miceadds
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).
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 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/
# NOT RUN {
##
## ::'''''''''''''''''''''''''''''''''::
## :: miceadds 0.11-69 (2013-12-01) ::
## ::'''''''''''''''''''''''''''''''''::
##
## ----------------------- mice at work ---------------------------------
##
## (\-.
## / _`> .---------.
## _) / _)= |'-------'|
## ( / _/ |O O o|
## `-.__(___)_ | o O . o |
## `---------'
##
## oo__
## <;___)------
## oo__ " "
## <;___)------ oo__
## " " <;___)------
## " "
# }
Run the code above in your browser using DataLab