Description Usage Arguments Details Value References See Also Examples
Runs an MCMC algorithm for the estimation of specified model parameters
1 2 3 4 5 6 7 8 9 10 11 12 13 | epimcmc (object, tmin = NULL, tmax,
niter, sus.par.ini, trans.par.ini = NULL, beta.ini = NULL, spark.ini = NULL,
Sformula = NULL, Tformula = NULL,
pro.sus.var, pro.trans.var = NULL, pro.beta.var = NULL, pro.spark.var = NULL,
prior.sus.dist, prior.trans.dist = NULL, prior.beta.dist = NULL,
prior.spark.dist = NULL, prior.sus.par, prior.trans.par, prior.beta.par = NULL,
prior.spark.par = NULL, adapt = FALSE, acc.rate = NULL)
|
object |
An object of class |
tmin |
The first time point at which the infection occurs, default value is one. |
tmax |
The last time point at which data is observed. |
niter |
Number of MCMC iterations. |
sus.par.ini |
Initial value(s) of the susceptibility parameter(s) (>0). |
trans.par.ini |
Initial value(s) of the transmissibility parameter(s) (>0). |
beta.ini |
Initial value(s) of the spatial parameter(s) (>0) or the network parameter(s) (>0) if contact network is used. |
spark.ini |
Initial value of the spark parameter (>=0). |
Sformula |
An object of class formula. See formula Individual-level covariate information associated with susceptibility can be passed through this argument. An expression of the form |
Tformula |
An object of class formula. See formula Individual-level covariate information associated with transmissibility can be passed through this argument. An expression of the form |
pro.sus.var |
Proposal density variance(s) for susceptibility parameter(s). If a zero value is assigned to the proposal variance of any parameter, the parameter is considered fixed to its |
pro.trans.var |
Proposal density variance(s) for transmissibility parameter(s). If a zero value is assigned to the proposal variance of any parameter, the parameter is considered fixed to its |
pro.beta.var |
Proposal density variance(s) for beta parameter(s). If a zero value is assigned to the proposal variance of any parameter, the parameter is considered fixed to its |
pro.spark.var |
Proposal density variance for the spark parameter. |
prior.sus.dist |
Select the prior distribution(s) for the susceptibility parameter(s) with the choice of "halfnormal" for positive half normal distribution, "gamma" for gamma distribution and "uniform" for uniform distribution |
prior.trans.dist |
Select the prior distribution(s) for the transmissibility parameter(s) with the choice of "halfnormal" for positive half normal distribution, "gamma" for gamma distribution and "uniform" for uniform distribution |
prior.beta.dist |
Select the prior distribution(s) for the beta parameter(s) with the choice of "halfnormal" for half normal distribution, "gamma" for gamma distribution and "uniform" for uniform distribution |
prior.spark.dist |
Select the prior distribution for the spark parameter with the choice of "halfnormal" for half normal distribution, "gamma" for gamma distribution and "uniform" for uniform distribution |
prior.sus.par |
A vector (matrix) of the prior distribution parameters for updating the susceptibility parameter(s). |
prior.trans.par |
A vector (matrix) of the prior distribution parameters for updating the transmissibility parameter(s). |
prior.beta.par |
A vector (matrix) of the prior distribution parameters for updating the kernel parameter(s). |
prior.spark.par |
A vector of the prior distribution parameters for updating the spark parameter. |
adapt |
To enable the adaptive MCMC method in the |
acc.rate |
To set an acceptance rate. This option will be ignored if |
Independent Gaussian random walks are used as the Metropolis-Hastings MCMC proposal for all parameters. The epimcmc
function depends on the MCMC
function from the adaptMCMC package.
Returns an object of class epimcmc
that contains:
the compartmental framework model used in the analysis.
the used kernel.type
in the function (distance-based or network-based).
the MCMC output of the updated model parameters.
the loglikelihood of the updated model parameters.
the MCMC output of all the model parameters (including fixed parameters).
the number of parameters in the susceptibility function.
the number of parameters in the transmissibility function.
the number of parameters in the kernel function.
Rob Deardon, Xuan Fang, and Grace P. S. Kwong (2015). Statistical modelling of spatio-temporal infectious disease tranmission in Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases, (Ed: D. Chen, B. Moulin, J. Wu), John Wiley & Sons.. Chapter 11.
summary.epimcmc
, plot.epimcmc
, epidata
, epilike
, pred.epi
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ## Example 1: spatial SI model
# generate 100 individuals
x <- runif(100, 0, 10)
y <- runif(100, 0, 10)
covariate <- runif(100, 0, 2)
out1 <- epidata(type = "SI", n = 100, Sformula = ~covariate, tmax = 15,
sus.par = c(0.1, 0.3), beta = 5.0, x = x, y = y)
alphapar1 <- matrix(c(1, 1, 1, 1), ncol = 2, nrow = 2)
betapar1 <- c(10, 2)
epi <- epimcmc(object = out1, tmin = 1, tmax = 15,
niter = 1000, sus.par.ini = c(1, 1), beta.ini = 1,
Sformula = ~covariate, pro.sus.var = c(0.5, 0.3), pro.beta.var = 0.1,
prior.sus.dist = c("gamma", "gamma"), prior.beta.dist = "gamma",
prior.sus.par = alphapar1, prior.beta.par = betapar1,
adapt = TRUE, acc.rate = 0.5)
epi
## Example 2: spatial SIR model
lambda <- rep(3, 100)
out2 <- epidata(type = "SIR", n = 100, tmax = 15, sus.par = 0.3, beta = 5.0, infperiod = lambda,
x = x, y = y)
alphapar2 <- c(1, 1)
betapar2 <- c(1, 1)
epi2 <- epimcmc(object = out2, tmin = 1, tmax = 15,
niter = 1000, sus.par.ini = 1, beta.ini = 1,
Sformula = NULL, pro.sus.var = 0.3, pro.beta.var = 0.1,
prior.sus.dist = "gamma", prior.beta.dist = "gamma",
prior.sus.par = alphapar2, prior.beta.par = betapar2,
adapt = FALSE, acc.rate = NULL)
epi2
|
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