estimate_multiThresh_MLE

Each study \(i\) has thresholds \(\{c_{ij}\}\), each with an observed proportion
\(p_{ij}^{obs}\). We assume \(\mu_i \sim \mathcal{N}(\mu_0,\tau^2)\) and
\(X_{ij} \sim \mathcal{N}(\mu_i,\sigma^2)\). The log-likelihood integrates out
\(\mu_i\) via Gauss-Hermite quadrature.

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_MLE function

<dl><dt>data_list</dt>
<dd>A list with:<ul>
<li><code>n_i</code>: numeric vector (length I)</li>
<li><code>c_ij</code>: list of length I</li>
<li><code>p_ij_obs</code>: list of length I</li>
</ul></dd>
<dt>gh_points</dt>
<dd>integer; number of Gauss-Hermite points (default 12).</dd></dl>

Arguments

MLE with Numeric Integration (Multiple Thresholds per Study) — estimate_multiThresh_MLE

<dl>

<dt>data_list</dt>
<dd>A list with:<ul>
<li><code>n_i</code>: numeric vector (length I)</li>
<li><code>c_ij</code>: list of length I</li>
<li><code>p_ij_obs</code>: list of length I</li>
</ul></dd>


<dt>gh_points</dt>
<dd>integer; number of Gauss-Hermite points (default 12).</dd>

</dl>

estimate_multiThresh_MLE: MLE with Numeric Integration (Multiple Thresholds per Study)

Description

Usage

Value

Arguments