mice3.17.0 package

Multivariate Imputation by Chained Equations

ampute.continuous

Multivariate amputation based on continuous probability functions

ampute.default.freq

Default freq in ampute

ampute.default.odds

Default odds in ampute()

ampute.default.patterns

Default patterns in ampute

ampute.default.type

Default type in ampute()

ampute.default.weights

Default weights in ampute

ampute.discrete

Multivariate amputation based on discrete probability functions

ampute.mcar

Multivariate amputation under a MCAR mechanism

ampute

Generate missing data for simulation purposes

anova

Compare several nested models

appendbreak

Appends specified break to the data

as.mids

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

as.mira

Create a mira object from repeated analyses

as.mitml.result

Converts into a mitml.result object

bwplot.mads

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

bwplot.mids

Box-and-whisker plot of observed and imputed data

cbind

Combine R objects by rows and columns

cc

Select complete cases

cci

Complete case indicator

complete.mids

Extracts the completed data from a mids object

construct.blocks

Construct blocks from formulas and predictorMatrix

convergence

Computes convergence diagnostics for a mids object

D1

Compare two nested models using D1-statistic

D2

Compare two nested models using D2-statistic

D3

Compare two nested models using D3-statistic

densityplot.mids

Density plot of observed and imputed data

estimice

Computes least squares parameters

extend.formula

Extends a formula with predictors

extend.formulas

Extends formula's with predictor matrix settings

extractBS

Extract broken stick estimates from a lmer object

fico

Fraction of incomplete cases among cases with observed

filter.mids

Subset rows of a mids object

fix.coef

Fix coefficients and update model

flux

Influx and outflux of multivariate missing data patterns

fluxplot

Fluxplot of the missing data pattern

futuremice

Wrapper function that runs MICE in parallel

getfit

Extract list of fitted models

getqbar

Extract estimate from mipo object

glance.mipo

Glance method to extract information from a mipo object

glm.mids

Generalized linear model for mids object

ibind

Enlarge number of imputations by combining mids objects

ic

Select incomplete cases

ici

Incomplete case indicator

ifdo

Conditional imputation helper

is.mads

Check for mads object

is.mids

Check for mids object

is.mipo

Check for mipo object

is.mira

Check for mira object

is.mitml.result

Check for mitml.result object

lm.mids

Linear regression for mids object

mads

Multivariate amputed data set (mads)

make.blocks

Creates a blocks argument

make.blots

Creates a blots argument

make.formulas

Creates a formulas argument

make.method

Creates a method argument

make.post

Creates a post argument

make.predictorMatrix

Creates a predictorMatrix argument

make.visitSequence

Creates a visitSequence argument

make.where

Creates a where argument

matchindex

Find index of matched donor units

mcar

Jamshidian and Jalal's Non-Parametric MCAR Test

md.pairs

Missing data pattern by variable pairs

md.pattern

Missing data pattern

mdc

Graphical parameter for missing data plots

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

mice.impute.2l.norm

Imputation by a two-level normal model

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice.impute.2lonly.mean

Imputation of most likely value within the class

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.cart

Imputation by classification and regression trees

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.lasso.logreg

Imputation by direct use of lasso logistic regression

mice.impute.lasso.norm

Imputation by direct use of lasso linear regression

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

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.logreg

Imputation by logistic regression

mice.impute.mean

Imputation by the mean

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selec...

mice.impute.mnar

Imputation under MNAR mechanism by NARFCS

mice.impute.mpmm

Imputation by multivariate predictive mean matching

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.norm.predict

Imputation by linear regression through prediction

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.panImpute

Impute multilevel missing data using pan

mice.impute.passive

Passive imputation

mice.impute.pmm

Imputation by predictive mean matching

mice.impute.polr

Imputation of ordered data by polytomous regression

mice.impute.polyreg

Imputation of unordered data by polytomous regression

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

mice.impute.sample

Imputation by simple random sampling

mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)

mice

mice: Multivariate Imputation by Chained Equations

mice.theme

Set the theme for the plotting Trellis functions

mids

Multiply imputed data set (mids)

mids2mplus

Export mids object to Mplus

mids2spss

Export mids object to SPSS

mipo

mipo: Multiple imputation pooled object

mira

Create an object of class "mira"

name.blocks

Name imputation blocks

name.formulas

Name formula list elements

ncc

Number of complete cases

nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator

nic

Number of incomplete cases

nimp

Number of imputations per block

norm.draw

Draws values of beta and sigma by Bayesian linear regression

parlmice

Wrapper function that runs MICE in parallel

pattern

Datasets with various missing data patterns

pmm.match

Finds an imputed value from matches in the predictive metric (deprecat...

pool.compare

Compare two nested models fitted to imputed data

pool.r.squared

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

pool

Combine estimates by pooling rules

pool.scalar

Multiple imputation pooling: univariate version

pool.table

Combines estimates from a tidy table

print

Print a mira object

quickpred

Quick selection of predictors from the data

reexports

Objects exported from other packages

squeeze

Squeeze the imputed values to be within specified boundaries.

stripplot.mids

Stripplot of observed and imputed data

summary

Summary of a mira object

supports.transparent

Supports semi-transparent foreground colors?

tidy.mipo

Tidy method to extract results from a mipo object

version

Echoes the package version number

with.mids

Evaluate an expression in multiple imputed datasets

xyplot.mads

Scatterplot of amputed and non-amputed data against weighted sum score...

xyplot.mids

Scatterplot of observed and imputed data

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. 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.

  • Maintainer: Stef van Buuren
  • License: GPL (>= 2)
  • Last published: 2024-11-27