mice.impute.rf {mice} | R Documentation |
Imputes univariate missing data using random forests.
mice.impute.rf(y, ry, x, ntree = 10, ...)
y |
Numeric vector with incomplete data |
ry |
Response pattern of |
x |
Design matrix with |
ntree |
The number of trees to grow. The default is 10. |
... |
Other named arguments passed down to |
Imputation of y
by random forests. The method
calls randomForrest()
which implements Breiman's random forest
algorithm (based on Breiman and Cutler's original Fortran code)
for classification and regression. See Appendix A.1 of Doove et al.
(2014) for the definition
of the algorithm used. An alternative implementation was independently
developed by Shah et al (2014), and is available in the package
CALIBERrfimpute
. Simulations by Shah (Feb 13, 2014) suggested that
the quality of the imputation for 10 and 100 trees was identical,
so mice 2.22 changed the default number of trees from ntree = 100
to
ntree = 10
.
Numeric vector of length sum(!ry)
with imputations
Lisa Doove, Stef van Buuren, Elise Dusseldorp, 2012
Doove, L.L., van Buuren, S., Dusseldorp, E. (2014), Recursive partitioning for missing data imputation in the presence of interaction Effects. Computational Statistics \& Data Analysis, 72, 92-104.
Shah, A.D., Bartlett, J.W., Carpenter, J., Nicholas, O., Hemingway, H. (2014), Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study. American Journal of Epidemiology, doi: 10.1093/aje/kwt312.
Van Buuren, S.(2012), Flexible imputation of missing data, Boca Raton, FL: Chapman & Hall/CRC.
mice
, mice.impute.cart
,
randomForest
,
mice.impute.rfcat
,
mice.impute.rfcont
library("lattice") imp <- mice(nhanes2, meth = "rf", ntree = 3) plot(imp)