drugDat

Simulated data for a hypothetical drug experiment comparing control versus treatment.

datasets

elrm 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.

David Zamar

elrm

Exact Logistic Regression via MCMC

drugDat function

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