estimate_singleThresh_MLE

Treats the count of "above threshold" in study \(i\) as binomial with probability
\(1 - \Phi((c_i - \mu)/\sigma)\). This uses numerical optimization (<code>optim</code>)
to maximize the binomial likelihood. Optionally uses Weighted OLS estimates as starting
values to improve convergence.

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_MLE function

<dl><dt>n_i</dt>
<dd>numeric vector of sample sizes</dd>
<dt>c_i</dt>
<dd>numeric vector of thresholds</dd>
<dt>p_i_obs</dt>
<dd>numeric vector of observed proportions above threshold</dd>
<dt>use_wols_init</dt>
<dd>logical; if <code>TRUE</code>, uses Weighted OLS estimates
(<code>estimate_singleThresh_WOLS</code>) as initial values in <code>optim</code>.</dd></dl>

Arguments

MLE (Single Threshold per Study) — estimate_singleThresh_MLE

<dl>

<dt>n_i</dt>
<dd>numeric vector of sample sizes</dd>


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


<dt>p_i_obs</dt>
<dd>numeric vector of observed proportions above threshold</dd>


<dt>use_wols_init</dt>
<dd>logical; if <code>TRUE</code>, uses Weighted OLS estimates
(<code>estimate_singleThresh_WOLS</code>) as initial values in <code>optim</code>.</dd>

</dl>

estimate_singleThresh_MLE: MLE (Single Threshold per Study)

Description

Usage

Value

Arguments