mitml-package: mitml: Tools for multiple imputation in multilevel modeling
In mitml: Tools for Multiple Imputation in Multilevel Modeling
mitml-package R Documentation
mitml: Tools for multiple imputation in multilevel modeling
Description
Provides tools for multiple imputation of missing data in multilevel modeling.
This package includes a user-friendly interface to the algorithms implemented in the R packages pan and jomo as well as several functions for visualizing, managing, and analyzing multiply imputed data sets.
The main interface to pan is the function panImpute, which allows specifying imputation models for continuous variables with missing data at level 1.
In addition, the function jomoImpute provides an interface to jomo, which allows specifying imputation models for both continuous and categorical variables with missing data at levels 1 and 2 as well as single-level data.
The imputations and parameter values are stored in objects of class mitml.
To obtain the completed (i.e., imputed) data sets, mitmlComplete is used, producing a list of imputed data sets of class mitml.list that can be used in further analyses.
Several additional functions allow for convenient analysis of multiply imputed data sets including (bot not limited to) multilevel analyses with lme4 or nlme and structural equation modeling with lavaan.
The functions within, sort, and subset can be used to manage and manipulate multiply imputed data sets.
Statistical models are fitted using with.
Pooled parameter estimates can be extracted with testEstimates, and model comparisons as well as single- and multi-parameter hypotheses tests can be performed using the functions testModels and testConstraints.
In addition, the anova method provides a simple interface to model comparisons.
Data sets can be imported and exported from or to different statistical software packages.
Currently, mids2mitml.list, amelia2mitml.list, jomo2mitml.list, and long2mitml.list can be used for importing imputations for other packages in R.
In addition, write.mitmlMplus, write.mitmlSAV, and write.mitmlSPSS export data sets to Mplus and SPSS, respectively.
Finally, the package provides tools for summarizing and visualizing imputation models, which is useful for the assessment of convergence and the reporting of results.
The data sets contained in this package are published under the same license as the package itself.
They contain simulated data and may be used by anyone free of charge as long as reference to this package is given.
Author(s)
Authors: Simon Grund, Alexander Robitzsch, Oliver Luedtke
Maintainer: Simon Grund <grund@ipn.uni-kiel.de>
mitml documentation built on March 31, 2023, 7:01 p.m.
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| mitml-package | R Documentation |
mitml: Tools for multiple imputation in multilevel modeling
Description
Provides tools for multiple imputation of missing data in multilevel modeling.
This package includes a user-friendly interface to the algorithms implemented in the R packages pan and jomo as well as several functions for visualizing, managing, and analyzing multiply imputed data sets.
The main interface to pan is the function panImpute, which allows specifying imputation models for continuous variables with missing data at level 1.
In addition, the function jomoImpute provides an interface to jomo, which allows specifying imputation models for both continuous and categorical variables with missing data at levels 1 and 2 as well as single-level data.
The imputations and parameter values are stored in objects of class mitml.
To obtain the completed (i.e., imputed) data sets, mitmlComplete is used, producing a list of imputed data sets of class mitml.list that can be used in further analyses.
Several additional functions allow for convenient analysis of multiply imputed data sets including (bot not limited to) multilevel analyses with lme4 or nlme and structural equation modeling with lavaan.
The functions within, sort, and subset can be used to manage and manipulate multiply imputed data sets.
Statistical models are fitted using with.
Pooled parameter estimates can be extracted with testEstimates, and model comparisons as well as single- and multi-parameter hypotheses tests can be performed using the functions testModels and testConstraints.
In addition, the anova method provides a simple interface to model comparisons.
Data sets can be imported and exported from or to different statistical software packages.
Currently, mids2mitml.list, amelia2mitml.list, jomo2mitml.list, and long2mitml.list can be used for importing imputations for other packages in R.
In addition, write.mitmlMplus, write.mitmlSAV, and write.mitmlSPSS export data sets to Mplus and SPSS, respectively.
Finally, the package provides tools for summarizing and visualizing imputation models, which is useful for the assessment of convergence and the reporting of results.
The data sets contained in this package are published under the same license as the package itself. They contain simulated data and may be used by anyone free of charge as long as reference to this package is given.
Author(s)
Authors: Simon Grund, Alexander Robitzsch, Oliver Luedtke
Maintainer: Simon Grund <grund@ipn.uni-kiel.de>
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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