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Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box

Yu-Sung Su; Masanao Yajima; Andrew E. Gelman; Jennifer Hill

Title:
Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box
Author(s):
Su, Yu-Sung
Yajima, Masanao
Gelman, Andrew E.
Hill, Jennifer
Date:
Type:
Articles
Department:
Statistics
Volume:
45
Permanent URL:
Book/Journal Title:
Journal of Statistical Software
Abstract:
Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional distributions used for imputation; and plots for comparing the distributions of observed and imputed data. In addition, we use Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models. Our goal is to have a demonstration package that (a) avoids many of the practical problems that arise with existing multivariate imputation programs, and (b) demonstrates state-of-the-art diagnostics that can be applied more generally and can be incorporated into the software of others.
Subject(s):
Statistics
Item views:
356
Metadata:
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