make.method()
in a more efficient way (resolves #672)literanger
package for rf
imputation that is about twice as fast as ranger
(#648). Thanks @stephematician for the contribution.Rprofile
prints to stdout
on Fedora, R version 4.1.3 (#646, #647). Thanks @brookslogan for the fix.minpuc
argument in quickpred()
(#634)coef() not available on S4 object
when using with lavaan
(#615, #616).github/dependabot.yml
configuration to automate daily check (#598)roxygen2 7.3.1
requirementsNEWS.md
formatting to get correct version sequence on CRAN and in-package NEWSblocks
argument at various placesblocks
in initialize_chain()
rbind()
, when formulas are concatenated and duplicate names are found, also rename the duplicated variables in formulas by their new namefilter.mids()
that incorrectly removed empty components in the imp
objectibind()
that incorrectly used length(blocks)
as the first dimension of the chainMean
and chainVar
objectsvisitSequence
, chainMean
and chainVar
components of the mids
objectcomplete()
that auto-repeated imputed values into cells that should NOT be imputed (occurred as a special case of rbind()
, where the first set of rows was imputed and the second was not).type
by the more informative pred
(currently active row of predictorMatrix
)Imputing categorical data by predictive mean matching. Predictive mean matching (PMM) is the default method of mice()
for imputing numerical variables, but it has long been possible to impute factors. This enhancement introduces better support to work with categorical variables in PMM. The former system translated factors into integers by ynum <- as.integer(f)
. However, the order of integers in ynum
may have no sensible interpretation for an unordered factor. The new system quantifies ynum
and could yield better results because of higher $R^2$. The method calculates the canonical correlation between y
(as dummy matrix) and a linear combination of imputation model predictors x
. The algorithm then replaces each category of y
by a single number taken from the first canonical variate. After this step, the imputation model is fitted, and the predicted values from that model are extracted to function as the similarity measure for the matching step.
The method works for both ordered and unordered factors. No special precautions are taken to ensure monotonicity between the category numbers and the quantifications, so the method should be able to preserve quadratic and other non-monotone relations of the predicted metric. It may be beneficial to remove very sparsely filled categories, for which there is a new trim
argument. All you have to use the new technique is specify to mice(..., method = "pmm", ...)
. Both numerical and categorical variables will then be imputed by PMM.
Potential advantages are:
Note that we still lack solid evidence for these claims. (#576). Contributed @stefvanbuuren
pool.table()
that takes a tidy table of parameter estimates stemming from m
repeated analyses. The input data must consist of three columns (parameter name, estimate, standard error) and a specification of the degrees of freedom of the model fitted to the complete data. The pool.table()
function outputs 14 pooled statistics in a tidy form. The primary use of pool.table()
is to support parameter pooling for techiques that have no tidy()
or glance()
methods, either within R
or outside R
. The pool.table()
function also allows for a novel workflows that 1) break apart the traditional pool()
function into a data-wrangling part and a parameters-reducing part, and 2) does not necessarily depend on classed R objects. (#574). Contributed @stefvanbuurencomplete(..., action = "long", ...)
command puts the columns named ".imp"
and ".id"
in the last two positions of the long data (instead of first two positions). In this way, the columns of the imputed data will have the same positions as in the original data, which is more user-friendly and easier to work with. Note that any existing code that assumes that variables ".imp"
and ".id"
are in columns 1 and 2 will need to be modified. The advice is to modify the code using the variable names ".imp"
and ".id"
. If you want the old behaviour, specify the argument order = "first"
. (#569). Contributed @stefvanbuurendots
argument to ranger::ranger(...)
in mice.impute.rf()
(#563). Contributed @edbonnevilleExpands futuremice()
functionality by allowing for external packages and user-written functions (#550). Contributed @thomvolker
Adds GH issue templates bug_report
, feature_request
and help_wanted
(#560). Contributed @hanneoberman
rbind.mids()
and cbind.mids()
to conform to CRAN policymitml
and glmnet
to imports so that test code conforms to _R_CHECK_DEPENDS_ONLY=true
flag in R CMD check
futuremice()
if there is no .Random.seed
yet.predictorMatrix
for case F by adding a predictorMatrix
argument to make.predictorMatrix()
mice.impute.mpmm()
example codemice.impute.2lonly.pmm()
(#555)tidy()
, update()
, format()
and sum()
R CMD check
with _R_CHECK_DEPENDS_ONLY=true
futuremice()
that throws an error when the number of cores is not specified, but the number of available cores is greater than the number of imputations.mice.impute.mpmm()
that changed the column order of the dataAdds a function futuremice()
with support for parallel imputation using the future
package (#504). Contributed @thomvolker, @gerkovink
Adds multivariate predictive mean matching mice.impute.mpmm()
. (#460). Contributed @Mingyang-Cai
Adds convergence()
for convergence evaluation (#484). Contributed @hanneoberman
Reverts the internal seed behaviour back to mice 3.13.10
(#515). #432 introduced new local seed in response to #426. However, various issues arose with this facility (#459, #492, #502, #505). This version restores the old behaviour using global .Random.seed
. Contributed @gerkovink
Adds a custom.t
argument to pool()
that allows the advanced user to specify a custom rule for calculating the total variance $T$. Contributed @gerkovink
Adds new argument exclude
to mice.impute.pmm()
that excludes a user-specified vector of values from matching. Excluded values will not appear in the imputations. Since the observed values are not imputed, the user-specified values are still being used to fit the imputation model (#392, #519). Contributed @gerkovink
.R
and .Rmd
filessampler.R
(#511)inherits()
to check on class membershipparlmice()
prop
, patterns
and weights
matrices for pattern with only 1'sD1()
and D2()
(#420)mice()
make.where()
test-mice.impute.rf.R
(#448).Random.seed
reads from the .GlobalEnv
by get(".Random.seed", envir = globalenv(), mode = "integer", inherits = FALSE)
lastSeedValue
variable namex$lastSeedValue
problem in cbind.mids()
(#502)ampute()
mice()
by smarter random seed initialisation (#459)drop = FALSE
buglet in mice.impute.rf()
(#447, #448)withr
package should have version 2.4.0 (published in January 2021) or higher. Versions withr 2.3.0
and before may give Error: object 'local_seed' is not exported by 'namespace:withr'
. Either update manually, or install the patched version mice 3.14.1
from GitHub. (#445). NOTE: withr
is no longer needed in mice 3.15.0
Adds four new univariate functions using the lasso for automatic variable selection. Contributed by @EdoardoCostantini (#438).
mice.impute.lasso.norm()
for lasso linear regressionmice.impute.lasso.logreg()
for lasso logistic regressionmice.impute.lasso.select.norm()
for lasso selector + linear regressionmice.impute.lasso.select.logreg()
for lasso selector + logistic regressionAdds Jamshidian && Jalal's non-parametric MCAR test, mice::MCAR()
and associated plot method. Contributed by @cjvanlissa (#423).
Adds two new functions pool.syn()
and pool.scalar.syn()
that specialise pooling estimates from synthetic data. The "reiter2003"
pooling rule assumes that synthetic data were created from complete data. Thanks Thom Volker (#436).
By default, mice.impute.rf()
now uses the faster ranger
package as back-end instead of randomForest
package. If you want the old behaviour specify the rfPackage = "randomForest"
argument to the mice(...)
call. Contributed @prockenschaub (#431).
.Random.seed
(#426, #432) by implementing withr::local_preserve_seed()
and withr::local_seed()
. This change provides stabler behavior in complex scripts. The change does not appear to break reproducibility when mice()
was run with a seed. Nevertheless, if you run into a reproducibility problem, install mice 3.13.12
or before.mice.impute.quadratic()
, adds a parameter quad.outcome
containing the name of the outcome variable in the complete-data model. Contributed @Mingyang-Cai, @gerkovink (#408)pool()
so that it processes the parameters from all gamlss
sub-models. Thanks Marcio Augusto Diniz (#406, #405)pool()
can extract robust.se
from the object returned by broom::tidy()
(#310)pool()
cannot take a mids
object (#433)mice.impute.2l.lmer()
to indicate a problem in fitting the imputation model (#385)post
parameter (#326)install.on.demand()
broke the standard CRAN workflow. mice 3.14.0 does not call install.on.demand()
anymore for recommended packages. Also, install.on.demand()
will not run anymore in non-interactive mode.mice:::barnard.rubin()
function for infinite dfcom
. Thanks @huftis (#441).Xi <- as.matrix(...)
in mice.impute.2l.lmer()
that occurred when a cluster contains only one observation (#384)predictorMatrix
to a monotone pattern if visitSequence = "monotone"
and maxit = 1
(#316)md.pattern()
(#318, #323)make.formulas()
(#305, #324)newdata
in mice.mids()
(#313, #325)where
element created in rbind()
(#319)mids2spss()
replaces the foreign
by haven
package. Contributed Gerko Vink (#291)tests\testhat\test-D1.R
that failed on mitml 0.4-0
with.mids()
function to old version because the change in commit 4634094 broke downstream package metafor
(#292)mice.impute.rf()
in finding candidate donors (#288, #289)Much faster predictive mean matching. The new matchindex
C function makes predictive mean matching 50 to 600 times faster.
The speed of pmm
is now on par with normal imputation (mice.impute.norm()
)
and with the miceFast
package, without compromising on the statistical quality of
the imputations. Thanks to Polkas https://github.com/Polkas/miceFast/issues/10 and
suggestions by Alexander Robitzsch. See #236 for more details.
New ignore
argument to mice()
. This argument is a logical vector
of nrow(data)
elements indicating which rows are ignored when creating
the imputation model. We may use the ignore
argument to split the data
into a training set (on which the imputation model is built) and a test
set (that does not influence the imputation model estimates). The argument
is based on the suggestion in
https://github.com/amices/mice/issues/32#issuecomment-355600365. See #32 for
more background and techniques. Crafted by Patrick Rockenschaub
New filter()
function for mids
objects. New filter()
method that
subsets a mids
object (multiply-imputed data set).
The method accepts a logical vector of length nrow(data)
, or an expression
to construct such a vector from the incomplete data. (#269).
Crafted by Patrick Rockenschaub.
Breaking change: The matcher
algorithm in pmm
has changed to matchindex
for speed improvements. If you want the old behavior, specify mice(..., use.matcher = TRUE)
.
cpp11
package (#286)with.mids()
by calling eval_tidy()
on a quosure. Does not yet solve #265.pool()
and pool.scalar()
(#142, #106, #190 and others)tidy.mipo
more flexible (#276)nelsonaalen()
gets a tibble
(#272)NA
s can appear in the imputed data (#267)quickpred()
documentation (#268)sum.scores()
lm.mids()
, glm.mids()
, pool.compare()
.pmm.match()
and expandcov()
return()
calls placed just before end-of-functionprintFlag
value (#258)amices
df.residual
, which caused problematic behavior in the D1()
, D2()
, D3()
, anova()
and pool()
. mice
now extracts the relevant information from other parts of the objects returned by survival::coxph()
, which solves long-standing issues with the integration of the Cox model (#246).Rccp
dependency to work with tidyr 1.1.1
(#248).Non-file package-anchored link(s) in documentation object
.ampute
documentation (#251).suggests
.tidy.mipo()
and glance.mipo()
return standardized output that conforms to broom
specifications. Kindly contributed by Vincent Arel Bundock (#240).D3
testing script that produced an error on CRAN (#244).The D3()
function in mice
gave incorrect results. This version solves a problem in the calculation of the D3
-statistic. See #226 and #228 for more details. The documentation explains why results from mice::D3()
and mitml::testModels()
may differ.
The pool()
function is now more forgiving when there is no glance()
function (#233)
It is possible to bypass remove.lindep()
by setting eps = 0
(#225)
plot.mids()
documentationThis version adds two new NARFCS methods for imputing data under the Missing Not at Random (MNAR) assumption. NARFCS is generalised version of the so-called $\delta$-adjustment method. Margarita Moreno-Betancur and Ian White kindly contributes the functions mice.impute.mnar.norm()
and mice.impute.mnar.logreg()
. These functions aid in performing sensitivity analysis to investigate the impact of different MNAR assumptions on the conclusion of the study. An alternative for MNAR is the older mice.impute.ri()
function.
Installation of mice
is faster. External packages needed for imputation and analyses are now installed on demand. The number of dependencies as estimated by rsconnect::appDepencies()
decreased from 132 to 83.
The name clash with the complete()
function of tidyr
should no longer be a problem.
There is now a more flexible pool()
function that integrates better with the broom
and broom.mixed
packages.
pool.compare()
. Use D1()
instead (#220)utils::globalVariables()
tidyr
by defining complete.mids()
as an S3 method for the tidyr::complete()
generic (#212)pool()
function to deal with multiple sets of parameters. Currently supported keywords are: term
(all broom
functions), component
(some broom.mixed
functions) and y.values
(for multinom()
model) (#219)install.on.demand()
function for lighter installationtoenail2
and remove dependency on HSAUR3
ampute
in extreme cases (#216)pool
with mgcv::gam
(#218).gitattributes
for consistent line endingspolr()
always fail (#206)data.frame
(#208)mira-class
documentation (#207)CALIBERrfimpute
2lonly.norm
and 2lonly.pmm
a2
to elementwise division by a matrix of observations2lonly.norm
and 2lonly.pmm
2lonly.pmm
2lonly.mean
now also works with factorsimputationMethod
argument in examples by method
check.predictorMatrix()
(#191)toenail
data from orphaned DPpackage
packageDPpackage
from Suggests
field in DESCRIPTION
md.pattern()
(#170, #177)as.mids
() (#173)mice.impute.xxx()
so that mice::mice()
works as expected (#55)mids2spss()
, thanks Edgar Schoreit (#149)predictorMatrix
.mice 3.3.1
will impute
those variables using the intercept onlynelsonaalen()
function for data where variables
time
or status
have already been defined (#140), thanks matthieu-faronmice 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()
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)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()