Draw samples from the regression tree prior

Draws M binary regression trees from the flexBART tree prior, resulting in a single prior draw from the sum-of-trees prior.

Usage

rflexBART(train_data,nd,verbose = TRUE,
          print_every = floor(nd/10),...)

Arguments

train_data: an object of class data.frame containing data used to train the model. As usual, rows (resp. columns) correspond to observations (resp. variables)
nd: Number of trees to draw
verbose: Logical, indicating whether a message should be printed to the R console after every print_every trees have been sampled. Default is FALSE.
print_every: If verbose == TRUE, then a message is printed to the R console after every print_every trees have been sampled. Default is floor(nd/10).
...: Additional arguments for setting prior hyperparameters (e.g., number of trees, \(\mu_{0}\), \(\tau\), etc.). See flexBART for details about additional arguments.

Details

This function is useful for drawing samples from the regression tree prior underpinning flexBART. Together, these sampled trees form a single ensemble of trees. The main utility of this function is to study how often certain observations are clustered together in individual trees. This is key to understanding how flexBART “borrows strength” across observations.

Value

A list containing

num_leafs: An integer vector of length M recording the number of leaf nodes in each tree of the ensemble.
num_singletons: An integer vector of length M recording the number of leaf nodes in each tree that contain only one observation.
num_empty: An integer vector of length M recording the number of leaf nodes in each tree that contain no observations.
max_leaf_size: An integer vector of length M recording the maximum number of observation contained in a leaf in each tree of the ensemble.
min_leaf_size: An integer vector of length M recording the minimum number of observation contained in a leaf in each tree of the ensemble.
kernel: An n x n matrix whose (i,j) entry is the proportion of tree draws in which observations i and j land in the same leaf of the tree.