# Set control parameters for epinet MCMC algorithm Sets parameter values that control the MCMC algorithm used by epinet to produce posterior samples. 2.10 ```r MCMCcontrol(nsamp, thinning, extrathinning = FALSE, burnin = 0, seed = floor(runif(1, 0, 2^30)), etapropsd) ``` ## Arguments - `nsamp`: number of iterations to run MCMC algorithm. - `thinning`: thinning interval. - `extrathinning`: set to FALSE unless we want to return inferred values of the exposure / infective times and the transmission tree, in which case it is an integer specifying the extra thinning interval. Defaults to FALSE. - `burnin`: number of burn-in iterations to the run the MCMC algorithm. Defaults to 0. - `seed`: seed for random number generation. Defaults to a random value. - `etapropsd`: standard deviation of proposal distributions for eta parameters. ## Details Auxiliary function that can be used to set parameter values that control the MCMC algorithm used by epinet to produce posterior samples. This function is only used in conjunction with the `epinet` function. nsamp is the number of samples that will be produced for each of the model parameters. thinning is the thinning interval, e.g., to return every $10^{th}$ sample, use thinning = 10. If exposure and / or infective times are being inferred and we wish to return the inferred values of these times (along with the inferred transmission tree), set extrathinning equal to an integer specifying the extra thinning interval for these values. Because returning values for a large number of nodes can be very space-intensive, an extra thinning interval can be given as a multiple of the thinning interval for the other parameters. For example, using thinning = 10 and extrathinning = 20 will return the values of the inferred exposure and infective times and transmission tree every 200 iterations, and the values of the other parameters every 10 iterations. If these inferred values are not desired, set this variable to FALSE. burnin controls the number of burn-in iterations to be run by the MCMC algorithm before samples begin to become recorded. etapropsd is a vector of length $k$, where $k$ is the number of eta (network) parameters in the model, including the intercept. These are tuning parameters for the MCMC algorithm. ## Returns A list with arguments as components. ## See Also `epinet` for generating posterior samples of the parameters, and `priorcontrol` for specifying prior distributions and their hyperparameters. ## References Groendyke, C. and Welch, D. 2018. epinet: An R Package to Analyze Epidemics Spread across Contact Networks, **Journal of Statistical Software**, 83-11 . ## Author(s) Chris Groendyke cgroendyke@gmail.com ## Examples ```r # Simulate an epidemic through a network of 30 set.seed(3) N <- 30 # Build dyadic covariate matrix (X) # Have a single covariate for overall edge density; this is the Erdos-Renyi model nodecov <- matrix(1:N, nrow = N) dcm <- BuildX(nodecov) # Simulate network and then simulate epidemic over network examplenet <- SimulateDyadicLinearERGM(N, dyadiccovmat=dcm, eta=-1.8) exampleepidemic <- SEIR.simulator(examplenet, N = 30, beta = 0.3, ki = 2, thetai = 5, latencydist="gamma") # Set inputs for MCMC algorithm mcmcinput <- MCMCcontrol(nsamp = 5000, thinning = 10, etapropsd = 0.2) priorcontrol <- priorcontrol(bprior = c(0, 1), tiprior = c(1, 3), teprior = c(1, 3), etaprior = c(0, 10), kiprior = c(2, 8), keprior = c(2, 8), priordists = "uniform") # Run MCMC algorithm on this epidemic # Note: Not enough data or iterations for any real inference examplemcmc <- epinet( ~ 1, exampleepidemic, dcm, mcmcinput, priorcontrol) ## Not run: # Note: This may take a few minutes to run. set.seed(1) N <- 50 mycov <- data.frame(id = 1:N, xpos = runif(N), ypos = runif(N)) dyadCov <- BuildX(mycov, binaryCol = list(c(2, 3)),binaryFunc = c("euclidean")) # Build network eta <- c(0,-7) net <- SimulateDyadicLinearERGM(N = N, dyadiccovmat = dyadCov, eta = eta) # Simulate epidemic epi <- SEIR.simulator(M = net, N = N, beta = 1, ki = 3, thetai = 7, ke = 3, latencydist = "gamma") # Run MCMC routine on simulated epidemic mcmcinput <- MCMCcontrol(nsamp = 1000000, thinning = 100, etapropsd = c(1, 1)) priors <- priorcontrol(bprior = c(0, 4), tiprior = c(1, 15), teprior = c(1, 15), etaprior = c(0, 10, 0, 10), kiprior = c(1, 7), keprior = c(1, 7), priordists = "uniform") out <- epinet(~ xpos.ypos.L2Dist, epidata = epi, dyadiccovmat = dyadCov, mcmcinput = mcmcinput, priors = priors) ## End(Not run) ```

MCMCcontrol function