crashDat

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

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

crashDat function

Matrix with columns:
<table><tr><td>
[,1] </td>
<td> age </td>
<td> numeric </td>
</tr><tr><td> designed age of the crash dummy  </td>
<td>
[,2] </td>
<td> vel </td>
</tr><tr><td> numeric </td>
<td> velocity of the car at impact </td>
<td>
[,3] </td>
</tr><tr><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>
</tr><tr><td> outcome from test (1=fatal, 0=non-fatal) </td>
<td>
[,5] </td>
<td> n </td>
</tr><tr><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>
<td>
[,1] </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.	[,1]