algo.twins {surveillance} | R Documentation |
Fit a Two-Component Epidemic Model using MCMC
Description
Fits a negative binomial model as described in Held et al. (2006)
to an univariate time series of counts.
This is an experimental implementation that may be removed
in future versions of the package.
Usage
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"))
Arguments
disProgObj |
object of class disProg
|
control |
control object:
burnin Number of burn in samples.
filter Thinning parameter. If filter = 10 every 10th sample is after the burn in is returned.
sampleSize Number of returned samples. Total
number of samples = burnin +filter *sampleSize
noOfHarmonics Number of harmonics to use in the
modelling, i.e. L in (2.2) of Held et al (2006).
alpha_xi Parameter αξ of the hyperprior of the epidemic parameter λ
beta_xi Parameter βξ of the hyperprior of the epidemic parameter λ
psiRWSigma Starting value for the tuning of the variance of the random walk proposal for the overdispersion parameter ψ .
alpha_psi Parameter αψ of the prior of the overdispersion parameter ψ
beta_psi Parameter βψ
of the prior of the overdispersion parameter ψ
nu_trend Adjust for a linear trend in the endemic
part? (default: FALSE )
logFile Base file name for the output files. The function writes three output files in the current working directory getwd() . If logfile = "twins.log" the results are stored in the three files ‘twins.log’, ‘twins.log2’ and ‘twins.log.acc’.
‘twins.log’ contains the returned samples of the parameters ψ , γ0 , γ1 , γ2 , K, ξλ λ1,...,λn , the predictive distribution of the number of cases at time n+1 and the deviance.
‘twins.log2’ contains the sample means of the variables Xt,Yt,ωt 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.
‘twins.log.acc’ contains the acceptance rates of ψ , the changepoints and the endemic parameters γ0 , γ1 , γ2 in the third column and the variance of the random walk proposal for the update of the parameter ψ in the second column.
|
Value
Returns an object of class atwins
with elements
control |
specified control object
|
disProgObj |
specified disProg -object
|
logFile |
contains the returned samples of the parameters ψ , γ0 , γ1 , γ2 , K, ξλ λ1,...,λn , the predictive distribution and the deviance.
|
logFile2 |
contains the sample means of the variables Xt,Yt,ωt 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.
|
Note
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.
Author(s)
M. Hofmann and M. Höhle and
D. Sabanés Bové
References
Held, L., Hofmann, M., Höhle, M. and Schmid V. (2006):
A two-component model for counts of infectious diseases.
Biostatistics, 7, pp. 422–437.
Examples
# 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)
oldwd <- setwd(tempdir()) # where logfiles will be written
otwins <- algo.twins(hepatitisA,
control=list(burnin=500, filter=1, sampleSize=1000))
setwd(oldwd)
# 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")])))
}
[Package
surveillance version 1.23.0
Index]