# mice v3.9.0

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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 | |

ampute | Generate Missing Data for Simulation Purposes | |

boys | Growth of Dutch boys | |

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

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

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

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

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

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

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

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

anova.mira | Compare several nested models | |

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

cci | Complete case indicator | |

appendbreak | Appends specified break to the data | |

cbind | Combine R Objects by Rows and Columns | |

estimice | Computes least squares parameters | |

extend.formula | Extends a formula with predictors | |

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

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

ici | Incomplete case indicator | |

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

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

fluxplot | Fluxplot of the missing data pattern | |

employee | Employee selection data | |

cbind.mids | Combine mids objects by columns | |

ic | Select incomplete cases | |

getfit | Extract list of fitted model | |

make.formulas | Creates a formulas argument | |

fdd | SE Fireworks disaster data | |

ifdo | Conditional imputation helper | |

make.method | Creates a method argument | |

cc | Select complete cases | |

is.mipo | Check for mipo object | |

getqbar | Extract estimate from mipo object | |

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

fdgs | Fifth Dutch growth study 2009 | |

make.post | Creates a post argument | |

make.visitSequence | Creates a visitSequence argument | |

make.predictorMatrix | Creates a predictorMatrix argument | |

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

leiden85 | Leiden 85+ study | |

is.mira | Check for mira object | |

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

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

fix.coef | Fix coefficients and update model | |

make.where | Creates a where argument | |

make.blocks | Creates a blocks argument | |

md.pattern | Missing data pattern | |

mdc | Graphical parameter for missing data plots. | |

is.mads | Check for mads object | |

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

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

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

mammalsleep | Mammal sleep data | |

is.mids | Check for mids object | |

make.blots | Creates a blots argument | |

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.jomoImpute | Multivariate multilevel imputation using jomo | |

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

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

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 | mice: Multivariate Imputation by Chained Equations | |

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

lm.mids | Linear regression for mids object | |

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

mids2mplus | Export mids object to Mplus | |

mice.impute.passive | Passive imputation | |

mids2spss | Export mids object to SPSS | |

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

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

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

nimp | Number of imputations per block | |

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

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

mipo | mipo: Multiple imputation pooled object | |

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

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

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

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

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

mice.impute.mnar.logreg | Imputation under MNAR mechanism by NARFCS | |

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

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

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

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

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

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

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

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

nic | Number of incomplete cases | |

print.mads | Print a mads object | |

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

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

quickpred | Quick selection of predictors from the data | |

name.blocks | Name imputation blocks | |

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

version | Echoes the package version number | |

toenail2 | Toenail data | |

mnar_demo_data | MNAR demo data | |

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

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

tbc | Terneuzen birth cohort | |

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

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

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

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

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

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

toenail | Toenail data | |

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

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

name.formulas | Name formula list elements | |

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

parlmice | Wrapper function that runs MICE in parallel | |

ncc | Number of complete cases | |

pattern | Datasets with various missing data patterns | |

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

selfreport | Self-reported and measured BMI | |

summary.mira | Summary of a mira object | |

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

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

rbind.mids | Combine mids objects by rows | |

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

print.mids | Print a mids object | |

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

pool | Combine estimates by Rubin's rules | |

potthoffroy | Potthoff-Roy data | |

nhanes | NHANES example - all variables numerical | |

walking | Walking disability data | |

windspeed | Subset of Irish wind speed data | |

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

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

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

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

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

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

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

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

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

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## Last month downloads

## Details

Type | Package |

Date | 2020-05-14 |

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 | 7.1.0 |

NeedsCompilation | yes |

Packaged | 2020-05-14 14:39:16 UTC; buurensv |

Repository | CRAN |

Date/Publication | 2020-05-14 15:20:03 UTC |

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

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

depends | R (>= 2.10.0) |

linkingto | Rcpp |

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

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