Description Usage Arguments Details Value Examples
Generate predictions and prediction variances from a random forest based on the infinitesimal jackknife.
1 2 
random.forest 
A random forest trained with 
rf.data 
The data used to train 
pred.data 
The data used to predict with the forest; defaults to

CI 
Should 95% confidence intervals based on the CLT be returned along with predictions and prediction variances? 
tree.type 
either 'ci' for conditional inference tree or 'rf' for traditional CART tree 
prog.bar 
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
created.
Note: This function does not use the default predict method for forests
produced by 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)
1 2 3 4 5 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.