# mice v3.6.0

Monthly downloads

## 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 | |

No Results! |

## Last month downloads

## Details

Type | Package |

Date | 2019-07-09 |

LinkingTo | Rcpp |

Encoding | UTF-8 |

License | GPL-2 | GPL-3 |

LazyLoad | yes |

LazyData | yes |

URL | http://stefvanbuuren.github.io/mice/ , http://www.stefvanbuuren.name , http://www.stefvanbuuren.name/fimd/ |

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

RoxygenNote | 6.1.1 |

NeedsCompilation | yes |

Packaged | 2019-07-09 20:30:44 UTC; buurensv |

Repository | CRAN |

Date/Publication | 2019-07-10 08:00:03 UTC |

suggests | AGD , BSDA , CALIBERrfimpute , gamlss , HSAUR3 , knitr , lme4 , miceadds , micemd , mitools , nlme , pan , randomForest , rmarkdown , testthat , tidyr , Zelig |

imports | broom , dplyr , graphics , grDevices , MASS , mitml , nnet , parallel , Rcpp , rlang , rpart , splines , stats , survival , utils |

depends | lattice , methods , R (>= 2.10.0) |

Contributors | Florian Meinfelder, Alexander Robitzsch, Karin GroothuisOudshoorn, Lisa Doove, Gerko Vink, Shahab Jolani, Rianne Schouten, Philipp Gaffert, Bernie Gray |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/mice)](http://www.rdocumentation.org/packages/mice)
```