mice v3.3.0


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