mice 3.0.0
- mice 3.2.0
under passive imputation.broom 0.5.0
(#128)mice.impute.2l.norm()
(#129)mice.impute.2l.norm()
(#129)D1()
(#128)md.pattern
(#126)rbind
and cbind
(#114)rbind
problem when method
is a list (#113)parlmice
(#109)dfcom
argument to pool()
(#105, #110)parlmice
+ bugfix (#107)parlmice
(#104)flux
(#102)estimice
(#101)parent.frame
(#98)NEWS.md
, index.Rmd
and online package documentation.R
instead of .r
updateLog
(#8, @alexanderrobitzsch)md.pattern
(#90)m
(#89)Version 3.0 represents a major update that implements the following features:
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; The blocks
argument allows mixing univariate imputation method multivariate imputation methods. The blocks
feature bridges two seemingly disparate approaches, joint modeling and fully conditional specification, into one framework;
where
: The where
argument is a logical matrix of the same size of data
that specifies which cells should be imputed. This opens up some new analytic possibilities;
Multivariate tests: There are new functions D1()
, D2()
, D3()
and anova()
that perform multivariate parameter tests on the repeated analysis from on multiply-imputed data;
formulas
: The old form
argument has been redesign and is now renamed to formulas
. 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 packages dplyr
, tibble
and broom
;
Improved numerical algorithms for low-level imputation function. Better handling of duplicate variables.
Last but not least: A brand new edition AND online version of Flexible Imputation of Missing Data. Second Edition.
mids
object in mice
(thanks stephematician) (#61)rbind.mids
(thanks stephematician) (#59)pool.compare()
in handling factors (#60)rbind.mids
in handling where
(#59)as.mids()
, add as()
cart
not accepting a matrix (thanks Joerg Drechsler)pool()
to list of modelsampute
function and vignettes (Rianne Schouten)mice.impute.2l.sys
to mice.impute.2l.lmer
where
argument to micewy
argument to imputation functionsmice.impute.2l.sys()
, author Shahab Jolanicbind()
functionmids
objectlattice
packagexyplot.mads
mice.impute.2lonly.pmm()
ampute()
by Rianne Schoutenmice
function (thanks Ben Ogorek)cbind.mids()
replaced by calls to cbind()
miceVignettes
on github (thanks Gerko Vink)README
for GitHubccn
–> ncc
, icn
–> nic
cc()
, ncc()
, cci()
, ic()
, nic()
and ici()
use S3
dispatchmultinom
MaxNWts type fix in polyreg
and polr
#9pool.compare
#12as.mids
if names not same as all columns #11glmer
models #5midastouch
: predictive mean matching for small samples (thanks Philip Gaffert, Florian Meinfelder)rpart
callridge
to 2l.norm()
.o
filesas.mids()
bug that crashed miceadds::mice.1chain()
Remove lots of dependencies, general cleanup
impute.polyreg()
bug that bombed if there were no predictors (thanks Jan Graffelman)as.mids()
bug that gave incorrect \(m\) (several users)pool.compare()
error for lmer
object (thanks Claudio Bustos)Fix error in mice.impute.2l.norm()
if just one NA
(thanks Jeroen Hoogland)
pool.scalar()
now can do Barnard-Rubin adjustmentpool()
now handles class lmerMod
from the lme4
package.pmm.match()
for safetymice.impute.pmm()
for increased visibilitymice.impute.rf()
from 100 to 10 (thanks Anoop Shah)long2mids()
deprecated. Use as.mids()
insteadlattice
back into DEPENDS to find generic xyplot()
and friends2lonly.pmm
(thanks Alexander Robitzsch, Gerko Vink, Judith Godin)as.mids()
(thanks Tommy Nyberg, Gerko Vink)mdc()
in example mice.impute.quadratic()
mice.impute.rf()
if just one NA
(thanks Anoop Shah)summary.mipo()
when names(x$qbar)
equals NULL
(thanks Aiko Kuhn)ncol()
in mice.impute.2lonly.mean()