
tools:::Rd_package_description("miceadds")
tools:::Rd_package_author("miceadds")
Maintainer: tools:::Rd_package_maintainer("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
.
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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1177/1094428117703686")
Mislevy, R. J. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56(2), 177-196. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1111/1467-9574.00217")
van Buuren, S. (2018). Flexible imputation of missing data. Boca Raton: CRC Press. tools:::Rd_expr_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. tools:::Rd_expr_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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