## Load the library library(kilde) ## Get some data df <- sample_data() ## This dataset contains 4 different countries, we'll pick Canada: df <- df[df$country == "Canada",] ## We need to format the data to the form accepted by the model function. ob <- dataformatting_ST(DATA = df, UM = 2) ## Initialize the mcmc data objects. Note: 100 iterations is too few. result <- initialize_mcmc_ST(ob$ns, ob$STu, MCMC = 100, ob$Nisolates) ## Run the MCMC for the ST model in R mcmc_ob <- runmcmc_ST(result = result, ob = ob, h = 0, FULL = 0) ## plot the history of the MCMC plot_history(mcmc_ob, 50) ## plot the modelfit plot_modelfit(mcmc_ob, 50) ## A summary of the model fit summary_kilde(mcmc_ob, 50) ## The sample attribution plot_sample_attribution(mcmc_ob, 50) ## a plot of the population attribution plot_population_attribution(mcmc_ob, 50) ## ## ## We can run the same ST model in BUGS ## ## Then the same model in BUGS ## Initialize the MCMC objects initial_result <- initialize_bugs_ST(ob) ## Run the model, this time with 1000 iterations, because bugs will ## throw an error if we try to run too few. ## result_bugs <- run_bugs(result = initial_result, ## ob = ob, ## MCMC = 1000, ## n.burnin = 100, ## FULL = 0, ## model = "SA_ST_model.jag", ## n.chains = 1) ## plot_history(result_bugs, 100) ## plot_modelfit(result_bugs, 100) ## summary_kilde(result_bugs, 100) ## plot_sample_attribution(result_bugs, 100) ## plot_population_attribution(result_bugs, 100)
library(kilde) ## Read in a format data df <- sample_data() df <- df[df$country == "Canada",] ob <- dataformatting(DATA = df, UM = 2) ###################################### ## Initialize and then run mcmc in R ###################################### result <- initialize_mcmc(ns = ob$inits$ns, nat = ob$inits$nat, MCMC = 100, Nisolates = ob$inits$Nisolates) mcmc_ob <- runmcmc(result, ob, MCMC = 100, h = 0, FULL = 0) ## Plot the results of this model. plot_history(mcmc_ob, 50) plot_modelfit(mcmc_ob, 50) summary_kilde(mcmc_ob, 50) plot_sample_attribution(mcmc_ob, 50) plot_population_attribution(mcmc_ob, 50) ## ################################################ ## Initialize and then run the model in OpenBugs ################################################ ## ## Below, BUGS model cannot handle a large number of MCMC iterations ## for all parameters. Therefore, it is advisable to try with smaller ## number of iterations to start, perhaps 1000. ## ## initial_result <- initialize_bugs(ob) ## result_bugs <- run_bugs(result = initial_result, ## ob = ob, ## MCMC = 1000, ## n.burnin = 100, ## FULL = 0) ## plot_history(result_bugs, 100) ## plot_modelfit(result_bugs, 100) ## summary_kilde(result_bugs, 100) ## plot_sample_attribution(result_bugs, 100) ## plot_population_attribution(result_bugs, 100)
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