Generate predictions and prediction variances from a random forest based on the infinitesimal jackknife.
A random forest trained with
The data used to train
The data used to predict with the forest; defaults to
Should 95% confidence intervals based on the CLT be returned along with predictions and prediction variances?
either 'ci' for conditional inference tree or 'rf' for traditional CART tree
should progress bar be shown? (only applicable when
The random forest trained with
keep.inbag=TRUE is supplied
only for the purpose of defining the resampling scheme. The function builds
a new random forest based on the
tree.type setting. However, the
resamples are maintained identically to the supplied random forest. This
allows for direct comparison of the tree methods without having to account
for variation in resampling.
Currently, the CI methods are much more computationally intensive because
there is no C implementation of the CI random forest method that indicates
the number of times that each sample is included in each resample. In
order to carry out our simulations using V_IJ^B, we had to use a
pure R implementation of CI random forests. This is different for CART
random forests, where a C implementation already exists in the
randomForest package. However, it should be noted that the
difference in computational times is due to the random forest creation
step, not the implementation of V_IJ^B. This should not be an
issue in the future when a C implementation of CI random forests is
Note: This function does not use the default predict method for forests
cforest. The predictions here are the direct averages of
all tree predictions, instead of using the observation weights. Therefore,
predictions from this function will likely differ from
predict.cforest when using subsampling.
This function currently only works with regression forests – not classification forests.
A data frame with the predictions and prediction variances (and optionally 95% confidence interval)
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