title | output |
---|---|
News |
github_document |
- Solves bug in passive imputation (#130). Warning: This bug may
have caused invalid imputations in
mice 3.0.0
-mice 3.2.0
under passive imputation. - Updates code to
broom 0.5.0
(#128) - Solves problem with
mice.impute.2l.norm()
(#129) - Use explicit foreign function calls in tests
- Skip tests for
mice.impute.2l.norm()
(#129) - Skip tests for
D1()
(#128) - Solve problem with
md.pattern
(#126) - Evades warning in
rbind
andcbind
(#114) - Solves
rbind
problem whenmethod
is a list (#113) - More efficient use of
parlmice
(#109) - Add
dfcom
argument topool()
(#105, #110) - Updates to
parlmice
+ bugfix (#107)
- New parallel functionality:
parlmice
(#104) - Incorporate suggestion of @JoergMBeyer to
flux
(#102) - Replace duplicate code by
estimice
(#101) - Better checking for empty methods (#99)
- Remove problem with
parent.frame
(#98) - Set empty method for complete data (#93)
- Add
NEWS.md
,index.Rmd
and online package documentation - Track
.R
instead of.r
- Patch issue with
updateLog
(#8, @alexanderrobitzsch) - Extend README
- Repair issue
md.pattern
(#90) - Repair check on
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; 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: A brand new edition AND online version of Flexible Imputation of Missing Data. Second Edition.
- simplify code for
mids
object inmice
(thanks stephematician) (#61) - simplify code in
rbind.mids
(thanks stephematician) (#59) - repair bug in
pool.compare()
in handling factors (#60) - fixed bug in
rbind.mids
in handlingwhere
(#59) - add new arguments to
as.mids()
, addas()
- update contact info
- resolved problem
cart
not accepting a matrix (thanks Joerg Drechsler) - Adds generalized
pool()
to list of models - Switch to 3-digit versioning
- Allow for capitals in imputation methods
- Reorganized vignettes to land on GitHUB pages
- Code changes for robustness, style and efficiency (Bernie Gray)
- Updates the
ampute
function and vignettes (Rianne Schouten)
- Rename
mice.impute.2l.sys
tomice.impute.2l.lmer
- Add new feature:
where
argument to mice - Add new
wy
argument to imputation functions - Add
mice.impute.2l.sys()
, author Shahab Jolani - Update with many simplifications and code enhancements
- Fixed broken
cbind()
function - Fixed Bug that made the pad element disappear from
mids
object
- Fixed integration with
lattice
package - Updates colors in
xyplot.mads
- Add support for factors in
mice.impute.2lonly.pmm()
- Create more robust version of as.mids()
- Update of
ampute()
by Rianne Schouten - Fix timestamp problem by rebuilding vignette using R 3.4.0.
- Update to roxygen 6.0.1
- Stylistic changes to
mice
function (thanks Ben Ogorek) - Calls to
cbind.mids()
replaced by calls tocbind()
- Add link to
miceVignettes
on github (thanks Gerko Vink) - Add package documentation
- Add
README
for GitHub - Add new ampute functions and vignette (thanks Rianne Schouten)
- Rename
ccn
-->ncc
,icn
-->nic
- Change helpers
cc()
,ncc()
,cci()
,ic()
,nic()
andici()
useS3
dispatch - Change issues tracker on Github - add BugReports URL #21
- Fixed
multinom
MaxNWts type fix inpolyreg
andpolr
#9 - Fix checking of nested models in
pool.compare
#12 - Fix
as.mids
if names not same as all columns #11 - Fix extension for
glmer
models #5
- Add
midastouch
: predictive mean matching for small samples (thanks Philip Gaffert, Florian Meinfelder)
- Repaired dots problem in
rpart
call
- Add
ridge
to2l.norm()
- Remove
.o
files
- Fix
as.mids()
bug that crashedmiceadds::mice.1chain()
-
Update of example code on /doc
-
Remove lots of dependencies, general cleanup
-
Fix
impute.polyreg()
bug that bombed if there were no predictors (thanks Jan Graffelman) -
Fix
as.mids()
bug that gave incorrect$m$ (several users) -
Fix
pool.compare()
error forlmer
object (thanks Claudio Bustos) -
Fix error in
mice.impute.2l.norm()
if just oneNA
(thanks Jeroen Hoogland)
- Add about six times faster predictive mean matching
pool.scalar()
now can do Barnard-Rubin adjustmentpool()
now handles classlmerMod
from thelme4
package- Added automatic bounds on donors in
.pmm.match()
for safety - Added donors argument to
mice.impute.pmm()
for increased visibility - Changes default number of trees in
mice.impute.rf()
from 100 to 10 (thanks Anoop Shah) long2mids()
deprecated. Useas.mids()
instead- Put
lattice
back into DEPENDS to find genericxyplot()
and friends - Fix error in
2lonly.pmm
(thanks Alexander Robitzsch, Gerko Vink, Judith Godin) - Fix number of imputations in
as.mids()
(thanks Tommy Nyberg, Gerko Vink) - Fix colors to
mdc()
in examplemice.impute.quadratic()
- Fix error in
mice.impute.rf()
if just oneNA
(thanks Anoop Shah) - Fix error in
summary.mipo()
whennames(x$qbar)
equalsNULL
(thanks Aiko Kuhn) - Fix improper testing in
ncol()
inmice.impute.2lonly.mean()
- FIXED: compilation problem in match.cpp on solaris CC
- ADDED: experimental fastpmm() function using Rcpp
- FIXED: fixes to mice.impute.cart() and mice.impute.rf() (thanks Anoop Shah)
- ADDED: mice.impute.rf() for random forest imputation (thanks Lisa Doove)
- CHANGED: default number of donors in mice.impute.pmm() changed from 3 to 5. Use mice(..., donors = 3) to get the old behavior.
- CHANGED: speedup in .norm.draw() by using crossprod() (thanks Alexander Robitzsch)
- CHANGED: speedup in .imputation.level2() (thanks Alexander Robitzsch)
- FIXED: define MASS, nnet, lattice as imports instead of depends
- FIXED: proper handling of rare case in remove.lindep() that removed all predictors (thanks Jaap Brand)
- ADDED: as.mids() for converting long format in a mids object (thanks Gerko Vink)
- FIXED: mice.impute.logreg.boot() now properly exported (thanks Suresh Pujar)
- FIXED: two bugs in rbind.mids() (thanks Gerko Vink)
- ADDED: new form argument to mice() to specify imputation models using forms (contributed Ross Boylan)
- FIXED: with.mids(), is.mids(), is.mira() and is.mipo() exported
- FIXED: eliminated errors in the documentation of pool.scalar()
- FIXED: error in mice.impute.ri() (thanks Shahab Jolani)
- ADDED: random indicator imputation by mice.impute.ri() for nonignorable models (thanks Shahab Jolani)
- ADDED: workhorse functions .norm.draw() and .pmm.match() are exported
- FIXED: bug in 2.14 and 2.15 in mice.impute.pmm() that produced an error on factors
- FIXED: bug that crashed R when the class variable was incomplete (thanks Robert Long)
- FIXED: bug in 2l.pan and 2l.norm by convert a class factor to integer (thanks Robert Long)
- FIXED: warning eliminated caused by character variables (thanks Robert Long)
- CHANGED: complete reorganization of documentation and source files
- ADDED: source published on GitHub.com
- ADDED: new imputation method mice.impute.cart() (thanks Lisa Doove)
- FIXED: calculation of degrees of freedom in pool.compare() (thanks Lorenz Uhlmann)
- FIXED: error in DESCRIPTION file (thanks Kurt Hornik)
- ADDED: mice.impute.2l.mean() for imputing class means at level 2
- ADDED: sampler(): new checks of degrees of freedom per variable at iteration 1
- ADDED: function check.df() to throw a warning about low degrees of freedom
- FIXED: tolower() added in "2l" test in sampler()
- FIXED: conversion of factors that have other roles (multilevel) in padModel()
- FIXED: family argument in call to glm() in glm.mids() (thanks Nicholas Horton)
- FIXED: .norm.draw(): evading NaN imputed values by setting df in rchisq() to a minimum of 1
- FIXED: bug in mice.df() that prevented the classic Rubin df calculation (thanks Jean-Batiste Pingaul)
- FIXED: bug fixed in mice.impute.2l.norm() (thanks Robert Long)
- CHANGED: faster .pmm.match2() from version 2.12 renamed to default .pmm.match()
- ADDED: new multilevel functions 2l.pan(), 2lonly.norm(), 2lonly.pmm() (contributed by Alexander Robitzsch)
- ADDED: new quadratic imputation function: quadratic() (contributed by Gerko Vink)
- ADDED: pmm2(), five times faster than pmm()
- ADDED: new argument data.init in mice() for initialization (suggested by Alexander Robitzsch)
- ADDED: mice() now accepts pmm as method for (ordered) factors
- ADDED: warning and a note to 2l.norm() that advises to use type=2 for the predictors
- FIXED: bug that chrashed plot.mids() if there was only one incomplete variable (thanks Dennis Prangle)
- FIXED: bug in sample() in .pmm.match() when donor=1 (thanks Alexander Robitzsch)
- FIXED: bug in sample() in mice.impute.sample()
- FIXED: fixed '?data' bug in check.method()
- REMOVED: wp.twin(). Now available from the AGD package
- UPDATE: version for launch of Flexible Imputation of Missing Data (FIMD)
- ADDED: code fimd1.r-fim9.r to inst/doc for calculating solutions in FIMD
- FIXED: more robust version of supports.transparent() (thanks Brian Ripley)
- ADDED: auxiliary functions ifdo(), long2mids(), appendbreak(), extractBS(), wp.twin()
- ADDED: getfit() function
- ADDED: datasets: tbc, potthoffroy, selfreport, walking, fdd, fdgs, pattern1-pattern4, mammalsleep
- FIXED: as.mira() added to namespace
- ADDED: functions flux(), fluxplot() and fico() for missing data patterns
- ADDED: function nelsonaalen() for imputing survival data
- CHANGED: rm.whitespace() shortened
- FIXED: bug in pool() that crashed on nonstandard behavior of survreg() (thanks Erich Studerus)
- CHANGED: pool() streamlined, warnings about incompatibility in lengths of coef() and vcov()
- FIXED: mdc() bug that ignored transparent=FALSE argument, now made visible
- FIXED: bug in md.pattern() for >32 variables (thanks Sascha Vieweg, Joshua Wiley)
- UPDATE: definite reference to JSS paper
- ADDED: rm.whitespace() to do string manipulation (thanks Gerko Vink)
- ADDED: function mids2mplus() to export data to Mplus (thanks Gerko Vink)
- CHANGED: plot.mids() changed into trellis version
- ADDED: code used in JSS-paper
- FIXED: bug in check.method() (thanks Gerko Vink)
- FIXED: arguments dec and sep in mids2spss (thanks Nicole Haag)
- FIXED: bug in keyword "monotone" in mice() (thanks Alain D)
- FIXED: appropriate trimming of ynames and xnames in Trellis plots
- FIXED: exported: spss2mids(), mice.impute.2L.norm()
- ADDED: mice.impute.norm.predict(), mice.impute.norm.boot(), mice.impute.logreg.boot()
- ADDED: supports.transparent() to detect whether .Device can do semi-transparent colors
- FIXED: stringr package is now properly loaded
- ADDED: trellis version of plot.mids()
- ADDED: automatic semi-transparancy detection in mdc()
- FIXED: documentation of mira class (thanks Sandro Tsang)
- FIXED: bug fixed in find.collinear() that bombed when only one variable was left
- CHANGED: check.data(), remove.lindep(): fully missing variables are imputed if allow.na=TRUE (Alexander Robitzsch)
- FIXED: bug in check.data(). Now checks collinearity in predictors only (Alexander Robitzsch)
- CHANGED: abbreviations of arguments eliminated to evade linux warnings
- ADDED: bwplot(), stripplot(), densityplot() and xyplot() for creating Trellis graphs
- ADDED: function mdc() and mice.theme() for graphical parameters
- ADDED: argument passing from mice() to lower-level functions (requested by Juned Siddique)
- FIXED: erroneous rgamma() replaced by rchisq() in .norm.draw, lowers variance a bit for small n
- ADDED: with.mids() extended to handle expression objects
- FIXED: reporting bug in summary.mipo()
- CHANGED: df calculation in pool(), intervals may become slightly wider
- ADDED: internal functions mice.df() and df.residual()
- FIXED: error in rm calculation for "likelihood" in pool.compare()
- CHANGED: default ridge parameter changed
- ADDED: various stability enhancements and code clean-up
- ADDED: find.collinear() function
- CHANGED: automatic removal of constant and collinear variables
- ADDED: ridge parameter in .norm.draw() and .norm.fix()
- ADDED: mice.impute.polr() for ordered factors
- FIXED: chainMean and chainVar in mice.mids()
- FIXED: iteration counter for mice.mids and sampler()
- ADDED: component 'loggedEvents' to mids-object for logging actions
- REMOVED: annoying warnings about removed predictors
- ADDED: updateLog() function
- CHANGED: smarter handling of model setup in mice()
- CHANGED: .pmm.match() now draws from the three closest donors
- ADDED: mids2spss() for shipping a mids-object to SPSS
- FIXED: change in summary.mipo() to work with as.mira()
- ADDED: function mice.impute.2L.norm.noint()
- ADDED: function as.mira()
- FIXED: global assign() removed from mice.impute.polyreg()
- FIXED: improved handling of factors by complete()
- FIXED: improved labeling of nhanes2 data
- ADDED: pool() now supports class 'polr' (Jean-Baptiste Pingault)
- FIXED: solved problem in mice.impute.polyreg when one of the variables was named y or x
- FIXED: remove.lindep: intercept prediction bug
- ADDED: version() function
- ADDED: cc(), cci() and ccn() convenience functions
- FIXED: check.method: logicals are now treated as binary variables (Emmanuel Charpentier)
- FIXED: complete: the NULL imputation case is now properly handled
- FIXED: mice.impute.pmm: now creates between imputation variability for univariate predictor
- FIXED: remove.lindep: returns 'keep' vector instead of data
- ADDED: pool() now supports class 'multinom' (Jean-Baptiste Pingault)
- FIXED: bug fixed in check.data for data consisting of two columns (Rogier Donders, Thomas Koepsell)
- ADDED: new function remove.lindep() that removes predictors that are (almost) linearly dependent
- FIXED: bug fixed in pool() that produced an (innocent) warning message (Qi Zheng)
- ADDED: pool() now also supports class 'mer'
- CHANGED: nlme and lme4 are now only loaded if needed (by pool())
- FIXED: bug fixed in mice.impute.polyreg() when there was one missing entry (Emmanuel Charpentier)
- FIXED: bug fixed in plot.mids() when there was one missing entry (Emmanuel Charpentier)
- CHANGED: NAMESPACE expanded to allow easy access to function code
- FIXED: mice() can now find mice.impute.xxx() functions in the .GlobalEnv
- ADDED: new functions cbind.mids(), rbind.mids(), ibind()
- ADDED: new argument in mice(): 'post' in post-processing imputations
- ADDED: new functions: pool.scaler(), pool.compare(), pool.r.squared()
- ADDED: new data: boys, popmis, windspeed
- FIXED: function summary.mipo all(object$df) command fixed
- REMOVED: data.frame.to.matrix replaced by the internal data.matrix function
- ADDED: new imputation method mice.impute.2l.norm() for multilevel data
- CHANGED: pool now works for any class having a vcov() method
- ADDED: with.mids() provides a general complete-data analysis
- ADDED: type checking in mice() to ensure appropriate imputation methods
- ADDED: warning added in mice() for constant predictors
- ADDED: prevention of perfect prediction in mice.impute.logreg() and mice.impute.polyreg()
- CHANGED: mice.impute.norm.improper() changed into mice.impute.norm.nob()
- REMOVED: mice.impute.polyreg2() deleted
- ADDED: new 'include' argument in complete()
- ADDED: support for the empty imputation method in mice()
- ADDED: new function md.pairs()
- ADDED: support for intercept imputation
- ADDED: new function quickpred()
- FIXED: plot.mids() bug fix when number of variables > 5
- FIXED: Stricter type checking on logicals in mice() to evade warnings.
- CHANGED: Modernization of all help files.
- FIXED: padModel: treatment changed to contr.treatment
- CHANGED: Functions check.visitSequence, check.predictorMatrix, check.imputationMethod are now coded as local to mice()
- FIXED: existsFunction in check.imputationMethod now works both under S-Plus and R
- FIXED: The impution function impute.logreg used convergence criteria that were too optimistic when fitting a GLM with glm.fit. Thanks to Ulrike Gromping.
- FIXED: In the lm.mids and glm.mids functions, parameters were not passed through to glm and lm.
- FIXED: Passive imputation works again. (Roel de Jong)
- CHANGED: Random seed is now left alone, UNLESS the argument "seed" is specified. This means that unless you specify identical seed values, imputations of the same dataset will be different for multiple calls to mice. (Roel de Jong)
- FIXED: (docs): Documentation for "impute.mean" (Roel de Jong)
- FIXED: Function 'summary.mids' now works (Roel de Jong)
- FIXED: Imputation function 'impute.polyreg' and 'impute.lda' should now work under R
- Changed function checkImputationMethod, Feb 6, 2004
- Maintainance, S-Plus 6.1 and R 1.8 unicode, January 2004
- R version (with help of Peter Malewski and Frank Harrell), Feb 2001
- Original S-PLUS release, June 14 2000