# mice v3.3.0

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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 | |

cci | Complete case indicator | |

extractBS | Extract broken stick estimates from a lmer object | |

ampute.continuous | Multivariate Amputation Based On Continuous Probability Functions | |

ampute.default.freq | Default freq in ampute | |

employee | Employee selection data | |

fdd | SE Fireworks disaster data | |

D3 | Compare two nested models using D3-statistic | |

ampute | Generate Missing Data for Simulation Purposes | |

ampute.default.odds | Default odds in ampute() | |

estimice | Computes least squares parameters | |

as.mids | Converts an multiply imputed dataset (long format) into a mids object | |

as.mira | Create a mira object from repeated analyses | |

extend.formula | Extends a formula with predictors | |

getqbar | Extract estimate from mipo object | |

fluxplot | Fluxplot of the missing data pattern | |

extend.formulas | Extends formula's with predictor matrix settings | |

ampute.default.patterns | Default patterns in ampute | |

ibind | Enlarge number of imputations by combining mids objects | |

as.mitml.result | Converts into a mitml.result object | |

boys | Growth of Dutch boys | |

bwplot.mids | Box-and-whisker plot of observed and imputed data | |

ic | Select incomplete cases | |

md.pattern | Missing data pattern | |

mdc | Graphical parameter for missing data plots. | |

cbind | Combine R Objects by Rows and Columns | |

mice.impute.midastouch | Imputation by predictive mean matching with distance aided donor selection | |

mice.impute.norm | Imputation by Bayesian linear regression | |

mids-class | Multiply imputed data set (mids) | |

ampute.default.type | Default type in ampute() | |

fdgs | Fifth Dutch growth study 2009 | |

fico | Fraction of incomplete cases among cases with observed | |

ampute.default.weights | Default weights in ampute | |

mids2mplus | Export mids object to Mplus | |

ampute.discrete | Multivariate Amputation Based On Discrete Probability Functions | |

pool.r.squared | Pooling: R squared | |

pool.scalar | Multiple imputation pooling: univariate version | |

summary.mira | Summary of a mira object | |

ampute.mcar | Multivariate Amputation In A MCAR Manner | |

construct.blocks | Construct blocks from formulas and predictorMatrix | |

glm.mids | Generalized linear model for mids object | |

densityplot.mids | Density plot of observed and imputed data | |

getfit | Extract list of fitted model | |

make.formulas | Creates a formulas argument | |

is.mads | Check for mads object | |

supports.transparent | Supports semi-transparent foreground colors? | |

ici | Incomplete case indicator | |

brandsma | Brandsma school data used Snijders and Bosker (2012) | |

xyplot.mids | Scatterplot of observed and imputed data | |

bwplot.mads | Box-and-whisker plot of amputed and non-amputed data | |

is.mids | Check for mids object | |

ifdo | Conditional imputation helper | |

fix.coef | Fix coefficients and update model | |

make.post | Creates a post argument | |

is.mitml.result | Check for mitml.result object | |

cbind.mids | Combine mids objects by columns | |

leiden85 | Leiden 85+ study | |

make.predictorMatrix | Creates a predictorMatrix argument | |

mice | mice: Multivariate Imputation by Chained Equations | |

make.method | Creates a method argument | |

cc | Select complete cases | |

flux | Influx and outflux of multivariate missing data patterns | |

lm.mids | Linear regression for mids object | |

make.visitSequence | Creates a visitSequence argument | |

mads-class | Multivariate Amputed Data Set (mads) | |

is.mipo | Check for mipo object | |

mice.impute.2l.bin | Imputation by a two-level logistic model using glmer | |

mammalsleep | Mammal sleep data | |

make.where | Creates a where argument | |

md.pairs | Missing data pattern by variable pairs | |

is.mira | Check for mira object | |

make.blocks | Creates a blocks argument | |

mice.impute.2l.pan | Imputation by a two-level normal model using pan | |

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.2l.lmer | Imputation by a two-level normal model using lmer | |

make.blots | Creates a blots argument | |

mice.impute.cart | Imputation by classification and regression trees | |

mice.impute.2l.norm | Imputation by a two-level normal model | |

mice.impute.logreg.boot | Imputation by logistic regression using the bootstrap | |

mice.impute.panImpute | Impute multilevel missing data using pan | |

mice.impute.mean | Imputation by the mean | |

mice.impute.norm.predict | Imputation by linear regression through prediction | |

mice.impute.polr | Imputation of ordered data by polytomous regression | |

mice.impute.jomoImpute | Multivariate multilevel imputation using jomo | |

mice.impute.2lonly.mean | Imputation of the mean within the class | |

mice.impute.lda | Imputation by linear discriminant analysis | |

mice.impute.passive | Passive imputation | |

mice.impute.quadratic | Imputation of quadratic terms | |

mice.theme | Set the theme for the plotting Trellis functions | |

mice.mids | Multivariate Imputation by Chained Equations (Iteration Step) | |

mice.impute.pmm | Imputation by predictive mean matching | |

mice.impute.rf | Imputation by random forests | |

mice.impute.polyreg | Imputation of unordered data by polytomous regression | |

mids2spss | Export mids object to SPSS | |

mipo | mipo: Multiple imputation pooled object | |

mice.impute.logreg | Imputation by logistic regression | |

nelsonaalen | Cumulative hazard rate or Nelson-Aalen estimator | |

mice.impute.norm.boot | Imputation by linear regression, bootstrap method | |

mice.impute.norm.nob | Imputation by linear regression without parameter uncertainty | |

mice.impute.ri | Imputation by the random indicator method for nonignorable data | |

name.formulas | Name formula list elements | |

mira-class | Multiply imputed repeated analyses (mira) | |

mice.impute.sample | Imputation by simple random sampling | |

nimp | Number of imputations per block | |

ncc | Number of complete cases | |

print.mads | Print a mads object | |

nhanes2 | NHANES example - mixed numerical and discrete variables | |

nic | Number of incomplete cases | |

quickpred | Quick selection of predictors from the data | |

norm.draw | Draws values of beta and sigma by Bayesian linear regression | |

nhanes | NHANES example - all variables numerical | |

parlmice | Wrapper function that runs MICE in parallel | |

name.blocks | Name imputation blocks | |

plot.mids | Plot the trace lines of the MICE algorithm | |

.pmm.match | Finds an imputed value from matches in the predictive metric (deprecated) | |

popmis | Hox pupil popularity data with missing popularity scores | |

pattern | Datasets with various missing data patterns | |

tbc | Terneuzen birth cohort | |

version | Echoes the package version number | |

pops | Project on preterm and small for gestational age infants (POPS) | |

squeeze | Squeeze the imputed values to be within specified boundaries. | |

stripplot.mids | Stripplot of observed and imputed data | |

walking | Walking disability data | |

windspeed | Subset of Irish wind speed data | |

pool | Combine estimates by Rubin's rules | |

print.mids | Print a mids object | |

potthoffroy | Potthoff-Roy data | |

pool.compare | Compare two nested models fitted to imputed data | |

rbind.mids | Combine mids objects by rows | |

selfreport | Self-reported and measured BMI | |

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

with.mids | Evaluate an expression in multiple imputed datasets | |

D1 | Compare two nested models using D1-statistic | |

D2 | Compare two nested models using D2-statistic | |

anova.mira | Compare several nested models | |

appendbreak | Appends specified break to the data | |

complete | Extracts the completed data from a mids object | |

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## Last month downloads

## Details

Type | Package |

Date | 2018-07-27 |

LinkingTo | Rcpp |

License | GPL-2 | GPL-3 |

LazyLoad | yes |

LazyData | yes |

URL | http://stefvanbuuren.github.io/mice/ , http://www.stefvanbuuren.nl , http://www.multiple-imputation.com |

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

RoxygenNote | 6.0.1 |

NeedsCompilation | yes |

Packaged | 2018-07-27 08:22:52 UTC; buurensv |

Repository | CRAN |

Date/Publication | 2018-07-27 10:10:03 UTC |

suggests | AGD , BSDA , CALIBERrfimpute , DPpackage , 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 |

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