Description Details Author(s) See Also Examples

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.

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`

).

Alexander Robitzsch, Simon Grund, Thorsten Henke

Maintainer: Alexander Robitzsch <[email protected]>

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/

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
##
## ::'''''''''''''''''''''''''''''''''::
## :: miceadds 0.11-69 (2013-12-01) ::
## ::'''''''''''''''''''''''''''''''''::
##
## ----------------------- mice at work ---------------------------------
##
## (\-.
## / _`> .---------.
## _) / _)= |'-------'|
## ( / _/ |O O o|
## `-.__(___)_ | o O . o |
## `---------'
##
## oo__
## <;___)------
## oo__ " "
## <;___)------ oo__
## " " <;___)------
## " "
``` |

```
Loading required package: mice
* miceadds 2.7-19 (2017-08-24 17:34:44)
```

miceadds documentation built on Sept. 21, 2018, 6:21 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.