crashDat

<p>Data from 58 simulated car crashes were analyzed. The relationship between the crash outcome (fatal, non-fatal) and 3 covariates was modeled.</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

crashDat function

<p>Matrix with columns:</p><table><tr><td>
[,1] </td>
<td> age </td>
<td> numeric </td>
<td> designed age of the crash dummy  </td>
</tr><tr><td>
[,2] </td>
<td> vel </td>
<td> numeric </td>
<td> velocity of the car at impact </td>
</tr><tr><td>
[,3] </td>
<td> acl </td>
<td> numeric </td>
<td> acceleration of the car at impact </td>
</tr><tr><td>
[,4] </td>
<td> y </td>
<td> numeric </td>
<td> outcome from test (1=fatal, 0=non-fatal) </td>
</tr><tr><td>
[,5] </td>
<td> n </td>
<td> numeric </td>
<td> number of binomial trials. For this data, n is a vector of ones, thus each trial is modeled as a Bernoulli random variable. </td>
</tr></table>

crashDat: Crash Dataset: Calibration of Crash Dummies in Automobile Safety Tests

Description

Usage

Arguments

Format

[,1]	age	numeric	designed age of the crash dummy
[,2]	vel	numeric	velocity of the car at impact
[,3]	acl	numeric	acceleration of the car at impact
[,4]	y	numeric	outcome from test (1=fatal, 0=non-fatal)
[,5]	n	numeric	number of binomial trials. For this data, n is a vector of ones, thus each trial is modeled as a Bernoulli random variable.