simul1

The simulated data set <code>simul1</code> considers a situation with four
binary covariates in both sub-models of the Pogit model, 
i.e. <code>X</code> = <code>W</code>.
The respective design matrix is built by computing all 2^4 possible 0/1 
combinations and one observation is generated for each covariate pattern. 
The regression effects are set to <code>beta = {0.75,0.5,-2,0,0}</code> in the 
Poisson and to <code>alpha = {2.2,-1.9,0,0,0}</code> in the logit model.
Additionally to the main study sample, validation data are available for
each covariate pattern. For details concerning the simulation setup, see 
Dvorzak and Wagner (2016).

datasets

Bayesian variable selection for regression models of under-reported
count data as well as for (overdispersed) Poisson, negative binomal and
binomial logit regression models using spike and slab priors.

Michaela Dvorzak

pogit

Bayesian Variable Selection for a Poisson-Logistic Model

Helga Wagner

simul1 function

A data frame with 16 rows and the following 9 variables:<dl class="dl-horizontal">
 <dt><code>y</code></dt><dd>number of observed counts for each covariate pattern</dd>
 <dt><code>E</code></dt><dd>total exposure time</dd>
 <dt><code>X.0</code></dt><dd>intercept</dd>
 <dt><code>X.1</code>, <code>X.2</code>, <code>X.3</code>, <code>X.4</code></dt><dd>binary covariates</dd>
 <dt><code>v</code></dt><dd>number of reported cases for each covariate pattern in 
 the validation sample</dd>
 <dt><code>m</code></dt><dd>number of true cases subject to the fallible reporting
 process (sample size of validation data)</dd>
</dl>

Format

Simulated data set — simul1

A data frame with 16 rows and the following 9 variables:<dl class='dl-horizontal'>
 <dt><code>y</code></dt><dd>number of observed counts for each covariate pattern</dd>
 <dt><code>E</code></dt><dd>total exposure time</dd>
 <dt><code>X.0</code></dt><dd>intercept</dd>
 <dt><code>X.1</code>, <code>X.2</code>, <code>X.3</code>, <code>X.4</code></dt><dd>binary covariates</dd>
 <dt><code>v</code></dt><dd>number of reported cases for each covariate pattern in 
 the validation sample</dd>
 <dt><code>m</code></dt><dd>number of true cases subject to the fallible reporting
 process (sample size of validation data)</dd>
</dl>

simul1: Simulated data set

Description

Usage

Arguments

Format

See Also