Model fit based on a two-component epidemic model
Fits a negative binomial model (as described in Held et al. (2006) to an univariate time series of counts.
algo.twins(disProgObj, control=list(burnin=1000, filter=10, sampleSize=2500, noOfHarmonics=1, alpha_xi=10, beta_xi=10, psiRWSigma=0.25,alpha_psi=1, beta_psi=0.1, nu_trend=FALSE, logFile="twins.log"))
disProgObj |
object of class |
control |
control object:
|
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 (2006) paper.
Note also that the function writes three logfiles in the current
working directory getwd(): ‘twins.log’,
‘twins.log.acc’ and ‘twins.log2’.
Thus you need to have write permissions in the current working
directory.
Finally, inspection of the C++ code using valgrind shows some memory leaks when running the old underlying C++ program. As we are unable to fix this impurity at the present time, we have instead put the example code in a 'dontrun' environment. The example code, however, works fine – the measure is thus more aimed at reducing the number of CRAN problems with the package.
Returns an object of class atwins with elements
control |
specified control object |
disProgObj |
specified |
logFile |
contains the returned samples of the parameters ψ, γ_0, γ_1, γ_2, K, ξ_λ λ_1,...,λ_{n}, the predictive distribution and the deviance. |
logFile2 |
contains the sample means of the variables X_t, Y_t, ω_t and the relative frequency of a changepoint at time t for t=1,...,n and the relative frequency of a predicted changepoint at time n+1. |
M. Hofmann and M. Höhle and D. Sabanés Bové
Held, L., Hofmann, M., Höhle, M. and Schmid V. (2006): A two-component model for counts of infectious diseases. Biostatistics, 7, pp. 422–437.
## Not run:
# Load the data used in the Held et al. (2006) paper
data("hepatitisA")
# Fix seed - this is used for the MCMC samplers in twins
set.seed(123)
# Call algorithm and save result (use short chain without filtering for speed)
otwins <- algo.twins(hepatitisA,
control=list(burnin=500, filter=1, sampleSize=1000))
# This shows the entire output (use ask=TRUE for pause between plots)
plot(otwins, ask=FALSE)
# Direct access to MCMC output
hist(otwins$logFile$psi,xlab=expression(psi),main="")
if (require("coda")) {
print(summary(mcmc(otwins$logFile[,c("psi","xipsi","K")])))
}
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.