prune.SDForest

prune

Prunes all trees in the forest and re-calculates the out-of-bag predictions and performance measures.
The training data is needed to calculate the out-of-bag statistics. Note that the forest is pruned in place.
If you intend to keep the original forest, make a copy of it before pruning.

Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.48550/arXiv.2502.03969>).

Markus Ulmer

SDModels

Spectrally Deconfounded Models

Cyrill Scheidegger

prune.SDForest function

<dl><dt>object</dt>
<dd>an SDForest object</dd>
<dt>cp</dt>
<dd>Complexity parameter, the higher the value the more nodes are pruned.</dd>
<dt>X</dt>
<dd>The training data, if NULL the data from the forest object is used.</dd>
<dt>Y</dt>
<dd>The training response variable, if NULL the data from the forest object is used.</dd>
<dt>Q</dt>
<dd>The transformation function, if NULL the data from the forest object is used.</dd>
<dt>pred</dt>
<dd>If TRUE the predictions are calculated, if FALSE only the out-of-bag statistics are calculated.
This can set to FALSE to save computation time if only the out-of-bag statistics are needed.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Prune an SDForest — prune.SDForest

<dl>

<dt>object</dt>
<dd>an SDForest object</dd>


<dt>cp</dt>
<dd>Complexity parameter, the higher the value the more nodes are pruned.</dd>


<dt>X</dt>
<dd>The training data, if NULL the data from the forest object is used.</dd>


<dt>Y</dt>
<dd>The training response variable, if NULL the data from the forest object is used.</dd>


<dt>Q</dt>
<dd>The transformation function, if NULL the data from the forest object is used.</dd>


<dt>pred</dt>
<dd>If TRUE the predictions are calculated, if FALSE only the out-of-bag statistics are calculated.
This can set to FALSE to save computation time if only the out-of-bag statistics are needed.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

prune.SDForest: Prune an SDForest

Description

Usage

Value

Arguments

Author

See Also

Examples