Multivariate Imputation by Chained Equations
Multivariate amputation based on continuous probability functions
Default freq
in ampute
Default odds
in ampute()
Default patterns
in ampute
Default type
in ampute()
Default weights
in ampute
Multivariate amputation based on discrete probability functions
Multivariate amputation under a MCAR mechanism
Generate missing data for simulation purposes
Compare several nested models
Appends specified break to the data
Converts an imputed dataset (long format) into a mids
object
Create a mira
object from repeated analyses
Converts into a mitml.result
object
Box-and-whisker plot of amputed and non-amputed data
Box-and-whisker plot of observed and imputed data
Combine R objects by rows and columns
Select complete cases
Complete case indicator
Extracts the completed data from a mids
object
Construct blocks from formulas
and predictorMatrix
Computes convergence diagnostics for a mids
object
Compare two nested models using D1-statistic
Compare two nested models using D2-statistic
Compare two nested models using D3-statistic
Density plot of observed and imputed data
Computes least squares parameters
Extends a formula with predictors
Extends formula's with predictor matrix settings
Extract broken stick estimates from a lmer
object
Fraction of incomplete cases among cases with observed
Subset rows of a mids
object
Fix coefficients and update model
Influx and outflux of multivariate missing data patterns
Fluxplot of the missing data pattern
Wrapper function that runs MICE in parallel
Extract list of fitted models
Extract estimate from mipo
object
Glance method to extract information from a mipo
object
Generalized linear model for mids
object
Enlarge number of imputations by combining mids
objects
Select incomplete cases
Incomplete case indicator
Conditional imputation helper
Check for mads
object
Check for mids
object
Check for mipo
object
Check for mira
object
Check for mitml.result
object
Linear regression for mids
object
Multivariate amputed data set (mads
)
Creates a blocks
argument
Creates a blots
argument
Creates a formulas
argument
Creates a method
argument
Creates a post
argument
Creates a predictorMatrix
argument
Creates a visitSequence
argument
Creates a where
argument
Find index of matched donor units
Jamshidian and Jalal's Non-Parametric MCAR Test
Missing data pattern by variable pairs
Missing data pattern
Graphical parameter for missing data plots
Imputation by a two-level logistic model using glmer
Imputation by a two-level normal model using lmer
Imputation by a two-level normal model
Imputation by a two-level normal model using pan
Imputation of most likely value within the class
Imputation at level 2 by Bayesian linear regression
Imputation at level 2 by predictive mean matching
Imputation by classification and regression trees
Multivariate multilevel imputation using jomo
Imputation by direct use of lasso logistic regression
Imputation by direct use of lasso linear regression
Imputation by indirect use of lasso logistic regression
Imputation by indirect use of lasso linear regression
Imputation by linear discriminant analysis
Imputation by logistic regression using the bootstrap
Imputation by logistic regression
Imputation by the mean
Imputation by predictive mean matching with distance aided donor selec...
Imputation under MNAR mechanism by NARFCS
Imputation by multivariate predictive mean matching
Imputation by linear regression, bootstrap method
Imputation by linear regression without parameter uncertainty
Imputation by linear regression through prediction
Imputation by Bayesian linear regression
Impute multilevel missing data using pan
Passive imputation
Imputation by predictive mean matching
Imputation of ordered data by polytomous regression
Imputation of unordered data by polytomous regression
Imputation of quadratic terms
Imputation by random forests
Imputation by the random indicator method for nonignorable data
Imputation by simple random sampling
Multivariate Imputation by Chained Equations (Iteration Step)
mice
: Multivariate Imputation by Chained Equations
Set the theme for the plotting Trellis functions
Multiply imputed data set (mids
)
Export mids
object to Mplus
Export mids
object to SPSS
mipo
: Multiple imputation pooled object
Create an object of class "mira"
Name imputation blocks
Name formula list elements
Number of complete cases
Cumulative hazard rate or Nelson-Aalen estimator
Number of incomplete cases
Number of imputations per block
Draws values of beta and sigma by Bayesian linear regression
Wrapper function that runs MICE in parallel
Datasets with various missing data patterns
Finds an imputed value from matches in the predictive metric (deprecat...
Compare two nested models fitted to imputed data
Pools R^2 of m models fitted to multiply-imputed data
Combine estimates by pooling rules
Multiple imputation pooling: univariate version
Combines estimates from a tidy table
Print a mira
object
Quick selection of predictors from the data
Objects exported from other packages
Squeeze the imputed values to be within specified boundaries.
Stripplot of observed and imputed data
Summary of a mira
object
Supports semi-transparent foreground colors?
Tidy method to extract results from a mipo
object
Echoes the package version number
Evaluate an expression in multiple imputed datasets
Scatterplot of amputed and non-amputed data against weighted sum score...
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.
Useful links