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.

Deze oefening maakt deel uit van de cursus

Bayesian Modeling with RJAGS

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Oefeninstructies

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!

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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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