mean_pistarjj_compute

Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects

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

mean_pistarjj_compute function

<dl><dt>pistar_matrix</dt>
<dd>A numeric matrix of conditional probabilities obtained from
the internal function <code>pistar_compute_for_chains</code>. Rows of the matrix correspond to
each subject and to each observed outcome category. Columns of the matrix
correspond to each true, latent outcome category.</dd>
<dt>j</dt>
<dd>An integer value representing the true outcome category to compute
the average conditional probability of correct classification for.
<code>j</code> can take on values <code>1</code> and <code>2</code>.</dd>
<dt>sample_size</dt>
<dd>An integer value specifying the number of observations in the sample.</dd></dl>

Arguments

Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects — mean_pistarjj_compute

<dl>

<dt>pistar_matrix</dt>
<dd>A numeric matrix of conditional probabilities obtained from
the internal function <code>pistar_compute_for_chains</code>. Rows of the matrix correspond to
each subject and to each observed outcome category. Columns of the matrix
correspond to each true, latent outcome category.</dd>


<dt>j</dt>
<dd>An integer value representing the true outcome category to compute
the average conditional probability of correct classification for.
<code>j</code> can take on values <code>1</code> and <code>2</code>.</dd>


<dt>sample_size</dt>
<dd>An integer value specifying the number of observations in the sample.</dd>

</dl>

mean_pistarjj_compute: Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects

Description

Usage

Value

Arguments