drugDat

<p>Simulated data for a hypothetical drug experiment comparing control versus treatment.</p>

datasets

Implements a Markov Chain Monte Carlo algorithm to approximate
exact conditional inference for logistic regression models. Exact
conditional inference is based on the distribution of the sufficient
statistics for the parameters of interest given the sufficient statistics
for the remaining nuisance parameters. Using model formula notation, users
specify a logistic model and model terms of interest for exact inference.
See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.

David Zamar

elrm

Exact Logistic Regression via MCMC

Jinko Graham

Brad McNeney

drugDat function

<p>Matrix with columns:</p><table><tr><td>
[,1] </td>
<td> sex </td>
<td> numeric </td>
<td> gender (1=male, 0=female) </td>
</tr><tr><td>
[,2] </td>
<td> treatment </td>
<td> numeric </td>
<td> treatment type (1=treatment, 0=control) </td>
</tr><tr><td>
[,3] </td>
<td> recovered </td>
<td> numeric </td>
<td> number of subjects that recovered </td>
</tr></table>

drugDat: Drug Dataset

Description

Usage

Arguments

Format

[,1]	sex	numeric	gender (1=male, 0=female)
[,2]	treatment	numeric	treatment type (1=treatment, 0=control)
[,3]	recovered	numeric	number of subjects that recovered