Package index • mice

Main imputation functions The workflow of multiple imputation is: multiply-impute the data, apply the complete-data model to each imputed data set, and pool the results to get to the final inference. The main functions for imputing the data are:
`mice()`	mice: Multivariate Imputation by Chained Equations
`mice.mids()`	Multivariate Imputation by Chained Equations (Iteration Step)
`parlmice()`	Wrapper function that runs MICE in parallel
`futuremice()`	Wrapper function that runs MICE in parallel
Missing data exploration Functions to count and explore the structure of the missing data.
`md.pattern()`	Missing data pattern
`md.pairs()`	Missing data pattern by variable pairs
`cc()`	Select complete cases
`cci()`	Complete case indicator
`ic()`	Select incomplete cases
`ici()`	Incomplete case indicator
`mcar()`	Jamshidian and Jalal's Non-Parametric MCAR Test
`ncc()`	Number of complete cases
`nic()`	Number of incomplete cases
`nimp()`	Number of imputations per block
`fico()`	Fraction of incomplete cases among cases with observed
`flux()`	Influx and outflux of multivariate missing data patterns
`fluxplot()`	Fluxplot of the missing data pattern
Elementary imputation functions The elementary imputation function is the workhorse that creates the actual imputations. Elementary functions are called through the `method` argument of `mice` function. Each function imputes one or more columns in the data. There are also `mice.impute.xxx` functions outside the `mice` package.
`mice.impute.2l.bin()`	Imputation by a two-level logistic model using `glmer`
`mice.impute.2l.lmer()`	Imputation by a two-level normal model using `lmer`
`mice.impute.2l.norm()`	Imputation by a two-level normal model
`mice.impute.2l.pan()`	Imputation by a two-level normal model using `pan`
`mice.impute.2lonly.mean()`	Imputation of most likely value within the class
`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.cart()`	Imputation by classification and regression trees
`mice.impute.jomoImpute()`	Multivariate multilevel imputation using `jomo`
`mice.impute.lasso.logreg()`	Imputation by direct use of lasso logistic regression
`mice.impute.lasso.norm()`	Imputation by direct use of lasso linear regression
`mice.impute.lasso.select.logreg()`	Imputation by indirect use of lasso logistic regression
`mice.impute.lasso.select.norm()`	Imputation by indirect use of lasso linear regression
`mice.impute.lda()`	Imputation by linear discriminant analysis
`mice.impute.logreg()`	Imputation by logistic regression
`mice.impute.logreg.boot()`	Imputation by logistic regression using the bootstrap
`mice.impute.mean()`	Imputation by the mean
`mice.impute.midastouch()`	Imputation by predictive mean matching with distance aided donor selection
`mice.impute.mnar.logreg()` `mice.impute.mnar.norm()`	Imputation under MNAR mechanism by NARFCS
`mice.impute.mpmm()`	Imputation by multivariate predictive mean matching
`mice.impute.norm()`	Imputation by Bayesian linear regression
`mice.impute.norm.boot()`	Imputation by linear regression, bootstrap method
`mice.impute.norm.nob()`	Imputation by linear regression without parameter uncertainty
`mice.impute.norm.predict()`	Imputation by linear regression through prediction
`mice.impute.panImpute()`	Impute multilevel missing data using `pan`
`mice.impute.passive()`	Passive imputation
`mice.impute.pmm()`	Imputation by predictive mean matching
`mice.impute.polr()`	Imputation of ordered data by polytomous regression
`mice.impute.polyreg()`	Imputation of unordered data by polytomous regression
`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
`mice.impute.sample()`	Imputation by simple random sampling
Imputation model helpers Specification of the imputation models can be made more convenient using the following set of helpers.
`construct.blocks()`	Construct blocks from `formulas` and `predictorMatrix`
`make.blocks()`	Creates a `blocks` argument
`make.blots()`	Creates a `blots` argument
`make.formulas()`	Creates a `formulas` argument
`make.method()`	Creates a `method` argument
`make.calltype()`	Create calltype of the imputation model
`make.post()`	Creates a `post` argument
`make.predictorMatrix()`	Creates a `predictorMatrix` argument
`make.visitSequence()`	Creates a `visitSequence` argument
`make.where()`	Creates a `where` argument
`name.blocks()`	Name imputation blocks
`name.formulas()`	Name formula list elements
`quickpred()`	Quick selection of predictors from the data
`squeeze()`	Squeeze the imputed values to be within specified boundaries.
Plots comparing observed to imputed/amputed data These plots contrast the observed data with the imputed/amputed data, usually with a blue/red distinction.
`bwplot(<mids>)`	Box-and-whisker plot of observed and imputed data
`densityplot(<mids>)`	Density plot of observed and imputed data
`mids()` `plot(<mids>)` `print(<mids>)` `summary(<mids>)`	Multiply imputed data set (`mids`)
`stripplot(<mids>)`	Stripplot of observed and imputed data
`xyplot(<mids>)`	Scatterplot of observed and imputed data
Repeated analyses and combining analytic estimates Multiple imputation creates m > 1 completed data sets, fits the model of interest to each of these, and combines the analytic estimates. The following functions assist in executing the analysis and pooling steps:
`with(<mids>)`	Evaluate an expression in multiple imputed datasets
`pool()` `pool.syn()`	Combine estimates by pooling rules
`pool.r.squared()`	Pools R^2 of m models fitted to multiply-imputed data
`pool.scalar()` `pool.scalar.syn()`	Multiple imputation pooling: univariate version
`pool.table()`	Combines estimates from a tidy table
`nelsonaalen()`	Cumulative hazard rate or Nelson-Aalen estimator
`pool.compare()`	Compare two nested models fitted to imputed data
`anova(<mira>)`	Compare several nested models
`fix.coef()`	Fix coefficients and update model
`D1()`	Compare two nested models using D1-statistic
`D2()`	Compare two nested models using D2-statistic
`D3()`	Compare two nested models using D3-statistic
Data manipulation The multiply-imputed data can be combined in various ways, and exported into other formats.
`complete(<mids>)`	Extracts the completed data from a `mids` object
`cbind()` `rbind()`	Combine R objects by rows and columns
`ibind()`	Enlarge number of imputations by combining `mids` objects
`as.mids()`	Converts an imputed dataset (long format) into a `mids` object
`as.mira()`	Create a `mira` object from repeated analyses
`as.mitml.result()`	Converts into a `mitml.result` object
`filter(<mids>)`	Subset rows of a `mids` object
`mids2mplus()`	Export `mids` object to Mplus
`mids2spss()`	Export `mids` object to SPSS
Class descriptions The data created at the various analytic phases are stored as list objects of a specific class. The most important classes and class-test functions are:
`mids()` `plot(<mids>)` `print(<mids>)` `summary(<mids>)`	Multiply imputed data set (`mids`)
`mira()`	Create an object of class "mira"
`mipo()` `summary(<mipo>)` `print(<mipo>)` `print(<mipo.summary>)` `process_mipo()`	`mipo`: Multiple imputation pooled object
`is.mids()`	Check for `mids` object
`is.mipo()`	Check for `mipo` object
`is.mira()`	Check for `mira` object
`is.mitml.result()`	Check for `mitml.result` object
Extraction functions Helpers to extract and print information from objects of specific classes.
`convergence()`	Computes convergence diagnostics for a `mids` object
`getfit()`	Extract list of fitted models
`getqbar()`	Extract estimate from `mipo` object
`glance(<mipo>)`	Glance method to extract information from a `mipo` object
`mids()` `plot(<mids>)` `print(<mids>)` `summary(<mids>)`	Multiply imputed data set (`mids`)
`print(<mira>)` `print(<mice.anova>)` `print(<mice.anova.summary>)`	Print a `mira` object
`summary(<mira>)` `summary(<mice.anova>)`	Summary of a `mira` object
`tidy(<mipo>)`	Tidy method to extract results from a `mipo` object
Low-level imputation functions Several functions are dedicated to common low-level operations to generate the imputations:
`estimice()`	Computes least squares parameters
`norm.draw()` `.norm.draw()`	Draws values of beta and sigma by Bayesian linear regression
`.pmm.match()`	Finds an imputed value from matches in the predictive metric (deprecated)
Multivariate amputation Amputation is the inverse of imputation, starting with a complete dataset, and creating missing data pattern according to the posited missing data mechanism. Amputation is useful for simulation studies.
`ampute()`	Generate missing data for simulation purposes
`bwplot(<mads>)`	Box-and-whisker plot of amputed and non-amputed data
`xyplot(<mads>)`	Scatterplot of amputed and non-amputed data against weighted sum scores
`is.mads()`	Check for `mads` object
`mads()` `print(<mads>)` `summary(<mads>)`	Multivariate amputed data set (`mads`)
Datasets Built-in datasets
`boys`	Growth of Dutch boys
`brandsma`	Brandsma school data used Snijders and Bosker (2012)
`employee`	Employee selection data
`fdd` `fdd.pred`	SE Fireworks disaster data
`fdgs`	Fifth Dutch growth study 2009
`leiden85`	Leiden 85+ study
`mammalsleep` `sleep`	Mammal sleep data
`mnar_demo_data`	MNAR demo data
`nhanes`	NHANES example - all variables numerical
`nhanes2`	NHANES example - mixed numerical and discrete variables
`pattern` `pattern1` `pattern2` `pattern3` `pattern4`	Datasets with various missing data patterns
`popmis`	Hox pupil popularity data with missing popularity scores
`pops` `pops.pred`	Project on preterm and small for gestational age infants (POPS)
`potthoffroy`	Potthoff-Roy data
`selfreport` `mgg`	Self-reported and measured BMI
`tbc` `tbc.target` `terneuzen`	Terneuzen birth cohort
`toenail`	Toenail data
`toenail2`	Toenail data
`walking`	Walking disability data
`windspeed`	Subset of Irish wind speed data
Miscellaneous functions Miscellaneous functions
`appendbreak()`	Appends specified break to the data
`extractBS()`	Extract broken stick estimates from a `lmer` object
`glm.mids()`	Generalized linear model for `mids` object
`lm.mids()`	Linear regression for `mids` object
`matchindex()`	Find index of matched donor units
`mdc()`	Graphical parameter for missing data plots
`mice.theme()`	Set the theme for the plotting Trellis functions
`supports.transparent()`	Supports semi-transparent foreground colors?
`version()`	Echoes the package version number

Reference

Main imputation functions

Missing data exploration

Elementary imputation functions

Imputation model helpers

Plots comparing observed to imputed/amputed data

Repeated analyses and combining analytic estimates

Data manipulation

Class descriptions

Extraction functions

Low-level imputation functions

Multivariate amputation

Datasets

Miscellaneous functions