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mice (version 2.25)

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

83,091

Version

2.25

License

GPL-2 | GPL-3

Homepage

http://www.stefvanbuuren.nl

Maintainer

Stef van Buuren

Last Published

November 9th, 2015

Functions in mice (2.25)

mice.impute.norm.predict

Imputation by linear regression, prediction method

Multivariate Imputation by Chained Equations (Iteration Step)

Multiple imputation pooling

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

densityplot.mids

Density plot of observed and imputed data

mice.impute.2l.norm

Imputation by a two-level normal model

Fluxplot of the missing data pattern

Complete cases n

Influx and outflux of multivariate missing data patterns

Generalized linear model for mids object

Create a mira object from repeated analyses

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.quadratic

Imputation of quadratric terms

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

Box-and-whisker plot of observed and imputed data

Conversion of a imputed data set (long form) to a mids object

Missing data pattern by variable pairs

mice.impute.norm.nob

Imputation by linear regression (non Bayesian)

mice.impute.polyreg

Imputation by polytomous regression - unordered

mice.impute.2l.pan

Imputation by a two-level normal model using pan

Fifth Dutch growth study 2009

mice.impute.pmm

Imputation by predictive mean matching

Check for mira object

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

Appends specified break to the data

Scatterplot of observed and imputed data

Growth of Dutch boys

Columnwise combination of a mids object.

NHANES example - all variables numerical

SE Fireworks disaster data

Creates imputed data sets from a mids object

Multivariate Imputation by Chained Equations (MICE)

Linear regression for mids object

mice.impute.cart

Imputation by classification and regression trees

mice.impute.polr

Imputation by polytomous regression - ordered

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.mean

Imputation by the mean

Self-reported and measured BMI

Complete case indicator

Evaluate an expression in multiple imputed datasets

Squeeze the imputed values to be within specified boundaries.

Incomplete cases

Set the theme for the plotting Trellis functions

Multiple imputation pooling: univariate version

mice.impute.2lonly.mean

Imputation of the mean within the class

Graphical parameter for missing data plots.

Summary of a mira object

Conditional imputation helper

mice.impute.fastpmm

Imputation by fast predictive mean matching

Extracts fit objects from mira object

Incomplete case indicator

Leiden 85+ study

Export mids object to Mplus

Cumulative hazard rate or Nelson-Aalen estimator

Combine imputations fitted to the same data

Export mids object to SPSS

Imputation by the random indicator method for nonignorable data

Incomplete cases n

Multiply imputed repeated analyses (mira)

Imputation by random forests

Multiply imputed pooled analysis (mipo)

Missing data pattern

NHANES example - mixed numerical and discrete variables

Datasets with various missing data patterns

Hox pupil popularity data with missing popularity scores

Print a mids object

Quick selection of predictors from the data

Potthoff-Roy data

Terneuzen birth cohort

Extract broken stick estimates from a lmer object

Check for mipo object

supports.transparent

Supports semi-transparent foreground colors?

Multiply imputed data set (mids)

Mammal sleep data

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

Compare two nested models fitted to imputed data

Walking disability data

Stripplot of observed and imputed data

Plot the trace lines of the MICE algorithm

Check for mids object

mice.impute.passive

Passive imputation

Echoes the package version number

Subset of Irish wind speed data

Draws values of beta and sigma by Bayesian linear regression

Pooling: R squared

mice.impute.logreg

Imputation by logistic regression

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.sample

Imputation by simple random sampling

Fraction of incomplete cases among cases with observed

Rowwise combination of a mids object.