sample_incidence: Fit Bayesian 3-level Poisson model to evaluate sampling...

In dhaine/misclass: Sampling Strategies to Control Misclassification Bias in Longitudinal Udder Health Studies

Fit Bayesian 3-level Poisson model to evaluate sampling strategies to correct bias of incidence evaluation.

Description

Fit a Bayesian 3-level Poisson model using Stan to evaluate effect of various sampling strategies on biases when looking at incidence.

Usage

sample_incidence(data, iter = 500, warmup = 100, chains = 4, cores,
  seed = 123, nsimul)

Arguments

data	Data file.
iter	A positive integer specifying how many iterations for each chain (including warmup). 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.

Value

An object of class stanfit.

Author(s)

Denis Haine

Examples

sim_list <- vector("list", 1)
set.seed(123)
sim_list <- replicate(n = 1, expr = make_data(100, 30, "saureus"), simplify = FALSE)
## Not run: sample_incidence(sim_list,
                          iter = 200,
                          warmup = 25,
                          chains = 1,
                          cores = 1,
                          seed = 123,
                          nsimul = 1)
## End(Not run)

dhaine/misclass documentation built on June 12, 2025, 2:37 a.m.