estimate_multiThresh_GLMM

Creates a single data frame stacking all thresholds from all studies, then
calls <code>lme4::glmer(..., family=binomial(link='probit'))</code> to fit a random-intercept
model:
$$k_{ij} \sim \mathrm{Binomial}\bigl(n_i, \Phi(\alpha_i + \beta\, c_{ij})\bigr),$$
with $\alpha_i \sim \mathcal{N}(0, \sigma_\alpha^2)$.
Interpreting results: $\sigma = 1/|\,\beta\,|$,
$\tau^2 = \sigma^2 \times \sigma_\alpha^2$,
$\mu_0 = (\mathrm{Intercept}) \times \sigma$ (if not forced to 0).

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_GLMM function

<dl><dt>data_list</dt>
<dd>same structure: <code>n_i</code>, <code>c_ij</code>, <code>p_ij_obs</code></dd>
<dt>use_lme4</dt>
<dd>logical; if <code>TRUE</code>, calls <code><a href="/link/lme4%3A%3Aglmer?package=bin2norm&version=0.1.0" data-mini-rdoc="bin2norm::lme4::glmer">lme4::glmer</a></code> with a probit link.</dd></dl>

Arguments

GLMM (Multiple Thresholds per Study, Probit Link, Random Intercepts) — estimate_multiThresh_GLMM

<dl>

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


<dt>use_lme4</dt>
<dd>logical; if <code>TRUE</code>, calls <code><a href='https://rdrr.io/pkg/lme4/man/glmer.html'>lme4::glmer</a></code> with a probit link.</dd>

</dl>

estimate_multiThresh_GLMM: GLMM (Multiple Thresholds per Study, Probit Link, Random Intercepts)

Description

Usage

Value

Arguments