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.

`dRUMIndMH`

, `IndivdRUMIndMH`

1 2 3 4 | ```
data(simul)
data(simul_binary)
## see dRUMIndMH and IndivdRUMIndMH documentation for examples using
## these data
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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