# Multivariate Imputation by Chained Equations

## Documentation for package ‘mice’ version 3.16.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A B C D E F G H I L M N P Q R S T V W X misc

## -- A --

ampute | Generate missing data for simulation purposes
anova.mira | Compare several nested models
appendbreak | Appends specified break to the data
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

## -- B --

boys | Growth of Dutch boys
brandsma | Brandsma school data used Snijders and Bosker (2012)
bwplot | Box-and-whisker plot of observed and imputed data
bwplot.mads | Box-and-whisker plot of amputed and non-amputed data
bwplot.mids | Box-and-whisker plot of observed and imputed data

## -- C --

cart | Imputation by classification and regression trees
cbind | Combine R objects by rows and columns
cc | Select complete cases
cci | Complete case indicator
complete | Extracts the completed data from a 'mids' object
complete.mids | Extracts the completed data from a 'mids' object
construct.blocks | Construct blocks from 'formulas' and 'predictorMatrix'
convergence | Computes convergence diagnostics for a 'mids' object

## -- D --

D1 | Compare two nested models using D1-statistic
D2 | Compare two nested models using D2-statistic
D3 | Compare two nested models using D3-statistic
densityplot | Density plot of observed and imputed data
densityplot.mids | Density plot of observed and imputed data

## -- E --

employee | Employee selection data
estimice | Computes least squares parameters
extractBS | Extract broken stick estimates from a 'lmer' object

## -- F --

fdd | SE Fireworks disaster data
fdd.pred | SE Fireworks disaster data
fdgs | Fifth Dutch growth study 2009
fico | Fraction of incomplete cases among cases with observed
filter.mids | Subset rows of a 'mids' object
fix.coef | Fix coefficients and update model
flux | Influx and outflux of multivariate missing data patterns
fluxplot | Fluxplot of the missing data pattern
futuremice | Wrapper function that runs MICE in parallel

## -- G --

getfit | Extract list of fitted models
getqbar | Extract estimate from 'mipo' object
glm.mids | Generalized linear model for 'mids' object

## -- H --

hazard | Cumulative hazard rate or Nelson-Aalen estimator

## -- I --

ibind | Enlarge number of imputations by combining 'mids' objects
ic | Select incomplete cases
ici | Incomplete case indicator
ici-method | Incomplete case indicator
is.mads | Check for 'mads' 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

## -- L --

lasso.logreg | Imputation by direct use of lasso logistic regression
lasso.norm | Imputation by direct use of lasso linear regression
lasso.select.logreg | Imputation by indirect use of lasso logistic regression
lasso.select.norm | Imputation by indirect use of lasso linear regression
leiden85 | Leiden 85+ study
lm.mids | Linear regression for 'mids' object

## -- M --

mads-class | Multivariate amputed data set ('mads')
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.post | Creates a 'post' argument
make.predictorMatrix | Creates a 'predictorMatrix' argument
make.visitSequence | Creates a 'visitSequence' argument
make.where | Creates a 'where' argument
mammalsleep | Mammal sleep data
matchindex | Find index of matched donor units
md.pairs | Missing data pattern by variable pairs
md.pattern | Missing data pattern
mdc | Graphical parameter for missing data plots
mgg | Self-reported and measured BMI
mice | 'mice': Multivariate Imputation by Chained Equations
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 | Imputation under MNAR mechanism by NARFCS
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
mice.mids | Multivariate Imputation by Chained Equations (Iteration Step)
mice.theme | Set the theme for the plotting Trellis functions
mids | Multiply imputed data set ('mids')
mids-class | Multiply imputed data set ('mids')
mids2mplus | Export 'mids' object to Mplus
mids2spss | Export 'mids' object to SPSS
mira | Multiply imputed repeated analyses ('mira')
mira-class | Multiply imputed repeated analyses ('mira')
mnar.logreg | Imputation under MNAR mechanism by NARFCS
mnar.norm | Imputation under MNAR mechanism by NARFCS
mnar_demo_data | MNAR demo data
mpmm | Imputation by multivariate predictive mean matching

## -- N --

name.blocks | Name imputation blocks
name.formulas | Name formula list elements
ncc | Number of complete cases
nelsonaalen | Cumulative hazard rate or Nelson-Aalen estimator
nhanes | NHANES example - all variables numerical
nhanes2 | NHANES example - mixed numerical and discrete variables
nic | Number of incomplete cases
nimp | Number of imputations per block
norm | Imputation by Bayesian linear regression
norm.boot | Imputation by linear regression, bootstrap method
norm.draw | Draws values of beta and sigma by Bayesian linear regression
norm.nob | Imputation by linear regression without parameter uncertainty
norm.predict | Imputation by linear regression through prediction

## -- P --

parlmice | Wrapper function that runs MICE in parallel
pattern | Datasets with various missing data patterns
pattern1 | Datasets with various missing data patterns
pattern2 | Datasets with various missing data patterns
pattern3 | Datasets with various missing data patterns
pattern4 | Datasets with various missing data patterns
plot.mids | Plot the trace lines of the MICE algorithm
pmm | Imputation by predictive mean matching
pool | Combine estimates by pooling rules
pool.compare | Compare two nested models fitted to imputed data
pool.r.squared | Pools R^2 of m models fitted to multiply-imputed data
pool.scalar | Multiple imputation pooling: univariate version
pool.scalar.syn | Multiple imputation pooling: univariate version
pool.syn | Combine estimates by pooling rules
popmis | Hox pupil popularity data with missing popularity scores
pops | Project on preterm and small for gestational age infants (POPS)
pops.pred | Project on preterm and small for gestational age infants (POPS)
potthoffroy | Potthoff-Roy data
print.mads | Print a 'mads' object
print.mice.anova | Print a 'mids' object
print.mice.anova.summary | Print a 'mids' object
print.mids | Print a 'mids' object
print.mira | Print a 'mids' object

## -- Q --

quadratic | Imputation of quadratic terms
quickpred | Quick selection of predictors from the data

## -- R --

rbind | Combine R objects by rows and columns
ri | Imputation by the random indicator method for nonignorable data

## -- S --

selfreport | Self-reported and measured BMI
sleep | Mammal sleep data
squeeze | Squeeze the imputed values to be within specified boundaries.
stripplot | Stripplot of observed and imputed data
stripplot.mids | Stripplot of observed and imputed data
summary.mads | Summary of a 'mira' object
summary.mice.anova | Summary of a 'mira' object
summary.mids | Summary of a 'mira' object
summary.mira | Summary of a 'mira' object
supports.transparent | Supports semi-transparent foreground colors?

## -- T --

tbc | Terneuzen birth cohort
tbc.target | Terneuzen birth cohort
terneuzen | Terneuzen birth cohort
toenail | Toenail data
toenail2 | Toenail data
transparent | Supports semi-transparent foreground colors?

## -- V --

version | Echoes the package version number

## -- W --

walking | Walking disability data
windspeed | Subset of Irish wind speed data
with.mids | Evaluate an expression in multiple imputed datasets

## -- X --

xyplot | Scatterplot of observed and imputed data
xyplot.mads | Scatterplot of amputed and non-amputed data against weighted sum scores
xyplot.mids | Scatterplot of observed and imputed data

## -- misc --

.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)
2l.pan | Imputation by a two-level normal model using 'pan'
2lonly.mean | Imputation of most likely value within the class
2lonly.norm | Imputation at level 2 by Bayesian linear regression
2lonly.pmm | Imputation at level 2 by predictive mean matching