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.
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