Imputation by random forests
Imputes univariate missing data using random forests.
mice.impute.rf(y, ry, x, wy = NULL, ntree = 10, ...)
y |
Vector to be imputed |
ry |
Logical vector of length |
x |
Numeric design matrix with |
wy |
Logical vector of length |
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.
Vector with imputed data, same type as y, and of length
sum(wy)
An alternative implementation was independently
developed by Shah et al (2014). This were available as
functions CALIBERrfimpute::mice.impute.rfcat and
CALIBERrfimpute::mice.impute.rfcont (now archived).
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.
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. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.
Other univariate imputation functions:
mice.impute.cart(),
mice.impute.lda(),
mice.impute.logreg.boot(),
mice.impute.logreg(),
mice.impute.mean(),
mice.impute.midastouch(),
mice.impute.mnar.logreg(),
mice.impute.norm.boot(),
mice.impute.norm.nob(),
mice.impute.norm.predict(),
mice.impute.norm(),
mice.impute.pmm(),
mice.impute.polr(),
mice.impute.polyreg(),
mice.impute.quadratic(),
mice.impute.ri()
library("lattice")
imp <- mice(nhanes2, meth = "rf", ntree = 3)
plot(imp)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.