Imputation by classification and regression trees
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 |
x |
Numeric design matrix with |
wy |
Logical vector of length |
minbucket |
The minimum number of observations in any terminal node used.
See |
cp |
Complexity parameter. Any split that does not decrease the overall
lack of fit by a factor of cp is not attempted. See |
... |
Other named arguments passed down to |
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.
Vector with imputed data, same type as y, and of length
sum(wy)
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.
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.
Other univariate imputation functions:
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.rf(),
mice.impute.ri()
require(rpart) imp <- mice(nhanes2, meth = "cart", minbucket = 4) plot(imp)
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