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Multivariate Imputation by Chained Equations (MICE) is commonly used to impute missing values in analysis datasets using full conditional specifications. However, it requires that the predictor models are specified correctly, including interactions and nonlinearities. Random Forest is a regression and classification method which can accommodate interactions and non-linearities without requiring a particular statistical model to be specified.
● Data Source:
CranContrib
● Keywords: package
● Alias: CALIBERrfimpute, CALIBERrfimpute-package
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A convenience function to set global options for number of trees or number of nodes.
● Data Source:
CranContrib
● Keywords:
● Alias: setRFoptions
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This method can be used to impute continuous variables in MICE by specifying method = 'rfcont'. It was developed independently from the mice.impute.rf algorithm of Doove et al., and differs from it in drawing imputed values from a normal distribution.
● Data Source:
CranContrib
● Keywords:
● Alias: mice.impute.rfcont
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This method can be used to impute factor variables (binary or >2 levels) in MICE by specifying method = 'rfcat'. It was developed independently from the mice.impute.rf algorithm of Doove et al., and differs from it in some respects.
● Data Source:
CranContrib
● Keywords:
● Alias: mice.impute.rfcat
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Creates multivariate normal or normal and binary data, as used in the simulation study.
● Data Source:
CranContrib
● Keywords:
● Alias: simdata
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Introduces missingness into x1 and x2 into a data.frame of the format produced by simdata, for use in the simulation study. The probability of missingness depends on the logistic of the fully observed variables y and x3; hence it is missing at random but not missing completely at random.
● Data Source:
CranContrib
● Keywords:
● Alias: makemar
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providing 
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