`install.packages('mice')`

74,074

3.13.0

GPL-2 | GPL-3

January 27th, 2021

ampute.default.freq

Default

`freq`

in `ampute`

D2

Compare two nested models using D2-statistic

D3

Compare two nested models using D3-statistic

D1

Compare two nested models using D1-statistic

ampute.default.patterns

Default

`patterns`

in `ampute`

ampute.default.odds

Default

`odds`

in `ampute()`

ampute.default.weights

Default

`weights`

in `ampute`

ampute.continuous

Multivariate amputation based on continuous probability functions

ampute

Generate missing data for simulation purposes

ampute.default.type

Default

`type`

in `ampute()`

as.mitml.result

Converts into a

`mitml.result`

objectampute.discrete

Multivariate amputation based on discrete probability functions

ampute.mcar

Multivariate amputation under a MCAR mechanism

brandsma

Brandsma school data used Snijders and Bosker (2012)

anova.mira

Compare several nested models

appendbreak

Appends specified break to the data

cci

Complete case indicator

boys

Growth of Dutch boys

employee

Employee selection data

as.mids

Converts an imputed dataset (long format) into a

`mids`

objectcomplete.mids

Extracts the completed data from a

`mids`

objectflux

Influx and outflux of multivariate missing data patterns

bwplot.mads

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

cbind.mids

Combine

`mids`

objects by columnscc

Select complete cases

fluxplot

Fluxplot of the missing data pattern

as.mira

Create a

`mira`

object from repeated analysesextend.formulas

Extends formula's with predictor matrix settings

is.mitml.result

Check for

`mitml.result`

objectgetqbar

Extract estimate from

`mipo`

objectgetfit

Extract list of fitted models

extend.formula

Extends a formula with predictors

estimice

Computes least squares parameters

construct.blocks

Construct blocks from

`formulas`

and `predictorMatrix`

leiden85

Leiden 85+ study

bwplot.mids

Box-and-whisker plot of observed and imputed data

cbind

Combine R objects by rows and columns

glance.mipo

Glance method to extract information from a `mipo` object

extractBS

Extract broken stick estimates from a

`lmer`

objectmake.blocks

Creates a

`blocks`

argumentmake.blots

Creates a

`blots`

argumentglm.mids

Generalized linear model for

`mids`

objectis.mira

Check for

`mira`

objectici

Incomplete case indicator

fix.coef

Fix coefficients and update model

is.mipo

Check for

`mipo`

objectmake.post

Creates a

`post`

argumentfdd

SE Fireworks disaster data

filter.mids

Subset rows of a

`mids`

objectmd.pairs

Missing data pattern by variable pairs

densityplot.mids

Density plot of observed and imputed data

fdgs

Fifth Dutch growth study 2009

ifdo

Conditional imputation helper

mice.impute.panImpute

Impute multilevel missing data using

`pan`

make.predictorMatrix

Creates a

`predictorMatrix`

argumentibind

Enlarge number of imputations by combining

`mids`

objectsmice.impute.norm.predict

Imputation by linear regression through prediction

make.visitSequence

Creates a

`visitSequence`

argumentmake.where

Creates a

`where`

argumentmatchindex

Find index of matched donor units

mammalsleep

Mammal sleep data

ic

Select incomplete cases

mice.impute.2l.bin

Imputation by a two-level logistic model using

`glmer`

make.formulas

Creates a

`formulas`

argumentmice.impute.2lonly.mean

Imputation of most likely value within the class

make.method

Creates a

`method`

argumentmice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.mean

Imputation by the mean

md.pattern

Missing data pattern

mice.impute.sample

Imputation by simple random sampling

mice.impute.logreg

Imputation by logistic regression

mice.impute.ri

Imputation by the random indicator method for nonignorable data

mice.impute.2l.lmer

Imputation by a two-level normal model using

`lmer`

is.mads

Check for

`mads`

objectmice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

is.mids

Check for

`mids`

objectfico

Fraction of incomplete cases among cases with observed

mice.impute.cart

Imputation by classification and regression trees

mira-class

Multiply imputed repeated analyses (

`mira`

)lm.mids

Linear regression for

`mids`

objectplot.mids

Plot the trace lines of the MICE algorithm

reexports

Objects exported from other packages

pattern

Datasets with various missing data patterns

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

selfreport

Self-reported and measured BMI

xyplot.mads

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

nhanes2

NHANES example - mixed numerical and discrete variables

pool.r.squared

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

nhanes

NHANES example - all variables numerical

pool.compare

Compare two nested models fitted to imputed data

mads-class

Multivariate amputed data set (

`mads`

)tidy.mipo

Tidy method to extract results from a `mipo` object

tbc

Terneuzen birth cohort

xyplot.mids

Scatterplot of observed and imputed data

mnar_demo_data

MNAR demo data

mice.impute.jomoImpute

Multivariate multilevel imputation using

`jomo`

mice.impute.passive

Passive imputation

mdc

Graphical parameter for missing data plots

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS

mice.impute.polr

Imputation of ordered data by polytomous regression

name.blocks

Name imputation blocks

mice.theme

Set the theme for the plotting Trellis functions

mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)

mice.impute.pmm

Imputation by predictive mean matching

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.2l.norm

Imputation by a two-level normal model

mice

mice: Multivariate Imputation by Chained Equations

mice.impute.2l.pan

Imputation by a two-level normal model using

`pan`

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.polyreg

Imputation of unordered data by polytomous regression

mice.impute.rf

Imputation by random forests

mice.impute.quadratic

Imputation of quadratic terms

parlmice

Wrapper function that runs MICE in parallel

name.formulas

Name formula list elements

norm.draw

Draws values of beta and sigma by Bayesian linear regression

pool.scalar

Multiple imputation pooling: univariate version

ncc

Number of complete cases

mipo

`mipo`

: Multiple imputation pooled objectmids2spss

Export

`mids`

object to SPSS.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

toenail

Toenail data

popmis

Hox pupil popularity data with missing popularity scores

print.mids

Print a

`mids`

objectprint.mads

Print a

`mads`

objectmids2mplus

Export

`mids`

object to Mplusnic

Number of incomplete cases

pool

Combine estimates by Rubin's rules

pops

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

potthoffroy

Potthoff-Roy data

summary.mira

Summary of a

`mira`

objectsqueeze

Squeeze the imputed values to be within specified boundaries.

supports.transparent

Supports semi-transparent foreground colors?

windspeed

Subset of Irish wind speed data

stripplot.mids

Stripplot of observed and imputed data

toenail2

Toenail data

version

Echoes the package version number

quickpred

Quick selection of predictors from the data

walking

Walking disability data

nimp

Number of imputations per block

with.mids

Evaluate an expression in multiple imputed datasets

rbind.mids

Combine

`mids`

objects by rows