10) |
mice: Random forest |
Implements a random forest algorithm
(Breiman) and fills missing values with average predictions from 10
classification and regression trees that are based on 10 random subsets
of the predictor variables. This method shares many features with the
classification and regression tree imputation but the imputed values
exhibit a larger variability. |
Missing values are outside the range of
the reported values and/or MNAR |
11) |
mi: Bayes predictive mean matching |
Fits Bayesian
generalized linear models to fill missing values with those reported
values that are closest to the predicted ones. Like predictive mean
matching, imputed values are thereby restricted to a subset of the
reported ones. |
Missing values are outside the range of the reported
values and/or MNAR |
12) |
Amelia: Bootstrap expectation maximization |
Draws multiple
bootstrap samples from the supplied data and calculates separate
posterior maxima. The distribution of these maxima is then used to fill
the missing values. In order to yield reliable imputations this
algorithm assumes multivariate normality and MCAR or MAR. |
Missing
values are MNAR. |
13) |
missForest: Non-parametric random forest |
Iterates the
random forest-algorithm (Breimans) until a certain convergence criterion
is fulfilled. |
Missing values are outside the range of the reported
values and/or MNAR |
14) |
Hmisc: Additive regression plus bootstrap predictive mean
matching |
Draws multiple bootstrap samples from the supplied data and
fits separate additive regression models to obtain averaged predictions
for the missing values. These missing values are then filled with those
observed values that are closest to the predicted ones. Like predictive
mean matching, imputed values are thereby restricted to a subset of the
reported ones. |
Missing values are outside the range of the reported
values and/or MNAR |