mice v3.11.0


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Multivariate Imputation by Chained Equations

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

Functions in mice

Name Description
D1 Compare two nested models using D1-statistic
ampute.default.type Default type in ampute()
ampute.default.patterns Default patterns in ampute
D2 Compare two nested models using D2-statistic
ampute Generate Missing Data for Simulation Purposes
D3 Compare two nested models using D3-statistic
ampute.default.weights Default weights in ampute
ampute.default.freq Default freq in ampute
ampute.default.odds Default odds in ampute()
ampute.continuous Multivariate Amputation Based On Continuous Probability Functions
ampute.discrete Multivariate Amputation Based On Discrete Probability Functions
anova.mira Compare several nested models
construct.blocks Construct blocks from formulas and predictorMatrix
appendbreak Appends specified break to the data
brandsma Brandsma school data used Snijders and Bosker (2012)
bwplot.mads Box-and-whisker plot of amputed and non-amputed data
extend.formula Extends a formula with predictors
cci Complete case indicator
extend.formulas Extends formula's with predictor matrix settings
extractBS Extract broken stick estimates from a lmer object
as.mitml.result Converts into a mitml.result object
complete.mids Extracts the completed data from a mids object
densityplot.mids Density plot of observed and imputed data
fdd SE Fireworks disaster data
is.mira Check for mira object
ic Select incomplete cases
as.mids Converts an multiply imputed dataset (long format) into a mids object
make.blots Creates a blots argument
as.mira Create a mira object from repeated analyses
fdgs Fifth Dutch growth study 2009
bwplot.mids Box-and-whisker plot of observed and imputed data
is.mitml.result Check for mitml.result object
cbind Combine R Objects by Rows and Columns
ifdo Conditional imputation helper
fluxplot Fluxplot of the missing data pattern
fico Fraction of incomplete cases among cases with observed
mice.impute.2l.bin Imputation by a two-level logistic model using glmer
mdc Graphical parameter for missing data plots.
ampute.mcar Multivariate Amputation In A MCAR Manner
ici Incomplete case indicator
employee Employee selection data
mice mice: Multivariate Imputation by Chained Equations
is.mads Check for mads object
fix.coef Fix coefficients and update model
is.mids Check for mids object
is.mipo Check for mipo object
mice.impute.2l.lmer Imputation by a two-level normal model using lmer
getfit Extract list of fitted model
cbind.mids Combine mids objects by columns
boys Growth of Dutch boys
mads-class Multivariate Amputed Data Set (mads)
leiden85 Leiden 85+ study
estimice Computes least squares parameters
glm.mids Generalized linear model for mids object
mice.impute.lda Imputation by linear discriminant analysis
mice.impute.jomoImpute Multivariate multilevel imputation using jomo
mice.impute.rf Imputation by random forests
mice.impute.norm.nob Imputation by linear regression without parameter uncertainty
mice.impute.quadratic Imputation of quadratic terms
mice.impute.norm.boot Imputation by linear regression, bootstrap method
mice.impute.2lonly.norm Imputation at level 2 by Bayesian linear regression
make.formulas Creates a formulas argument
mice.impute.passive Passive imputation
md.pattern Missing data pattern
mice.impute.pmm Imputation by predictive mean matching
mice.impute.2lonly.mean Imputation of most likely value within the class
cc Select complete cases
mids2spss Export mids object to SPSS
getqbar Extract estimate from mipo object
flux Influx and outflux of multivariate missing data patterns
glance.mipo Glance method to extract information from a `mipo` object
make.method Creates a method argument
make.blocks Creates a blocks argument
make.predictorMatrix Creates a predictorMatrix argument
mnar_demo_data MNAR demo data
make.visitSequence Creates a visitSequence argument
mipo mipo: Multiple imputation pooled object
mammalsleep Mammal sleep data
mice.impute.2l.pan Imputation by a two-level normal model using pan
pool.compare Compare two nested models fitted to imputed data
pool.r.squared Pooling: R squared
md.pairs Missing data pattern by variable pairs
mice.impute.panImpute Impute multilevel missing data using pan
mice.impute.sample Imputation by simple random sampling
mice.impute.norm.predict Imputation by linear regression through prediction
mice.impute.logreg Imputation by logistic regression
mice.impute.logreg.boot Imputation by logistic regression using the bootstrap
mice.impute.mnar.logreg Imputation under MNAR mechanism by NARFCS
mice.impute.ri Imputation by the random indicator method for nonignorable data
mice.impute.polr Imputation of ordered data by polytomous regression
print.mids Print a mids object
nelsonaalen Cumulative hazard rate or Nelson-Aalen estimator
print.mads Print a mads object
toenail Toenail data
mice.impute.norm Imputation by Bayesian linear regression
nhanes NHANES example - all variables numerical
mids2mplus Export mids object to Mplus
mids-class Multiply imputed data set (mids)
toenail2 Toenail data
mira-class Multiply imputed repeated analyses (mira)
name.formulas Name formula list elements
ibind Enlarge number of imputations by combining mids objects
version Echoes the package version number
reexports Objects exported from other packages
lm.mids Linear regression for mids object
selfreport Self-reported and measured BMI
walking Walking disability data
mice.impute.polyreg Imputation of unordered data by polytomous regression
mice.impute.mean Imputation by the mean
nic Number of incomplete cases
make.post Creates a post argument
name.blocks Name imputation blocks
norm.draw Draws values of beta and sigma by Bayesian linear regression
parlmice Wrapper function that runs MICE in parallel
mice.impute.midastouch Imputation by predictive mean matching with distance aided donor selection
mice.impute.2l.norm Imputation by a two-level normal model
pool.scalar Multiple imputation pooling: univariate version
pattern Datasets with various missing data patterns
nhanes2 NHANES example - mixed numerical and discrete variables
plot.mids Plot the trace lines of the MICE algorithm
popmis Hox pupil popularity data with missing popularity scores
ncc Number of complete cases
squeeze Squeeze the imputed values to be within specified boundaries.
stripplot.mids Stripplot of observed and imputed data
mice.impute.2lonly.pmm Imputation at level 2 by predictive mean matching
make.where Creates a where argument
mice.impute.cart Imputation by classification and regression trees
quickpred Quick selection of predictors from the data
mice.theme Set the theme for the plotting Trellis functions
mice.mids Multivariate Imputation by Chained Equations (Iteration Step)
nimp Number of imputations per block
xyplot.mads Scatterplot of amputed and non-amputed data against weighted sum scores
.pmm.match Finds an imputed value from matches in the predictive metric (deprecated)
pool Combine estimates by Rubin's rules
potthoffroy Potthoff-Roy data
rbind.mids Combine mids objects by rows
summary.mira Summary of a mira object
supports.transparent Supports semi-transparent foreground colors?
windspeed Subset of Irish wind speed data
pops Project on preterm and small for gestational age infants (POPS)
with.mids Evaluate an expression in multiple imputed datasets
tbc Terneuzen birth cohort
xyplot.mids Scatterplot of observed and imputed data
tidy.mipo Tidy method to extract results from a `mipo` object
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