean μ_{it} (as described in Held/Höhle/Hofmann, 2005) to a multivariate time series of counts.

Usage

  algo.hhh(disProgObj, control=list(linear=FALSE, nseason=0, period=52,   
      neighbours=FALSE, negbin=FALSE, lambda=TRUE), thetastart=NULL, 
      verbose=TRUE)

Arguments

disProgObj object of class disProg
control Control object:
    linear
    if true a linear trend is included.
    nseason
    number of Fourier frequencies to be used
    period
    period length (defaults to 52 for weekly data)
    neighbours
    if true a autoregressive parameter for adjacent units is included.
    negbin
    if true negative binomial rather than poisson is used.
    lambda
    if true a autoregressive parameter is included.
thetastart vector with starting values for all parameters specified in the control object (for optim).
verbose if true information about convergence is printed

Details

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.

Value

list Returns a list with estimated parameters, standard errors, covariance matrix and the value of the loglikelihood as elements.

Author(s)

M. Paul, L. Held, M. Höhle

Source

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.

See Also

meanResponse

Examples


    # univariate time series: salmonella agona cases
    data(salmonella.agona)
    salmonella <- create.disProg(week=1:nrow(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)
                  

[Package surveillance version 0.9-2 Index]