Description Usage Format Source See Also Examples
For testing purposes we constructed the very extreme and unbalanced simulated binomial data set simul
. The pattern of this data set is typical of models for rare events, e.g. rare diseases or financial defaults. Based on a fixed number of dims = 10
covariates consisting of nine binary variables and the intercept, the design matrix X
is built by computing all 2^9 possible 0/1 combinations. The true parameter vector is beta={0.05,2,1.5,-3,-0.01,-1.3,2.9,-2.1,0.5,-0.2}
. For details concerning the simulation of the data set see the paper by Fussl, Fruehwirth-Schnatter and Fruehwirth (2013). To use the data set with the function IndivdRUMIndMH
, binary outcomes are reconstructed from the binomial observations and saved as simul_binary
.
1 2 3 |
The binomial data set simul
consists of 512 binomial observations and the following 12 variables:
yi
number of successes for each covariate pattern
Ni
group size for each covariate pattern
X,X.1,...,X.8
binary covariates
X.9
intercept
Only 490 covariate patterns have a group size Ni > 0
and will be included when using the functions dRUMIndMH
, dRUMAuxMix
and dRUMHAM
.
The binary data set simul_binary
consists of 25803 binary observations and the following 11 variables:
y
binary response variable
X,X.1,...,X.8
binary covariates
X.9
intercept
Agnes Fussl, Sylvia Fruehwirth-Schnatter and Rudolf Fruehwirth (2013), "Efficient MCMC for Binomial Logit Models". ACM Transactions on Modeling and Computer Simulation 23, 1, Article 3, 21 pages.
1 2 3 4 | data(simul)
data(simul_binary)
## see dRUMIndMH and IndivdRUMIndMH documentation for examples using
## these data
|
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