The simulated data set simul2 considers a situation with clustered
observations and 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. C=50 clusters are built containing
one unit with each of the resulting 16 covariate patterns, i.e. a total of
I=800 units. The regression effects are set to beta = {0.75,0.1,0.1,0,0}
in the Poisson and to alpha = {2.2,-0.3,0,-0.3,0} in the logit model.
Random intercepts in both sub-models are simulated from a normal distribution
with standard deviations \(\theta_\beta\)=0.1 and
\(\theta_\alpha\)=0.3. Additionally to the main study sample,
validation data are available for each covariate pattern and cluster.
For details concerning the simulation setup, see Dvorzak and Wagner (2016).
data(simul2)A data frame with 800 rows and the following 10 variables:
ynumber of observed counts for each covariate pattern in each cluster
Etotal exposure times for each unit
cIDcluster ID for each unit
X.0intercept
X.1, X.2, X.3, X.4binary covariates
vnumber of reported cases for each covariate pattern in each cluster in the validation sample
mnumber of true cases subject to the fallible reporting process (sample size of validation data)