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)|
BaBooN-package: Package for multiple imputation of missing values based on...
BBPMMcol: (Multiple) Imputation through Bayesian Bootstrap Predictive...
BBPMMrow: (Multiple) Imputation of variable vectors
dmi: Data monotonicity index for missing values
impdiagnosticconversion: Conversion from BBPMM output to mice's mids object or...
MIinference: Multiple Imputation inference
rowimpprep: Missing-data pattern identifier
SummaryImp: Summary method for objects of class 'imp'
SummaryImpprep: Summary method for objects of class 'impprep'