`install.packages('mice')`

60,676

3.4.0

GPL-2 | GPL-3

March 7th, 2019

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

bwplot.mads

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

brandsma

Brandsma school data used Snijders and Bosker (2012)

cbind.mids

Combine

`mids`

objects by columnscc

Select complete cases

extractBS

Extract broken stick estimates from a

`lmer`

objectD1

Compare two nested models using D1-statistic

D2

Compare two nested models using D2-statistic

fdd

SE Fireworks disaster data

cci

Complete case indicator

complete

Extracts the completed data from a

`mids`

objectextend.formula

Extends a formula with predictors

ampute.default.type

Default

`type`

in `ampute()`

ampute.default.weights

Default

`weights`

in `ampute`

anova.mira

Compare several nested models

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

ibind

Enlarge number of imputations by combining

`mids`

objectsextend.formulas

Extends formula's with predictor matrix settings

appendbreak

Appends specified break to the data

fix.coef

Fix coefficients and update model

ic

Select incomplete cases

is.mads

Check for

`mads`

objectis.mids

Check for

`mids`

objectmake.blots

Creates a

`blots`

argumentmake.blocks

Creates a

`blocks`

argumentmake.visitSequence

Creates a

`visitSequence`

argumentmake.where

Creates a

`where`

argumentampute.mcar

Multivariate Amputation In A MCAR Manner

mice.impute.lda

Imputation by linear discriminant analysis

as.mitml.result

Converts into a

`mitml.result`

objectampute.default.odds

Default

`odds`

in `ampute()`

mice.impute.logreg

Imputation by logistic regression

ampute.default.patterns

Default

`patterns`

in `ampute`

make.formulas

Creates a

`formulas`

argumentmake.method

Creates a

`method`

argumentmammalsleep

Mammal sleep data

mice.impute.mean

Imputation by the mean

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

md.pairs

Missing data pattern by variable pairs

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

flux

Influx and outflux of multivariate missing data patterns

mice.impute.quadratic

Imputation of quadratic terms

mice.impute.rf

Imputation by random forests

nhanes2

NHANES example - mixed numerical and discrete variables

mice.theme

Set the theme for the plotting Trellis functions

mice.impute.norm.predict

Imputation by linear regression through prediction

mice.impute.panImpute

Impute multilevel missing data using

`pan`

mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)

cbind

Combine R Objects by Rows and Columns

bwplot.mids

Box-and-whisker plot of observed and imputed data

getqbar

Extract estimate from

`mipo`

objectnic

Number of incomplete cases

fdgs

Fifth Dutch growth study 2009

ici

Incomplete case indicator

fico

Fraction of incomplete cases among cases with observed

ifdo

Conditional imputation helper

parlmice

Wrapper function that runs MICE in parallel

nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator

nhanes

NHANES example - all variables numerical

boys

Growth of Dutch boys

plot.mids

Plot the trace lines of the MICE algorithm

.pmm.match

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

estimice

Computes least squares parameters

glm.mids

Generalized linear model for

`mids`

objectis.mitml.result

Check for

`mitml.result`

objectemployee

Employee selection data

leiden85

Leiden 85+ study

with.mids

Evaluate an expression in multiple imputed datasets

mice

mice: Multivariate Imputation by Chained Equations

pattern

Datasets with various missing data patterns

mice.impute.2l.bin

Imputation by a two-level logistic model using

`glmer`

mice.impute.cart

Imputation by classification and regression trees

mads-class

Multivariate Amputed Data Set (

`mads`

)lm.mids

Linear regression for

`mids`

objectmice.impute.jomoImpute

Multivariate multilevel imputation using

`jomo`

summary.mira

Summary of a

`mira`

objectmice.impute.passive

Passive imputation

mice.impute.pmm

Imputation by predictive mean matching

supports.transparent

Supports semi-transparent foreground colors?

xyplot.mads

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

mice.impute.2l.pan

Imputation by a two-level normal model using

`pan`

mice.impute.2lonly.mean

Imputation of the mean within the class

D3

Compare two nested models using D3-statistic

mids-class

Multiply imputed data set (

`mids`

)mice.impute.polr

Imputation of ordered data by polytomous regression

mice.impute.2l.lmer

Imputation by a two-level normal model using

`lmer`

mice.impute.2l.norm

Imputation by a two-level normal model

mids2mplus

Export

`mids`

object to Mplusampute

Generate Missing Data for Simulation Purposes

mice.impute.polyreg

Imputation of unordered data by polytomous regression

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

walking

Walking disability data

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.ri

Imputation by the random indicator method for nonignorable data

windspeed

Subset of Irish wind speed data

mice.impute.sample

Imputation by simple random sampling

as.mids

Converts an multiply imputed dataset (long format) into a

`mids`

objectmids2spss

Export

`mids`

object to SPSSas.mira

Create a

`mira`

object from repeated analysesmipo

`mipo`

: Multiple imputation pooled objectname.formulas

Name formula list elements

construct.blocks

Construct blocks from

`formulas`

and `predictorMatrix`

pool

Combine estimates by Rubin's rules

ncc

Number of complete cases

nimp

Number of imputations per block

potthoffroy

Potthoff-Roy data

norm.draw

Draws values of beta and sigma by Bayesian linear regression

popmis

Hox pupil popularity data with missing popularity scores

densityplot.mids

Density plot of observed and imputed data

pops

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

pool.compare

Compare two nested models fitted to imputed data

print.mids

Print a

`mids`

objecttbc

Terneuzen birth cohort

rbind.mids

Combine

`mids`

objects by rowsselfreport

Self-reported and measured BMI

fluxplot

Fluxplot of the missing data pattern

getfit

Extract list of fitted model

version

Echoes the package version number

is.mipo

Check for

`mipo`

objectis.mira

Check for

`mira`

objectmake.post

Creates a

`post`

argumentmake.predictorMatrix

Creates a

`predictorMatrix`

argumentprint.mads

Print a

`mads`

objectmd.pattern

Missing data pattern

mdc

Graphical parameter for missing data plots.

quickpred

Quick selection of predictors from the data

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

name.blocks

Name imputation blocks

mira-class

Multiply imputed repeated analyses (

`mira`

)pool.r.squared

Pooling: R squared

pool.scalar

Multiple imputation pooling: univariate version

squeeze

Squeeze the imputed values to be within specified boundaries.

stripplot.mids

Stripplot of observed and imputed data

xyplot.mids

Scatterplot of observed and imputed data

ampute.default.freq

Default

`freq`

in `ampute`