Imputes univariate missing data using classification and regression trees.

`mice.impute.cart(y, ry, x, wy = NULL, minbucket = 5, cp = 1e-04, ...)`

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.

minbucket

The minimum number of observations in any terminal node used.
See `rpart.control`

for details.

cp

Complexity parameter. Any split that does not decrease the overall
lack of fit by a factor of cp is not attempted. See `rpart.control`

for details.

...

Other named arguments passed down to `rpart()`

.

Vector with imputed data, same type as `y`

, and of length
`sum(wy)`

Numeric vector of length `sum(!ry)`

with imputations

Imputation of `y`

by classification and regression trees. The procedure
is as follows:

Fit a classification or regression tree by recursive partitioning;

For each

`ymis`

, find the terminal node they end up according to the fitted tree;Make a random draw among the member in the node, and take the observed value from that draw as the imputation.

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.

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984), Classification and regression trees, Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.

Van Buuren, S. (2018).
*Flexible Imputation of Missing Data. Second Edition.*
Chapman & Hall/CRC. Boca Raton, FL.

`mice`

, `mice.impute.rf`

,
`rpart`

, `rpart.control`

Other univariate imputation functions: `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.rf`

,
`mice.impute.ri`

```
# NOT RUN {
require(rpart)
imp <- mice(nhanes2, meth = 'cart', minbucket = 4)
plot(imp)
# }
```

Run the code above in your browser using DataLab