sample_stgy: Fit Bayesian 3-level logistic model to evaluate sampling...

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

Description

Fit a Bayesian 3-level logistic model using Stan to evaluate effect of various sampling strategies on biases of measure of association.

Usage

sample_stgy(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_stgy(sim_list,
                     iter = 200,
                     warmup = 25,
                     chains = 1,
                     cores = 1,
                     seed = 123,
                     nsimul = 1)
## End(Not run)

dhaine/misclass documentation built on May 15, 2019, 8:21 a.m.