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mice (version 2.14)

Multivariate Imputation by Chained Equations

Description

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. 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.

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Version

Install

install.packages('mice')

Monthly Downloads

74,074

Version

2.14

License

GPL-2 | GPL-3

Maintainer

Last Published

March 19th, 2013

Functions in mice (2.14)

mipo

Multiply Imputed Pooled Analysis
md.pattern

Missing Data Pattern
mice.internal

Internal mice functions
flux

Influx and outflux of multivatiate missing data patterns
mice.impute.passive

Passive Imputation
cci

Extracts (in)complete case indicator
mice.impute.2lonly.pmm

Imputation at Level 2 by Predictive Mean Matching
mice.impute.norm.boot

Imputation by Linear Regression, Bootstrap Method
mice.impute.norm.predict

Imputation by Linear Regression, Prediction Method
mice.auxiliary

Auxiliary functions used in FIMD
mdc

Graphical parameter for missing data plots.
mice.impute.2lonly.norm

Imputation at Level 2 by Bayesian Linear Regression
mids2spss

Export Multiply Imputed Data to SPSS
mids2mplus

Export Multiply Imputed Data to Mplus
nhanes

NHANES example - all variables numerical
glm.mids

Generalized Linear Model for Multiply Imputed Data
lm.mids

Linear Regression on Multiply Imputed Data
nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator
mice.impute.polyreg

Imputation by Polytomous Regression
mice.impute.sample

Imputation by Simple Random Sampling
mice.impute.2l.pan

Imputation by a Two-Level Normal Model using pan
mice.impute.lda

Imputation by Linear Discriminant Analysis
md.pairs

Missing data pattern by variable pairs
mice

Multivariate Imputation by Chained Equations (MICE)
mira

Multiply Imputed Repeated Analyses
quickpred

Quick selection of predictors from the data
cc

Extracts complete and incomplete cases
mice.impute.quadratic

Imputation of quadratric terms
ccn

Number of (in)complete cases
getfit

Extracts fit objects from mira object
mice.impute.pmm

Imputation by Predictive Mean Matching
nhanes2

NHANES example - mixed numerical and discrete variables
complete

Creates a Complete Flat File from a Multiply Imputed Data Set
mice.impute.mean

Imputation by the Mean
mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)
mids

Multiply Imputed Data Set
pool.r.squared

Pooling: R squared
mice.impute.norm

Imputation by Bayesian Linear Regression
pool

Multiple Imputation Pooling
with.mids

Evaluate an expression in multiple imputed datasets
mice.impute.logreg

Multiple Imputation by Logistic Regression
supports.transparent

Does the current graphic device support semi-transparent foreground colors?
stripplot

Box-and-whisker plot, stripplot, density plot and scatterplot for imputed data
version

Echoes the package version number