mice v3.6.0


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