BMLCimpute: BMLCimpute : Bayesian Multilevel Latent Class Models for the...

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

Description

A package for the multiple imputation of single-level and nested categorical data by means of Bayesian Multilevel Latent Class models.

Details

'BMLCimpute' allows researchers and users of categorical datasets with missing data to perform Multiple Imputation via Bayesian latent class models. Data can be either single- or multi-level. Model estimation and imputations are implemented via a Gibbs sampler run with the Rcpp package interface. The function multilevelLCMI performs the imputations. Prior to the imputation step, data should be processed with the function convData; the resulting list is then passed as input to the multilevelLCMI. Complete datasets are obtained via the compData function.

Functions

• multilevelLCMI for the imputations and model estimation (internally calls Rcpp code);

• convData for data preparation (preprocessing);

• compData for dataset completion.

Author(s)

D. Vidotto <[e-mail]>

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)

# Load data 
data(simul_incomplete)

# Preprocess the Data
cd <- convData(simul_incomplete, GID = 1, UID = 2, var2 = 8:12)

# Model Selection
set.seed(1)
mmLC <- multilevelLCMI( convData = cd, L = 10, K = 10, it1 = 1000, it2 = 3000, it3 = 100,
    it.print = 250, v = 10, I = 0, pri2 = 1 / 10, pri1 = 1 / 15, priresp =  0.01,
    priresp2 = 0.01, random = TRUE, estimates = FALSE, count = TRUE, plot.loglik = FALSE,
    prec = 3, scale = 1.0)

# Select posterior maxima of the number of classes for the imputations 
# (Other alternatives are possible, such as posterior modes or posterior quantiles)
L = max(which(mmLC[[12]] != 0))
K = max(apply(mmLC[[13]], 1, function(x) max(which( x != 0))), na.rm = TRUE)

# Perform 5 imutations on the dataset 
mmLC <- multilevelLCMI( convData = cd, L = L, K = K, it1 = 2000, it2 = 4000, it3 = 100, 
     it.print = 250, v = 10, I = 5, pri2 = 500, pri1 = 50, priresp = 0.01, priresp2 = 0.01,
     random = TRUE, estimates = FALSE, count = TRUE, plot.loglik = TRUE, prec = 4, scale = 1.0)

# Obtain the dataset completed with the first set of imputations (ind = 1)
complete_data = compData( convData = cd, implev1 = mmLC[[1]], implev2 = mmLC[[2]], ind = 1 )


## End(Not run) 

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