surveillance (version 1.12.1)

algo.hhh.grid: Fit a Classical HHH Model (DEPRECATED) with Varying Start Values

Description

algo.hhh.grid tries multiple starting values in algo.hhh. Starting values are provided in a matrix with gridSize rows (usually created by create.grid). The grid search is conducted until either all starting values are used or a time limit maxTime is exceeded. The result with the highest likelihood is returned. Note that the algo.hhh implementation of HHH models is deprecated and superseded by the function hhh4.

Usage

algo.hhh.grid(disProgObj, control=list(lambda=TRUE, neighbours=FALSE, 
   linear=FALSE, nseason=0,
   negbin=c("none", "single", "multiple"), 
   proportion=c("none", "single", "multiple"),lag.range=NULL), 
   thetastartMatrix, maxTime=1800, verbose=FALSE)

Arguments

disProgObj

object of class disProg

control

control object: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

thetastartMatrix

matrix with initial values for all parameters specified in the control object as rows.

verbose

if true progress information is printed

maxTime

maximum of time (in seconds) to elapse until algorithm stopps.

Value

• Returns an object of class ahg with elements
• best
  result of a call to algo.hhh with highest likelihood
• allLoglik
  values of loglikelihood for all starting values used
• gridSize
  number of different starting values in thetastartMatrix
• gridUsed
  number of used starting values
• time
  elapsed time
• convergence
  if false algo.hhh did not converge for all (used) starting values

encoding

latin1

References

Held, L., H�hle{Hoehle}, M., Hofmann, M. (2005) A statistical framework for the analysis of multivariate infectious disease surveillance counts, Statistical Modelling, 5, 187--199. Paul, M., Held, L. and Toschke, A. M. (2008) Multivariate modelling of infectious disease surveillance data, Statistics in Medicine, 27, 6250--6267.

See Also

algo.hhh, create.grid

Examples

## monthly counts of menigococcal infections in France
data(meningo.age)
            
# specify model for algo.hhh.grid
model1 <- list(lambda=TRUE)

# create grid of inital values
grid1 <- create.grid(meningo.age, model1, 
                     params = list(epidemic=c(0.1,0.9,5)))
                     
# try multiple starting values, print progress information
algo.hhh.grid(meningo.age, control=model1, thetastartMatrix=grid1,
               verbose=TRUE)


## more sophisticated models with a much longer runtime follow
# specify model
model2 <- list(lambda=TRUE, neighbours=TRUE, negbin="single",
               nseason=1)
grid2 <- create.grid(meningo.age, model2, 
                     params = list(epidemic=c(0.1,0.9,3), 
                                   endemic=c(-0.5,0.5,3), 
                                   negbin = c(0.3, 12, 10)))
                                 
# run algo.hhh.grid, search time is limited to 30 sec
algo.hhh.grid(meningo.age, control=model2, thetastartMatrix=grid2,
              maxTime=30)
              
## 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")
              
# create grid of initial values
grid <- create.grid(influMen,model, list(epidemic=c(.1,.9,3),
              endemic=c(-.5,.5,3), negbin=c(.3,15,10)))
# run algo.hhh.grid, search time is limited to 30 sec
algo.hhh.grid(influMen, control=model, thetastartMatrix=grid, maxTime=30)

# 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.grid(influMen, control=model2, thetastartMatrix=grid, maxTime=30)