Package:
CALIBERrfimpute
Authors:
Anoop Shah [aut, cre], Jonathan Bartlett [ctb], Harry Hemingway [ths], Owen Nicholas [ths], Aroon Hingorani [ths]
Category:
Multiple Imputation
Use-Cases:
Multiple Imputation, MICE and Random Forest
Popularity:
Description:
Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) doi:10.1093/aje/kwt312.
Algorithms for Imputation:
mice compatible methods such as:
rfcont- Impute continuous variables using Random Forest within MICE,rfcat- Impute categorical variables using Random Forest within MICE.
Other Algorithms:
simdata()- Simulate multivariate data for testing
Datasets:
none.
Further Information:
Documentation: https://www.rdocumentation.org/packages/CALIBERrfimpute/versions/1.0-7/topics/CALIBERrfimpute-package
Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H. Comparison of Random Forest and parametric imputation models for imputing missing data using MICE: a CALIBER study. American Journal of Epidemiology 2014; 179(6): 764–774. doi:10.1093/aje/kwt312
Doove LL, van Buuren S, Dusseldorp E. Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics and Data Analysis 2014; 72: 92–104. doi:10.1016/j.csda.2013.10.025
Input:
data.frame
Vignette:
Example:
library(CALIBERrfimpute)
mydata <- data.frame(x1 = as.factor(c('this', 'this', NA, 'that', 'this')),
x2 = 1:5,
x3 = c(TRUE, FALSE, TRUE, NA, FALSE))
mice(mydata, method = c('rfcat', 'rfcont'), m = 2, maxit = 2)