miceadds-package.RdContains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
The miceadds package contains some functionality for imputation of multilevel
data. The function mice.impute.ml.lmer is a general function for
imputing multilevel data with hierarchical or cross-classified structures for
variables at an arbitrary level. This imputation method uses the lme4::lmer function
in the lme4 package. The imputation method mice.impute.2lonly.function
conducts an imputation for a variable at a higher level for already defined imputation
methods in the mice package. Two-level imputation is available in several functions
in the mice package (mice::mice.impute.2l.pan,
mice::mice.impute.2l.norm)
as well in micemd and hmi packages. The miceadds package contains
additional imputation methods for two-level datasets:
mice.impute.2l.continuous for normally distributed data,
mice.impute.2l.pmm for predictive mean matching in multilevel models
and mice.impute.2l.binary for binary data.
In addition to the usual mice imputation function which employs
parallel chains, the function mice.1chain does multiple
imputation from a single chain.
Nested multiple imputation can be conducted with
mice.nmi. The function NMIcombine conducts
statistical inference for nested multiply imputed datasets.
Imputation based on partial least squares regression is implemented
in mice.impute.pls.
Unidimensional plausible value imputation for latent variables (or
variables with measurement error) in the mice sequential imputation
framework can be applied by using the method
mice.impute.plausible.values.
Substantive model compatible multiple imputation using fully conditional
specification can be conducted with mice.impute.smcfcs.
The function syn_mice allows the generation of
synthetic datasets with imputation methods for mice. It has
similar functionality as the synthpop package (Nowok, Raab, & Dibben, 2016).
The function mice.impute.synthpop allows the usage of
synthpop synthesization methods in mice, while
syn.mice allows the usage of
mice imputation methods in synthpop.
The method mice.impute.simputation is a wrapper function
to imputation methods in the simputation package.
The methods mice.impute.imputeR.lmFun and
mice.impute.imputeR.cFun are wrapper functions
to imputation methods in the imputeR package.
The miceadds package also includes some functions R utility functions
(e.g. write.pspp, ma.scale2).
Imputations for questionnaire items can be
accomplished by two-way imputation (tw.imputation).
Bartlett, J. W., Seaman, S. R., White, I. R., Carpenter, J. R., & Alzheimer's Disease Neuroimaging Initiative (2015). Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model. Statistical Methods in Medical Research, 24(4), 462-487. doi:10.1177/0962280214521348
Grund, S., Luedtke, O., & Robitzsch, A. (2018). Multiple imputation of multilevel data in organizational research. Organizational Research Methods, 21(1), 111-149. doi:10.1177/1094428117703686
Mislevy, R. J. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56(2), 177-196. doi:10.1007/BF02294457
Nowok, B., Raab, G., & Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11
Reiter, J. P. (2005) Releasing multiply-imputed, synthetic public use microdata: An illustration and empirical study. Journal of the Royal Statistical Society, Series A, 168(1), 185-205. doi:10.1111/j.1467-985X.2004.00343.x
Robitzsch, A., Pham, G., & Yanagida, T. (2016). Fehlende Daten und Plausible Values. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der oesterreichischen Bildungsstandardueberpruefung (S. 259-293). Wien: facultas.
Rubin, D. B. (2003). Nested multiple imputation of NMES via partially incompatible MCMC. Statistica Neerlandica, 57(1), 3-18. doi:10.1111/1467-9574.00217
van Buuren, S. (2018). Flexible imputation of missing data. Boca Raton: CRC Press. doi:10.1201/9780429492259
van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1-67. doi:10.18637/jss.v045.i03
See also the CRAN task view Missing Data:
https://CRAN.R-project.org/view=MissingData
See other R packages for conducting multiple imputation:
mice, Amelia, pan, mi, norm,
norm2, BaBooN, VIM, ...
Some links to internet sites related to missing data:
http://missingdata.lshtm.ac.uk/
http://www.stefvanbuuren.nl/mi/
http://www.bristol.ac.uk/cmm/software/realcom/
https://rmisstastic.netlify.com/
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