Fit a Bayesian 3-level Poisson model using Stan to evaluate true incidence, quantify bias, and compare relative impact of selection vs. misclassification biases.
check_incidence(data, iter = 500, warmup = 100, chains = 4, cores, seed = 123, nsimul)
data | Data file. |
---|---|
iter | A positive integer specifying how many iterations for each chain (including warmup). The default is 500. |
warmup | a positive integer specifying number of warmup (aka burnin)
iterations. Warmup samples should not be used for inference. The number of
warmup should not be larger than |
chains | A positive integer specifying number of chains. Defaults to 4. |
cores | Number of cores to use when executing the chains in parallel (up to the number of chains). |
seed | Positive integer. Used by |
nsimul | Number of simulations. |
An object of class stanfit
.
sim_list <- vector("list", 1) set.seed(123) sim_list <- replicate(n = 1, expr = make_data(100, 30, "saureus"), simplify = FALSE)# NOT RUN { check_incidence(sim_list, iter = 200, warmup = 25, chains = 1, cores = 1, seed = 123, nsimul = 1) # }