MICE for Flexible Imputation of Missing Data, Second Edition
Very pleased to announce mice 3.0.0
, a major redesign of the package. Important changes include:
-
blocks
: The main algorithm iterates over blocks. A block is simply a collection of variables. In the common MICE algorithm each block was equivalent to one variable, which - of course - is the default; Theblocks
argument allows mixing univariate imputation method multivariate imputation methods. Theblocks
feature bridges two seemingly disparate approaches, joint modeling and fully conditional specification, into one framework; -
where
: Thewhere
argument is a logical matrix of the same size ofdata
that specifies which cells should be imputed. This opens up some new analytic possibilities; -
Multivariate tests: There are new functions
D1()
,D2()
,D3()
andanova()
that perform multivariate parameter tests on the repeated analysis from on multiply-imputed data; -
formulas
: The oldform
argument has been redesign and is now renamed toformulas
. This provides an alternative way to specify imputation models that exploits the full power of R's native formula's. -
Better integration with the
tidyverse
framework, especially for packagesdplyr
,tibble
andbroom
; -
Improved numerical algorithms for low-level imputation function. Better handling of duplicate variables.
-
Last but not least:
mice 3.0.0
is fully synchronised with a brand new edition AND online version of
Flexible Imputation of Missing Data. Second Edition.
I have tried to minimise the changes, in the hope that your existing code runs under this version. A (yet incomplete) overview of the changes to the function arguments can be found here.
Hope you find it useful.