Package structure
The figure below shows how the imputation functions are organized in
this R package.
An R package for random-forest-empowered imputation of missing Data
RfEmpImp is an R package for multiple imputation using
chained random forests (RF).
This R package provides prediction-based and node-based multiple
imputation algorithms using random forests, and currently operates under
the multiple imputation computation framework mice.
For more details of the implemented imputation algorithms, please refer
to: arXiv:2004.14823
(further updates soon).
Users can install the CRAN version of RfEmpImp from
CRAN, or the latest development version of RfEmpImp from
GitHub:
# Install from CRAN
install.packages("RfEmpImp")
# Install from GitHub online
if(!"remotes" %in% installed.packages()) install.packages("remotes")
remotes::install_github("shangzhi-hong/RfEmpImp")
# Install from released source package
install.packages(path_to_source_file, repos = NULL, type = "source")
# Attach
library(RfEmpImp)For data with mixed types of variables, users can call function
imp.rfemp() to use RfEmp method, for using
RfPred.Emp method for continuous variables, and using
RfPred.Cate method for categorical variables (of type
logical or factor, etc.).
Starting with version 2.0.0, the names of parameters were
further simplified, please refer to the documentation for details.
For continuous variables, in RfPred.Emp method, the
empirical distribution of random forest’s out-of-bag prediction errors
is used when constructing the conditional distributions of the variable
under imputation, providing conditional distributions with better
quality. Users can set method = "rfpred.emp" in function
call to mice to use it.
Also, in RfPred.Norm method, normality was assumed for
RF prediction errors, as proposed by Shah et al., and users can
set method = "rfpred.norm" in function call to
mice to use it.
For categorical variables, in RfPred.Cate method, the
probability machine theory is used, and the predictions of missing
categories are based on the predicted probabilities for each missing
observation. Users can set method = "rfpred.cate" in
function call to mice to use it.
# Prepare data
df <- conv.factor(nhanes, c("age", "hyp"))
# Do imputation
imp <- imp.rfemp(df)
# Do analyses
regObj <- with(imp, lm(chl ~ bmi + hyp))
# Pool analyzed results
poolObj <- pool(regObj)
# Extract estimates
res <- reg.ests(poolObj)For continuous or categorical variables, the observations under the
predicting nodes of random forest are used as candidates for
imputation.
Two methods are now available for the RfNode algorithm
series.
It should be noted that categorical variables should be of types of
logical or factor, etc.
Users can call function imp.rfnode.cond() to use
RfNode.Cond method, performing imputation using the
conditional distribution formed by the prediction nodes.
The weight changes of observations caused by the bootstrapping of random
forest are considered, and only the “in-bag” observations are used as
candidates for imputation.
Also, users can set method = "rfnode.cond" in function call
to mice to use it.
Users can call function imp.rfnode.prox() to use
RfNode.Prox method, performing imputation using the
proximity matrices of random forests.
All the observations fall under the same predicting nodes are used as
candidates for imputation, including the out-of-bag ones.
Also, users can set method = "rfnode.prox" in function call
to mice to use it.
# Prepare data
df <- conv.factor(nhanes, c("age", "hyp"))
# Do imputation
imp <- imp.rfnode.cond(df)
# Or: imp <- imp.rfnode.prox(df)
# Do analyses
regObj <- with(imp, lm(chl ~ bmi + hyp))
# Pool analyzed results
poolObj <- pool(regObj)
# Extract estimates
res <- reg.ests(poolObj)| Type | Impute function | Univariate sampler | Variable type |
|---|---|---|---|
| Prediction-based imputation | imp.emp() | mice.impute.rfemp() | Mixed |
| / | mice.impute.rfpred.emp() | Continuous | |
| / | mice.impute.rfpred.norm() | Continuous | |
| / | mice.impute.rfpred.cate() | Categorical | |
| Node-based imputation | imp.node.cond() | mice.impute.rfnode.cond() | Mixed |
| imp.node.prox() | mice.impute.rfnode.prox() | Mixed | |
| / | mice.impute.rfnode() | Mixed |
The figure below shows how the imputation functions are organized in
this R package.
As random forest can be compute-intensive itself, and during multiple
imputation process, random forest models will be built for the variables
containing missing data for a certain number of iterations (usually 5 to
10 times) repeatedly (usually 5 to 20 times, for the number of
imputations performed). Thus, computational efficiency is of crucial
importance for multiple imputation using chained random forests,
especially for large data sets.
So in RfEmpImp, the random forest model building process is
accelerated using parallel computation powered by ranger.
The ranger R package provides support for parallel computation using
native C++. In our simulations, parallel computation can provide
impressive performance boost for imputation process (about 4x faster on
a quad-core laptop).