naive_model_picker

Select a Logisitic Regression Model for a Given Prior

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

naive_model_picker function

<dl><dt>prior</dt>
<dd>A character string specifying the prior distribution for the naive
\(\beta\) parameters. Options are <code>"t"</code>,
<code>"uniform"</code>, <code>"normal"</code>, or <code>"dexp"</code> (double Exponential, or Weibull).</dd></dl>

Arguments

Select a Logisitic Regression Model for a Given Prior — naive_model_picker

<dl>

<dt>prior</dt>
<dd>A character string specifying the prior distribution for the naive
\(\beta\) parameters. Options are <code>"t"</code>,
<code>"uniform"</code>, <code>"normal"</code>, or <code>"dexp"</code> (double Exponential, or Weibull).</dd>

</dl>

naive_model_picker: Select a Logisitic Regression Model for a Given Prior

Description

Usage

Value

Arguments