simul: Toy multilevel categorical dataset (simul)

In davidevdt/BMLCimpute: BMLCimpute: Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data

Description

The dataset contains 1000 level-1 artificial observations grouped into 200 level-2 groups. The dataset consists of: one identifier for the group, one identifier for the level-1 units, six level-1 binary variables (5 predictors and 1 response), five level-2 binary variables. The dataset was generated following the simulation study settings in Vidotto, Vermunt, van Deun (2018). The dataset can be used for performance evaluation of multilevel imputation methods applied on the simul_incomplete dataset.

Usage

data(simul)

Format

A data frame with 13 columns and 1,000 rows (level-1 units) from 200 groups (level-2 units):

[,1]	GroupID	numeric	Level-2 unit Identifier
[,2]	UnitID	numeric	Level-1 unit Identifier
[,3]	X1,...,X5	binary	Level-1 predictors
[,4]	Z1,...,Z5	binary	Level-2 predictors
[,5]	Y	binary	Response Variable

References

• [1] Vidotto D., Vermunt J.K., Van Deun K. (2018). 'Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data'. Journal of Educational and Beahvioral Statistics 43(5), 511-539.

Examples

## Not run: 

library(BMLCimpute)

data(simul)


## End(Not run)

davidevdt/BMLCimpute documentation built on June 5, 2019, 12:36 a.m.