mice v3.9.0

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Multivariate Imputation by Chained Equations

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.

Functions in mice

Name Description
ampute Generate Missing Data for Simulation Purposes
boys Growth of Dutch boys
as.mitml.result Converts into a mitml.result object
bwplot.mads Box-and-whisker plot of amputed and non-amputed data
as.mira Create a mira object from repeated analyses
ampute.mcar Multivariate Amputation In A MCAR Manner
complete.mids Extracts the completed data from a mids object
ampute.discrete Multivariate Amputation Based On Discrete Probability Functions
as.mids Converts an multiply imputed dataset (long format) into a mids object
brandsma Brandsma school data used Snijders and Bosker (2012)
anova.mira Compare several nested models
bwplot.mids Box-and-whisker plot of observed and imputed data
cci Complete case indicator
appendbreak Appends specified break to the data
cbind Combine R Objects by Rows and Columns
estimice Computes least squares parameters
extend.formula Extends a formula with predictors
extractBS Extract broken stick estimates from a lmer object
extend.formulas Extends formula's with predictor matrix settings
ici Incomplete case indicator
ibind Enlarge number of imputations by combining mids objects
is.mitml.result Check for mitml.result object
fluxplot Fluxplot of the missing data pattern
employee Employee selection data
cbind.mids Combine mids objects by columns
ic Select incomplete cases
getfit Extract list of fitted model
make.formulas Creates a formulas argument
fdd SE Fireworks disaster data
ifdo Conditional imputation helper
make.method Creates a method argument
cc Select complete cases
is.mipo Check for mipo object
getqbar Extract estimate from mipo object
fico Fraction of incomplete cases among cases with observed
fdgs Fifth Dutch growth study 2009
make.post Creates a post argument
make.visitSequence Creates a visitSequence argument
make.predictorMatrix Creates a predictorMatrix argument
glm.mids Generalized linear model for mids object
leiden85 Leiden 85+ study
is.mira Check for mira object
construct.blocks Construct blocks from formulas and predictorMatrix
densityplot.mids Density plot of observed and imputed data
fix.coef Fix coefficients and update model
make.where Creates a where argument
make.blocks Creates a blocks argument
md.pattern Missing data pattern
mdc Graphical parameter for missing data plots.
is.mads Check for mads object
flux Influx and outflux of multivariate missing data patterns
md.pairs Missing data pattern by variable pairs
mice.impute.cart Imputation by classification and regression trees
mammalsleep Mammal sleep data
is.mids Check for mids object
make.blots Creates a blots argument
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.jomoImpute Multivariate multilevel imputation using jomo
mice.impute.panImpute Impute multilevel missing data using pan
mads-class Multivariate Amputed Data Set (mads)
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 mice: Multivariate Imputation by Chained Equations
mice.impute.2l.norm Imputation by a two-level normal model
lm.mids Linear regression for mids object
mice.impute.2l.pan Imputation by a two-level normal model using pan
mids2mplus Export mids object to Mplus
mice.impute.passive Passive imputation
mids2spss Export mids object to SPSS
mice.impute.lda Imputation by linear discriminant analysis
mice.impute.logreg.boot Imputation by logistic regression using the bootstrap
mice.impute.2lonly.mean Imputation of most likely value within the class
nimp Number of imputations per block
mira-class Multiply imputed repeated analyses (mira)
mice.impute.pmm Imputation by predictive mean matching
mipo mipo: Multiple imputation pooled object
mice.impute.polr Imputation of ordered data by polytomous regression
mice.impute.rf Imputation by random forests
mice.impute.norm.nob Imputation by linear regression without parameter uncertainty
norm.draw Draws values of beta and sigma by Bayesian linear regression
mice.impute.logreg Imputation by logistic regression
mice.impute.mnar.logreg Imputation under MNAR mechanism by NARFCS
mice.impute.midastouch Imputation by predictive mean matching with distance aided donor selection
mice.impute.mean Imputation by the mean
mice.impute.sample Imputation by simple random sampling
mice.impute.ri Imputation by the random indicator method for nonignorable data
mice.impute.norm.predict Imputation by linear regression through prediction
nhanes2 NHANES example - mixed numerical and discrete variables
.pmm.match Finds an imputed value from matches in the predictive metric (deprecated)
plot.mids Plot the trace lines of the MICE algorithm
nic Number of incomplete cases
print.mads Print a mads object
popmis Hox pupil popularity data with missing popularity scores
mice.mids Multivariate Imputation by Chained Equations (Iteration Step)
quickpred Quick selection of predictors from the data
name.blocks Name imputation blocks
pops Project on preterm and small for gestational age infants (POPS)
version Echoes the package version number
toenail2 Toenail data
mnar_demo_data MNAR demo data
pool.r.squared Pooling: R squared
mice.impute.polyreg Imputation of unordered data by polytomous regression
tbc Terneuzen birth cohort
xyplot.mids Scatterplot of observed and imputed data
mice.theme Set the theme for the plotting Trellis functions
squeeze Squeeze the imputed values to be within specified boundaries.
mice.impute.quadratic Imputation of quadratic terms
mice.impute.norm Imputation by Bayesian linear regression
pool.scalar Multiple imputation pooling: univariate version
toenail Toenail data
mice.impute.norm.boot Imputation by linear regression, bootstrap method
stripplot.mids Stripplot of observed and imputed data
name.formulas Name formula list elements
mids-class Multiply imputed data set (mids)
parlmice Wrapper function that runs MICE in parallel
ncc Number of complete cases
pattern Datasets with various missing data patterns
with.mids Evaluate an expression in multiple imputed datasets
selfreport Self-reported and measured BMI
summary.mira Summary of a mira object
xyplot.mads Scatterplot of amputed and non-amputed data against weighted sum scores
supports.transparent Supports semi-transparent foreground colors?
rbind.mids Combine mids objects by rows
nelsonaalen Cumulative hazard rate or Nelson-Aalen estimator
print.mids Print a mids object
pool.compare Compare two nested models fitted to imputed data
pool Combine estimates by Rubin's rules
potthoffroy Potthoff-Roy data
nhanes NHANES example - all variables numerical
walking Walking disability data
windspeed Subset of Irish wind speed data
ampute.default.patterns Default patterns 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.weights Default weights in ampute
ampute.default.freq Default freq in ampute
ampute.default.type Default type in ampute()
ampute.continuous Multivariate Amputation Based On Continuous Probability Functions
ampute.default.odds Default odds in ampute()
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Details

Type Package
Date 2020-05-14
LinkingTo Rcpp
Encoding UTF-8
License GPL-2 | GPL-3
LazyLoad yes
LazyData yes
URL https://github.com/stefvanbuuren/mice, https://stefvanbuuren.name/mice/, https://stefvanbuuren.name/fimd/
BugReports https://github.com/stefvanbuuren/mice/issues
RoxygenNote 7.1.0
NeedsCompilation yes
Packaged 2020-05-14 14:39:16 UTC; buurensv
Repository CRAN
Date/Publication 2020-05-14 15:20:03 UTC

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