simul1: Simulated data set

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

Description

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).

Usage

data(simul1)

Format

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)

Source

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.

See Also

pogitBvs

airbornemint/pogit documentation built on May 31, 2019, 1:49 a.m.