Reproducibility

Now that you've completed (and passed!) some Markov chain diagnostics, you're ready to finalize your RJAGS simulation. To this end, reproducibility is crucial. To obtain reproducible simulation output, you must set the seed of the RJAGS random number generator. This works differently than in base R. Instead of using set.seed(), you will specify a starting seed using inits = list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = ___) when you compile your model.

Cet exercice fait partie du cours

Bayesian Modeling with RJAGS

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Instructions

Run the provided code a few times. Notice that the summary() statistics change each time.
For reproducible results, supply the random number generator inits to jags.model(). Specify a starting seed of 1989.
Run the new code a few times. Notice that the summary() statistics do NOT change!

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# COMPILE the model
sleep_jags <- jags.model(textConnection(sleep_model), data = list(Y = sleep_study$diff_3)) 

# SIMULATE the posterior    
sleep_sim <- coda.samples(model = sleep_jags, variable.names = c("m", "s"), n.iter = 10000)

# Summarize the m and s chains of sleep_sim
summary(sleep_sim)

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