Learn R Programming

miceadds (version 2.10-14)

miceadds-package: Some Additional Multiple Imputation Functions, Especially for mice

Description

Contains some auxiliary functions for multiple imputation which complements existing functionality in R. In addition to some utility functions, main features include plausible value imputation, multilevel imputation functions (arbitrary number of levels, hierarchical and non-hierarchical datasets), imputation using partial least squares (PLS) for high dimensional predictors, and nested multiple imputation.

Arguments

Details

  • 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.

  • 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.

  • Imputations for questionnaire items can be accomplished by two-way imputation (tw.imputation).

  • The miceadds package also includes some functions R utility functions (e.g. write.pspp, ma.scale2).

See Also

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/

Examples

Run this code
# 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