algo.hhh {surveillance}R Documentation

Model fit based on the Held, Höhle, Hofman 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(lambda=TRUE, neighbours=FALSE, 
   linear=FALSE, nseason = 0,
   negbin=c("none", "single", "multiple"), 
   proportion=c("none", "single", "multiple")), 
   thetastart=NULL, verbose=TRUE)

Arguments

disProgObj Object of class disProg
control Control object:
lambda
if TRUE an autoregressive parameter λ is included, if lambda is a vector of logicals, unit-specific parameters λ_i are included
neighbours
if TRUE an autoregressive parameter for adjacent units phi is included, if neighbours is a vector of logicals, unit-specific parameters phi_i are included
linear
a logical (or a vector of logicals) indicating wether a linear trend β (or a linear trend β_i for each unit) is included
nseason
Integer number of Fourier frequencies; if nseason is a vector of integers, each unit i gets its own seasonal parameters
negbin
if "single" negative binomial rather than poisson is used, if "multiple" unit-specific overdispersion parameters are used.
proportion
see details in meanResponse
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

Returns an object of class ah with elements

coefficients estimated parameters
se estimated standard errors
cov covariance matrix
loglikelihood loglikelihood
convergence logical indicating whether optim converged or not
fitted.values fitted mean values μ_it
control specified control object
disProgObj specified disProg-object

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: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)


[Package surveillance version 0.9-9 Index]