algo.hhh.grid {surveillance} | R Documentation |
Tries multiple starting values in algo.hhh
.
Starting values are provided in a matrix with gridSize
rows, 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.
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)
disProgObj |
object of class disProg |
control |
control 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. |
ahg |
Returns an object of class ahg with elements
|
M. Paul, L. Held
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.
meanResponse
,create.grid
,algo.hhh
## 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) # 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)