Included are two variants of Bayesian Bootstrap Predictive Mean Matching to multiply impute missing data. The first variant is a variable-by-variable imputation combining sequential regression and Predictive Mean Matching (PMM) that has been extended for unordered categorical data. The Bayesian Bootstrap allows for generating approximately proper multiple imputations. The second variant is also based on PMM, but the focus is on imputing several variables at the same time. The suggestion is to use this variant, if the missing-data pattern resembles a data fusion situation, or any other missing-by-design pattern, where several variables have identical missing-data patterns. Both variants can be run as 'single imputation' versions, in case the analysis objective is of a purely descriptive nature.
|Author||Florian Meinfelder [aut, cre], Thorsten Schnapp [aut]|
|Date of publication||2015-06-15 17:30:31|
|Maintainer||Florian Meinfelder <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.