Description Usage Format Source See Also

The simulated data set `simul1`

considers a situation with four
binary covariates in both sub-models of the Pogit model,
i.e. `X`

= `W`

.
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 `beta = {0.75,0.5,-2,0,0}`

in the
Poisson and to `alpha = {2.2,-1.9,0,0,0}`

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).

1 |

A data frame with 16 rows and the following 9 variables:

`y`

number of observed counts for each covariate pattern

`E`

total exposure time

`X.0`

intercept

`X.1`

,`X.2`

,`X.3`

,`X.4`

binary covariates

`v`

number of reported cases for each covariate pattern in the validation sample

`m`

number of true cases subject to the fallible reporting process (sample size of validation data)

Dvorzak, M. and Wagner, H. (2016). Sparse Bayesian modelling
of underreported count data. *Statistical Modelling*, **16**(1),
24 - 46, http://dx.doi.org/10.1177/1471082x15588398.

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