Rcpp_bei-class: Class '"Rcpp_bei"'
In EditImputeCont: Simultaneous Edit-Imputation for Continuous Microdata

Description Details Extends Fields Methods References Examples

This class implements the MCMC sampler for a joint modeling approach to multiple edit-imputation for continuous data. It provides methods for updating and monitoring the sampler.

Rcpp_bei objects should be created with createModel. Please see the example in the demo folder for more detailed explanation.

Class "C++Object", directly.

Y.input: input dataset generated from readData (replacing NA in Y.original by -999 and zero values by 0.01).
Y.edited: current edit-imputed dataset.
K: number of mixture components (latent classes).
n.occ: effective number of mixture components.
Prob.A: ratio of the size of the observed sample to the size of the augmented sample.
RandomSeed: random seed.
msg.level: integer in {0,1,2} specifying the level of displayed message; 0: errors only, 1: errors and warnings, 2: all messages. Defaults to 0.
FaultyRecordID: record IDs of Y.orig whose values violate edit rules.

Iterate(): run a single iteration of MCMC.
Run(iter): run iter iterations of MCMC.

Hang J. Kim, Lawrence H. Cox, Alan F. Karr, Jerome P. Reiter and Quanli Wang (2015). "Simultaneous Edit-Imputation for Continuous Microdata", Journal of the American Statistical Association, DOI: 10.1080/01621459.2015.1040881.

data(SimpleEx)

## read the data
data1 = readData(SimpleEx$D.obs, SimpleEx$Ratio.edit, NULL, 
  SimpleEx$Balance.edit)

## create and initialize the model
# model1 = createModel(data1, K=15)

### run 10 iterations
# model1$Run(10)
# EI_data1 = model1$Y.edited    # store the edit-imputed dataset