miceadds-package: tools:::Rd_package_title("miceadds")

Description

tools:::Rd_package_description("miceadds")

Arguments

Author

tools:::Rd_package_author("miceadds")

Maintainer: tools:::Rd_package_maintainer("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.
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).

References

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")

Examples

Run this code

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