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 `length(y)`

indicating the
the subset `y[ry]`

of elements in `y`

to which the imputation
model is fitted. The `ry`

generally distinguishes the observed
(`TRUE`

) and missing values (`FALSE`

) in `y`

.

x

Numeric design matrix with `length(y)`

rows with predictors for
`y`

. Matrix `x`

may have no missing values.

wy

Logical vector of length `length(y)`

. A `TRUE`

value
indicates locations in `y`

for which imputations are created.

ntree

The number of trees to grow. The default is 10.

…

Other named arguments passed down to `randomForest()`

and
`randomForest:::randomForest.default()`

.

Vector with imputed data, same type as `y`

, and of length
`sum(wy)`

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.

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.

`mice`

, `mice.impute.cart`

,
`randomForest`

,
`mice.impute.rfcat`

,
`mice.impute.rfcont`

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.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`

```
# NOT RUN {
library("lattice")
imp <- mice(nhanes2, meth = "rf", ntree = 3)
plot(imp)
# }
```

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