check_incidence: Fit Bayesian 3-level Poisson model to assess bias of measure...
In dhaine/misclass: Sampling Strategies to Control Misclassification Bias in Longitudinal Udder Health Studies

Description Usage Arguments Value Author(s) Examples

Fit a Bayesian 3-level Poisson model using Stan to evaluate true incidence, quantify bias, and compare relative impact of selection vs. misclassification biases.

1 2	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 `iter` and the default is 100.
`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 `set.seed` to make results reproducible.
`nsimul`	Number of simulations.

An object of class stanfit.

Denis Haine

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)
## End(Not run)