random.imp: Random Imputation of Missing Data

In mi: Missing Data Imputation and Model Checking

Description

Simple random imputation of missing values in given data set.

Usage

random.imp(data, imp.method = c( "bootstrap", "pca" ) , ...)

Arguments

data	A vector, matrix, or data frame with missing data.
imp.method	Character to specify which method of random imputation to use. Default is "bootstrap". Note: pca is not implemented in the current version.
...	Unused

Details

Impute missing values based on the observed data for the variable.

Value

Data with its missing values imputed using the specified method.

Author(s)

Masanao Yajima [e-mail], M.Grazia Pittau [e-mail], Andrew Gelman [e-mail]

References

Andrew Gelman and Jennifer Hill. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

See Also

mi

Examples

  data(CHAIN)
  data.imp <- random.imp(CHAIN) 

mi documentation built on May 2, 2019, 4:43 p.m.