View source: R/imp.rfnode.cond.R
imp.rfnode.cond | R Documentation |
RfNode.Cond
multiple imputation method is for mixed types of variables,
using conditional distribution formed by predicting nodes of random forest
(out-of-bag observations will be excluded).
imp.rfnode.cond( data, num.imp = 5, max.iter = 5, num.trees = 10, pre.boot = TRUE, print.flag = FALSE, ... )
data |
A data frame or a matrix containing the incomplete data. Missing
values should be coded as |
num.imp |
Number of multiple imputations. The default is
|
max.iter |
Number of iterations. The default is |
num.trees |
Number of trees to build. The default is
|
pre.boot |
If |
print.flag |
If |
... |
Other arguments to pass down. |
During imputation using imp.rfnode.cond
, for missing observations, the
candidate non-missing observations will be found by the predicting nodes
of random trees in the random forest model. Only the in-bag observations
for each random tree will be used for imputation.
An object of S3 class mids
.
Shangzhi Hong
Hong, Shangzhi, et al. "Multiple imputation using chained random forests." Preprint, submitted April 30, 2020. https://arxiv.org/abs/2004.14823.
Zhang, Haozhe, et al. "Random Forest Prediction Intervals." The American Statistician (2019): 1-20.
Shah, Anoop D., et al. "Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study." American journal of epidemiology 179.6 (2014): 764-774.
Malley, James D., et al. "Probability machines." Methods of information in medicine 51.01 (2012): 74-81.
# Prepare data: convert categorical variables to factors nhanes.fix <- nhanes nhanes.fix[, c("age", "hyp")] <- lapply(nhanes[, c("age", "hyp")], as.factor) # Perform imputation using imp.rfnode.cond imp <- imp.rfnode.cond(nhanes.fix) # Do repeated analyses anl <- with(imp, lm(chl ~ bmi + hyp)) # Pool the results pool <- pool(anl) # Get pooled estimates reg.ests(pool)
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