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mice (version 3.14.0)

Multivariate Imputation by Chained Equations

Description

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . 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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Install

install.packages('mice')

Monthly Downloads

92,351

Version

3.14.0

License

GPL-2 | GPL-3

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Repository

https://github.com/amices/mice

Homepage

https://amices.org/mice/

Maintainer

Stef van Buuren

Last Published

November 24th, 2021

Functions in mice (3.14.0)

ampute.continuous

Multivariate amputation based on continuous probability functions

Compare two nested models using D3-statistic

Generate missing data for simulation purposes

Compare two nested models using D1-statistic

ampute.default.weights

Default weights in ampute

ampute.default.type

Default type in ampute()

ampute.default.patterns

Default patterns in ampute

ampute.default.odds

Default odds in ampute()

ampute.default.freq

Default freq in ampute

Compare two nested models using D2-statistic

Compare several nested models

Brandsma school data used Snijders and Bosker (2012)

Box-and-whisker plot of amputed and non-amputed data

Fix coefficients and update model

Select complete cases

Combine mids objects by columns

Subset rows of a mids object

Fifth Dutch growth study 2009

Check for mads object

Check for mids object

Check for mipo object

Check for mira object

Fraction of incomplete cases among cases with observed

Create a mira object from repeated analyses

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

Extracts the completed data from a mids object

Complete case indicator

Extract estimate from mipo object

Extract list of fitted models

Multivariate amputed data set (mads)

Linear regression for mids object

Appends specified break to the data

Extends a formula with predictors

extend.formulas

Extends formula's with predictor matrix settings

construct.blocks

Construct blocks from formulas and predictorMatrix

Extract broken stick estimates from a lmer object

densityplot.mids

Density plot of observed and imputed data

Multivariate amputation under a MCAR mechanism

ampute.discrete

Multivariate amputation based on discrete probability functions

Employee selection data

is.mitml.result

Check for mitml.result object

Computes least squares parameters

Incomplete case indicator

Mammal sleep data

Leiden 85+ study

Conditional imputation helper

as.mitml.result

Converts into a mitml.result object

SE Fireworks disaster data

Find index of matched donor units

make.visitSequence

Creates a visitSequence argument

Creates a where argument

Growth of Dutch boys

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.cart

Imputation by classification and regression trees

mice.impute.lasso.select.logreg

Imputation by indirect use of lasso logistic regression

mice.impute.lasso.select.norm

Imputation by indirect use of lasso linear regression

Creates a post argument

make.predictorMatrix

Creates a predictorMatrix argument

mice.impute.2lonly.mean

Imputation of most likely value within the class

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.lasso.logreg

Imputation by direct use of lasso logistic regression

Enlarge number of imputations by combining mids objects

Box-and-whisker plot of observed and imputed data

Select incomplete cases

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Combine R objects by rows and columns

mice.impute.norm

Imputation by Bayesian linear regression

Name imputation blocks

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

Export mids object to Mplus

Export mids object to SPSS

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.norm.predict

Imputation by linear regression through prediction

mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS

Imputation by random forests

Imputation by the random indicator method for nonignorable data

Pools R^2 of m models fitted to multiply-imputed data

Multiple imputation pooling: univariate version

Creates a method argument

Creates a formulas argument

Influx and outflux of multivariate missing data patterns

Objects exported from other packages

Combine mids objects by rows

mice: Multivariate Imputation by Chained Equations

Plot the trace lines of the MICE algorithm

Number of complete cases

Name formula list elements

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

Missing data pattern

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

Graphical parameter for missing data plots

Finds an imputed value from matches in the predictive metric (deprecated)

mice.impute.2l.norm

Imputation by a two-level normal model

mice.impute.mean

Imputation by the mean

Tidy method to extract results from a `mipo` object

Wrapper function that runs MICE in parallel

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.logreg

Imputation by logistic regression

mice.impute.passive

Passive imputation

mice.impute.panImpute

Impute multilevel missing data using pan

Print a mids object

Potthoff-Roy data

Scatterplot of observed and imputed data

Datasets with various missing data patterns

Subset of Irish wind speed data

Walking disability data

mice.impute.lasso.norm

Imputation by direct use of lasso linear regression

Multivariate Imputation by Chained Equations (Iteration Step)

Draws values of beta and sigma by Bayesian linear regression

Number of imputations per block

Combine estimates by pooling rules

Print a mads object

Quick selection of predictors from the data

mice.impute.sample

Imputation by simple random sampling

Compare two nested models fitted to imputed data

Self-reported and measured BMI

Fluxplot of the missing data pattern

supports.transparent

Supports semi-transparent foreground colors?

Squeeze the imputed values to be within specified boundaries.

Terneuzen birth cohort

Set the theme for the plotting Trellis functions

mice.impute.quadratic

Imputation of quadratic terms

NHANES example - all variables numerical

Multiply imputed data set (mids)

Cumulative hazard rate or Nelson-Aalen estimator

Summary of a mira object

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Stripplot of observed and imputed data

Evaluate an expression in multiple imputed datasets

Scatterplot of amputed and non-amputed data against weighted sum scores

Glance method to extract information from a `mipo` object

Creates a blocks argument

Jamshidian and Jalal's Non-Parametric MCAR Test

Creates a blots argument

Generalized linear model for mids object

Missing data pattern by variable pairs

mice.impute.polr

Imputation of ordered data by polytomous regression

mice.impute.pmm

Imputation by predictive mean matching

Number of incomplete cases

Hox pupil popularity data with missing popularity scores

NHANES example - mixed numerical and discrete variables

Multiply imputed repeated analyses (mira)

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

mipo: Multiple imputation pooled object

Echoes the package version number