`install.packages('mice')`

44,040

3.6.0

GPL-2 | GPL-3

July 10th, 2019

D1

Compare two nested models using D1-statistic

ampute.default.freq

Default

`freq`

in `ampute`

ampute.default.odds

Default

`odds`

in `ampute()`

ampute.default.type

Default

`type`

in `ampute()`

D2

Compare two nested models using D2-statistic

ampute.default.weights

Default

`weights`

in `ampute`

ampute.default.patterns

Default

`patterns`

in `ampute`

D3

Compare two nested models using D3-statistic

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute

Generate Missing Data for Simulation Purposes

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

as.mids

Converts an multiply imputed dataset (long format) into a

`mids`

objectas.mira

Create a

`mira`

object from repeated analysesappendbreak

Appends specified break to the data

anova.mira

Compare several nested models

ampute.mcar

Multivariate Amputation In A MCAR Manner

employee

Employee selection data

estimice

Computes least squares parameters

as.mitml.result

Converts into a

`mitml.result`

objectcbind.mids

Combine

`mids`

objects by columnsboys

Growth of Dutch boys

cc

Select complete cases

cbind

Combine R Objects by Rows and Columns

extend.formula

Extends a formula with predictors

bwplot.mids

Box-and-whisker plot of observed and imputed data

densityplot.mids

Density plot of observed and imputed data

construct.blocks

Construct blocks from

`formulas`

and `predictorMatrix`

fluxplot

Fluxplot of the missing data pattern

bwplot.mads

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

brandsma

Brandsma school data used Snijders and Bosker (2012)

extractBS

Extract broken stick estimates from a

`lmer`

objectcci

Complete case indicator

fdd

SE Fireworks disaster data

fdgs

Fifth Dutch growth study 2009

fico

Fraction of incomplete cases among cases with observed

ibind

Enlarge number of imputations by combining

`mids`

objectsici

Incomplete case indicator

extend.formulas

Extends formula's with predictor matrix settings

is.mipo

Check for

`mipo`

objectis.mira

Check for

`mira`

objectmake.blocks

Creates a

`blocks`

argumentifdo

Conditional imputation helper

getqbar

Extract estimate from

`mipo`

objectcomplete

Extracts the completed data from a

`mids`

objectglm.mids

Generalized linear model for

`mids`

objectmake.blots

Creates a

`blots`

argumentmake.formulas

Creates a

`formulas`

argumentgetfit

Extract list of fitted model

ic

Select incomplete cases

is.mitml.result

Check for

`mitml.result`

objectleiden85

Leiden 85+ study

mice.impute.2l.lmer

Imputation by a two-level normal model using

`lmer`

mdc

Graphical parameter for missing data plots.

md.pattern

Missing data pattern

mice.impute.lda

Imputation by linear discriminant analysis

fix.coef

Fix coefficients and update model

flux

Influx and outflux of multivariate missing data patterns

is.mids

Check for

`mids`

objectis.mads

Check for

`mads`

objectmake.post

Creates a

`post`

argumentmice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.logreg

Imputation by logistic regression

mice.impute.2l.pan

Imputation by a two-level normal model using

`pan`

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

lm.mids

Linear regression for

`mids`

objectmice.impute.2lonly.mean

Imputation of the mean within the class

mice.impute.2l.norm

Imputation by a two-level normal model

make.visitSequence

Creates a

`visitSequence`

argumentmake.method

Creates a

`method`

argumentmake.where

Creates a

`where`

argumentmice

mice: Multivariate Imputation by Chained Equations

mads-class

Multivariate Amputed Data Set (

`mads`

)mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.quadratic

Imputation of quadratic terms

mice.impute.rf

Imputation by random forests

mice.impute.ri

Imputation by the random indicator method for nonignorable data

nimp

Number of imputations per block

mice.impute.sample

Imputation by simple random sampling

norm.draw

Draws values of beta and sigma by Bayesian linear regression

mice.impute.norm

Imputation by Bayesian linear regression

pattern

Datasets with various missing data patterns

parlmice

Wrapper function that runs MICE in parallel

mice.impute.2l.bin

Imputation by a two-level logistic model using

`glmer`

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.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.pmm

Imputation by predictive mean matching

mice.theme

Set the theme for the plotting Trellis functions

mice.impute.passive

Passive imputation

nhanes2

NHANES example - mixed numerical and discrete variables

mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)

summary.mira

Summary of a

`mira`

objectsupports.transparent

Supports semi-transparent foreground colors?

squeeze

Squeeze the imputed values to be within specified boundaries.

stripplot.mids

Stripplot of observed and imputed data

mice.impute.mean

Imputation by the mean

make.predictorMatrix

Creates a

`predictorMatrix`

argumentmice.impute.cart

Imputation by classification and regression trees

mice.impute.norm.predict

Imputation by linear regression through prediction

toenail

Toenail data

tbc

Terneuzen birth cohort

nhanes

NHANES example - all variables numerical

mids2mplus

Export

`mids`

object to Mplusmice.impute.panImpute

Impute multilevel missing data using

`pan`

.pmm.match

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

mids-class

Multiply imputed data set (

`mids`

)nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator

plot.mids

Plot the trace lines of the MICE algorithm

nic

Number of incomplete cases

pool

Combine estimates by Rubin's rules

mammalsleep

Mammal sleep data

pool.compare

Compare two nested models fitted to imputed data

xyplot.mads

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

xyplot.mids

Scatterplot of observed and imputed data

mice.impute.polyreg

Imputation of unordered data by polytomous regression

md.pairs

Missing data pattern by variable pairs

name.blocks

Name imputation blocks

mice.impute.polr

Imputation of ordered data by polytomous regression

mira-class

Multiply imputed repeated analyses (

`mira`

)mids2spss

Export

`mids`

object to SPSSmipo

`mipo`

: Multiple imputation pooled objectmice.impute.jomoImpute

Multivariate multilevel imputation using

`jomo`

pool.r.squared

Pooling: R squared

version

Echoes the package version number

potthoffroy

Potthoff-Roy data

walking

Walking disability data

print.mids

Print a

`mids`

objectpops

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

rbind.mids

Combine

`mids`

objects by rowsname.formulas

Name formula list elements

popmis

Hox pupil popularity data with missing popularity scores

ncc

Number of complete cases

with.mids

Evaluate an expression in multiple imputed datasets

selfreport

Self-reported and measured BMI

windspeed

Subset of Irish wind speed data

pool.scalar

Multiple imputation pooling: univariate version

print.mads

Print a

`mads`

objectquickpred

Quick selection of predictors from the data