miceadds (version 3.2-48)

miceadds-package: miceadds

Description

miceadds

Details

• 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.

• 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).

References

Grund, S., Luedtke, O., & Robitzsch, A. (2018). Multiple imputation of multilevel data in organizational research. Organizational Research Methods, 21(1), 111-149.

Mislevy, R. J. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56, 177-196.

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.

van Buuren, S. (2018). Flexible imputation of missing data. Boca Raton: CRC Press.

van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1-67.

See Also

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/

Examples

# NOT RUN {
##
##   ::'''''''''''''''''''''''''''''''''::
##   :: miceadds 0.11-69 (2013-12-01)   ::
##   ::'''''''''''''''''''''''''''''''''::
##
##  ----------------------- mice at work ---------------------------------
##
##                         (\-.
##                         / _`> .---------.
##                 _)     / _)=  |'-------'|
##                (      / _/    |O   O   o|
##                 `-.__(___)_   | o O . o |
##                               `---------'
##
##                                          oo__
##                                         <;___)------
##                                    oo__   " "
##                                   <;___)------     oo__
##                                     " "           <;___)------
##                                                     " "
# }

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