Multivariate Imputation by Chained Equations

Documentation for package ‘mice’ version 3.16.0

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A B C D E F G H I L M N P Q R S T V W X misc

-- A --

ampute	Generate missing data for simulation purposes
anova.mira	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

-- B --

boys	Growth of Dutch boys
brandsma	Brandsma school data used Snijders and Bosker (2012)
bwplot	Box-and-whisker plot of observed and imputed data
bwplot.mads	Box-and-whisker plot of amputed and non-amputed data
bwplot.mids	Box-and-whisker plot of observed and imputed data

-- C --

cart	Imputation by classification and regression trees
cbind	Combine R objects by rows and columns
cc	Select complete cases
cci	Complete case indicator
complete	Extracts the completed data from a 'mids' object
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

-- D --

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	Density plot of observed and imputed data
densityplot.mids	Density plot of observed and imputed data

-- E --

employee	Employee selection data
estimice	Computes least squares parameters
extractBS	Extract broken stick estimates from a 'lmer' object

-- F --

fdd	SE Fireworks disaster data
fdd.pred	SE Fireworks disaster data
fdgs	Fifth Dutch growth study 2009
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

-- G --

getfit	Extract list of fitted models
getqbar	Extract estimate from 'mipo' object
glm.mids	Generalized linear model for 'mids' object

-- H --

hazard

Cumulative hazard rate or Nelson-Aalen estimator

-- I --

ibind	Enlarge number of imputations by combining 'mids' objects
ic	Select incomplete cases
ici	Incomplete case indicator
ici-method	Incomplete case indicator
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

-- L --

lasso.logreg	Imputation by direct use of lasso logistic regression
lasso.norm	Imputation by direct use of lasso linear regression
lasso.select.logreg	Imputation by indirect use of lasso logistic regression
lasso.select.norm	Imputation by indirect use of lasso linear regression
leiden85	Leiden 85+ study
lm.mids	Linear regression for 'mids' object

-- M --

mads-class	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
mammalsleep	Mammal sleep data
matchindex	Find index of matched donor units
md.pairs	Missing data pattern by variable pairs
md.pattern	Missing data pattern
mdc	Graphical parameter for missing data plots
mgg	Self-reported and measured BMI
mice	'mice': Multivariate Imputation by Chained Equations
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	Imputation by logistic regression
mice.impute.logreg.boot	Imputation by logistic regression using the bootstrap
mice.impute.mean	Imputation by the mean
mice.impute.midastouch	Imputation by predictive mean matching with distance aided donor selection
mice.impute.mnar.logreg	Imputation under MNAR mechanism by NARFCS
mice.impute.mnar.norm	Imputation under MNAR mechanism by NARFCS
mice.impute.mpmm	Imputation by multivariate predictive mean matching
mice.impute.norm	Imputation by Bayesian linear regression
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.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.theme	Set the theme for the plotting Trellis functions
mids	Multiply imputed data set ('mids')
mids-class	Multiply imputed data set ('mids')
mids2mplus	Export 'mids' object to Mplus
mids2spss	Export 'mids' object to SPSS
mira	Multiply imputed repeated analyses ('mira')
mira-class	Multiply imputed repeated analyses ('mira')
mnar.logreg	Imputation under MNAR mechanism by NARFCS
mnar.norm	Imputation under MNAR mechanism by NARFCS
mnar_demo_data	MNAR demo data
mpmm	Imputation by multivariate predictive mean matching

-- N --

name.blocks	Name imputation blocks
name.formulas	Name formula list elements
ncc	Number of complete cases
nelsonaalen	Cumulative hazard rate or Nelson-Aalen estimator
nhanes	NHANES example - all variables numerical
nhanes2	NHANES example - mixed numerical and discrete variables
nic	Number of incomplete cases
nimp	Number of imputations per block
norm	Imputation by Bayesian linear regression
norm.boot	Imputation by linear regression, bootstrap method
norm.draw	Draws values of beta and sigma by Bayesian linear regression
norm.nob	Imputation by linear regression without parameter uncertainty
norm.predict	Imputation by linear regression through prediction

-- P --

parlmice	Wrapper function that runs MICE in parallel
pattern	Datasets with various missing data patterns
pattern1	Datasets with various missing data patterns
pattern2	Datasets with various missing data patterns
pattern3	Datasets with various missing data patterns
pattern4	Datasets with various missing data patterns
plot.mids	Plot the trace lines of the MICE algorithm
pmm	Imputation by predictive mean matching
pool	Combine estimates by pooling rules
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.scalar	Multiple imputation pooling: univariate version
pool.scalar.syn	Multiple imputation pooling: univariate version
pool.syn	Combine estimates by pooling rules
popmis	Hox pupil popularity data with missing popularity scores
pops	Project on preterm and small for gestational age infants (POPS)
pops.pred	Project on preterm and small for gestational age infants (POPS)
potthoffroy	Potthoff-Roy data
print.mads	Print a 'mads' object
print.mice.anova	Print a 'mids' object
print.mice.anova.summary	Print a 'mids' object
print.mids	Print a 'mids' object
print.mira	Print a 'mids' object

-- Q --

quadratic	Imputation of quadratic terms
quickpred	Quick selection of predictors from the data

-- R --

rbind	Combine R objects by rows and columns
ri	Imputation by the random indicator method for nonignorable data

-- S --

selfreport	Self-reported and measured BMI
sleep	Mammal sleep data
squeeze	Squeeze the imputed values to be within specified boundaries.
stripplot	Stripplot of observed and imputed data
stripplot.mids	Stripplot of observed and imputed data
summary.mads	Summary of a 'mira' object
summary.mice.anova	Summary of a 'mira' object
summary.mids	Summary of a 'mira' object
summary.mira	Summary of a 'mira' object
supports.transparent	Supports semi-transparent foreground colors?

-- T --

tbc	Terneuzen birth cohort
tbc.target	Terneuzen birth cohort
terneuzen	Terneuzen birth cohort
toenail	Toenail data
toenail2	Toenail data
transparent	Supports semi-transparent foreground colors?

-- V --

version

Echoes the package version number

-- W --

walking	Walking disability data
windspeed	Subset of Irish wind speed data
with.mids	Evaluate an expression in multiple imputed datasets

-- X --

xyplot	Scatterplot of observed and imputed data
xyplot.mads	Scatterplot of amputed and non-amputed data against weighted sum scores
xyplot.mids	Scatterplot of observed and imputed data

-- misc --

.norm.draw	Draws values of beta and sigma by Bayesian linear regression
.pmm.match	Finds an imputed value from matches in the predictive metric (deprecated)
2l.pan	Imputation by a two-level normal model using 'pan'
2lonly.mean	Imputation of most likely value within the class
2lonly.norm	Imputation at level 2 by Bayesian linear regression
2lonly.pmm	Imputation at level 2 by predictive mean matching