simul2: Simulated data set

In pogit: Bayesian Variable Selection for a Poisson-Logistic Model

Simulated data set

Description

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 θ_β=0.1 and θ_α=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).

Usage

data(simul2)

Format

A data frame with 800 rows and the following 10 variables:

y

number of observed counts for each covariate pattern in each cluster

E

total exposure times for each unit

cID

cluster ID for each unit

X.0

intercept

X.1, X.2, X.3, X.4

binary covariates

v

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

m

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

Source

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

See Also

pogitBvs

pogit documentation built on May 25, 2022, 5:05 p.m.