R Under development (unstable) (2025-09-25 r88874 ucrt) -- "Unsuffered Consequences"
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> library("EpiILM")
Loading required package: coda
Loading required package: adaptMCMC
Loading required package: parallel
Loading required package: Matrix
Loading required package: LaplacesDemon
> 
> set.seed(789)
> # generating the XY coordinates of individuals:
> x  <-  runif(256,  0, 100)
> y  <-  runif(256,  0, 100)
> 
> # generating the sus. covariate:
> A <- round(rexp(256, 1/100))
> # simulating an epidemic:
> out_cov <- epidata(type = "SI", n = 256, tmax = 10, x = x, y = y, Sformula = ~A, sus.par = c(0.01, 0.05), beta = 2)
> 
> # performing the MCMC using the epimcmc function:
> t_end <- max(out_cov$inftime)
> unif_range <- matrix(c(0, 0, 1, 1), nrow =  2, ncol =  2)
> 
> mcmcout_M8 <- epimcmc(out_cov, Sformula = ~A,
+     tmax = t_end, niter = 100,
+     sus.par.ini = c(0.03, 0.005), beta.ini = 2,
+     pro.sus.var = c(0.005, 0.005), pro.beta.var = 0.01,
+     prior.sus.dist = c("uniform", "uniform"), prior.sus.par = unif_range,
+     prior.beta.dist = "uniform", prior.beta.par = c(0, 10),
+     adapt = TRUE, acc.rate = 0.5)
  generate 100 samples 
> 
> summary(mcmcout_M8)
Model: SI distance-based discrete-time ILM 
Method: Markov chain Monte Carlo (MCMC) 

Iterations = 1:100
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 100 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

           Mean      SD Naive SE Time-series SE
alpha.1 0.07818 0.06499 0.006499       0.021637
alpha.2 0.05911 0.01923 0.001923       0.003013
beta.1  2.06607 0.07078 0.007078       0.024573

2. Quantiles for each variable:

            2.5%     25%     50%     75%  97.5%
alpha.1 0.002556 0.03000 0.06800 0.09392 0.2445
alpha.2 0.005000 0.05666 0.05878 0.06395 0.1005
beta.1  1.992376 2.02379 2.04600 2.10372 2.3060

> 
> 
> mcmcout_M9 <- epimcmc(out_cov,
+     tmax = t_end, niter = 100, sus.par.ini = 0.01,
+     beta.ini = 2, pro.sus.var = 0.1, pro.beta.var = 0.5,
+     prior.sus.dist = "uniform", prior.sus.par = c(0, 3),
+     prior.beta.dist = "uniform", prior.beta.par = c(0, 10),
+     adapt = TRUE, acc.rate = 0.5)
  generate 100 samples 
> 
> summary(mcmcout_M9)
Model: SI distance-based discrete-time ILM 
Method: Markov chain Monte Carlo (MCMC) 

Iterations = 1:100
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 100 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

         Mean     SD Naive SE Time-series SE
alpha.1 0.711 0.2264  0.02264        0.10137
beta.1  1.620 0.1150  0.01150        0.05539

2. Quantiles for each variable:

          2.5%    25%    50%    75% 97.5%
alpha.1 0.1568 0.5334 0.8332 0.8356 1.056
beta.1  1.4639 1.5656 1.6314 1.6400 1.937

> 
> 
> #set.seed(23456)
> predepi1<-pred.epi(object = out_cov, xx = mcmcout_M8, criterion = "newly infectious", n.samples = 50, tmin = 1, Sformula = ~A)
generate 50 epidemics 
> 
> loglike1 <- epilike(object = out_cov, tmax = t_end, Sformula = ~A, sus.par = c(0.08806, 0.04421), beta = 1.96839)
> loglike2 <- epilike(object = out_cov, tmax = t_end, sus.par = 0.735, beta = 1.554)
> 
> dic1 <- epidic(burnin = 10, niter = 100, LLchain = mcmcout_M8$Loglikelihood, LLpostmean = loglike1)
> dic1
[1] 816.5224
> 
> dic2 <- epidic(burnin = 10, niter = 100, LLchain = mcmcout_M9$Loglikelihood, LLpostmean = loglike2)
> dic2
[1] 1052.309
> 
> proc.time()
   user  system elapsed 
   3.23    0.12    3.34