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The `SplitTree` function can be used for both splits on either dimension of the data. If the data is split on either dimension of the data, then the samples from each side of the cut are stored separately and can be saved as separate files. This is necessary for the use of a single tree to fit a large number of smaller trees, as well as for the save and reload features for managing large tree databases, which we will discuss in the next chapter. Additionally, this function creates a new node at the cut point and copies the contents of the nodes at both sides of the cut to the new nodes. By default, the data sample from each side of the cut is held as its own variable. The function also has some additional parameters to be specified, though most of them are used by the defaults.
Here is an example of a simple `SplitTree` function applied to a simple toy dataset.
In this function, the `split.by` parameter is used to specify which partitioning the sample should be split into. The parameters `tree` and `warmstart` specify the value that should be used for the first split, `means` is used to specify the location of the mean, and `var.regions` specifies the variable to calculate the mean for. In this example, the data is split into two parts by the first split, which is split in half by the second split.
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