q_beta_f

Objective function of the form:
\( Q_\beta = \sum_{i = 1}^N \Bigl[ \sum_{j = 0}^1 w_{ij} \text{log} \{ \pi_{ij} \}\Bigr]\).
Used to obtain estimates of \(\beta\) parameters.

Use frequentist and Bayesian methods to estimate parameters from a
binary outcome misclassification model. These methods correct for the problem
of "label switching" by assuming that the sum of outcome sensitivity and
specificity is at least 1. A description of the analysis methods is
available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

Kimberly Hochstedler

COMBO

Correcting Misclassified Binary Outcomes in Association Studies

Kimberly Hochstedler Webb

q_beta_f function

<dl><dt>beta</dt>
<dd>A numeric vector of regression parameters for the
<code>Y</code> (true outcome) ~ <code>X</code> (predictor matrix of interest).</dd>
<dt>X</dt>
<dd>A numeric design matrix.</dd>
<dt>w_mat</dt>
<dd>Matrix of E-step weights obtained from <code>w_j</code>.</dd>
<dt>sample_size</dt>
<dd>An integer value specifying the number of observations in the sample.
This value should be equal to the number of rows of the design matrix, <code>X</code>.</dd>
<dt>n_cat</dt>
<dd>The number of categorical values that the true outcome, <code>Y</code>,
can take.</dd></dl>

Arguments

M-Step Expected Log-Likelihood with respect to Beta — q_beta_f

<dl>

<dt>beta</dt>
<dd>A numeric vector of regression parameters for the
<code>Y</code> (true outcome) ~ <code>X</code> (predictor matrix of interest).</dd>


<dt>X</dt>
<dd>A numeric design matrix.</dd>


<dt>w_mat</dt>
<dd>Matrix of E-step weights obtained from <code>w_j</code>.</dd>


<dt>sample_size</dt>
<dd>An integer value specifying the number of observations in the sample.
This value should be equal to the number of rows of the design matrix, <code>X</code>.</dd>


<dt>n_cat</dt>
<dd>The number of categorical values that the true outcome, <code>Y</code>,
can take.</dd>

</dl>

q_beta_f: M-Step Expected Log-Likelihood with respect to Beta

Description

Usage

Value

Arguments