estimate_multiThresh_MCMC

Builds an inline Stan model for multiple thresholds per study. The user must have
the <code>rstan</code> package installed. We place random effects \(\mu_i = \mu_0 + \tau * mu\_raw[i]\)
and use a binomial likelihood for each threshold. By default, uses simple weakly
informative priors.

Provides likelihood-based and hierarchical estimation methods
for thresholded (binomial-probit) data. Supports fixed-mean and random-mean
models with maximum likelihood estimation (MLE), generalized linear mixed model
(GLMM), and Bayesian Markov chain Monte Carlo (MCMC) implementations.
For methodological background, see Albert and Chib (1993)
<doi:10.1080/01621459.1993.10476321> and McCulloch (1994)
<doi:10.2307/2297959>.

Zhaoze Liu

bin2norm

Hierarchical Probit Estimation for Dichotomized Data

estimate_multiThresh_MCMC function

<dl><dt>data_list</dt>
<dd>same structure as above: <code>n_i</code>, <code>c_ij</code>, <code>p_ij_obs</code></dd>
<dt>iter</dt>
<dd>number of total iterations for each chain (default 2000)</dd>
<dt>chains</dt>
<dd>number of MCMC chains (default 2)</dd></dl>

Arguments

Bayesian MCMC (Multiple Thresholds per Study) using rstan — estimate_multiThresh_MCMC

<dl>

<dt>data_list</dt>
<dd>same structure as above: <code>n_i</code>, <code>c_ij</code>, <code>p_ij_obs</code></dd>


<dt>iter</dt>
<dd>number of total iterations for each chain (default 2000)</dd>


<dt>chains</dt>
<dd>number of MCMC chains (default 2)</dd>

</dl>

estimate_multiThresh_MCMC: Bayesian MCMC (Multiple Thresholds per Study) using rstan

Description

Usage

Value

Arguments