# 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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## Last month downloads

## Details

Type | Package |

Date | 2020-08-02 |

LinkingTo | Rcpp |

Encoding | UTF-8 |

License | GPL-2 | GPL-3 |

LazyLoad | yes |

LazyData | yes |

URL | https://github.com/stefvanbuuren/mice, https://stefvanbuuren.name/mice/, https://stefvanbuuren.name/fimd/ |

BugReports | https://github.com/stefvanbuuren/mice/issues |

RoxygenNote | 7.1.1 |

NeedsCompilation | yes |

Packaged | 2020-08-03 12:44:35 UTC; buurensv |

Repository | CRAN |

Date/Publication | 2020-08-05 16:50:02 UTC |

imports | broom , dplyr , generics , graphics , lattice , methods , Rcpp , stats , tidyr , utils |

suggests | broom.mixed , knitr , lme4 , lmtest , MASS , miceadds , mitml , nnet , pan , randomForest , rmarkdown , rpart , survival , testthat |

depends | R (>= 2.10.0) |

Contributors | Florian Meinfelder, Alexander Robitzsch, Karin GroothuisOudshoorn, Vincent Arel-Bundock, Lisa Doove, Gerko Vink, Shahab Jolani, Rianne Schouten, Philipp Gaffert, Bernie Gray, Margarita Moreno-Betancur, Ian White |

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