estimate_singleThresh_probit

For each group $i$, we assume the data follows:
$$ \Pr(Y_i = 1) = \Phi\left( \frac{\mu - c_i}{\sigma} \right) $$
where $c_i$ is a known threshold, and $\Phi$ is the standard normal CDF (the probit link).
The function reconstructs individual binary outcomes based on observed probabilities,
and estimates the parameters using generalized linear modeling with a probit link.

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_singleThresh_probit function

<dl><dt>n_i</dt>
<dd>numeric vector</dd>
<dt>c_i</dt>
<dd>numeric vector</dd>
<dt>p_i_obs</dt>
<dd>numeric vector</dd></dl>

Arguments

GLM probit (Single Threshold per Study) — estimate_singleThresh_probit

<dl>

<dt>n_i</dt>
<dd>numeric vector</dd>


<dt>c_i</dt>
<dd>numeric vector</dd>


<dt>p_i_obs</dt>
<dd>numeric vector</dd>

</dl>

estimate_singleThresh_probit: GLM probit (Single Threshold per Study)

Description

Usage

Value

Arguments