plotOOB

This function visualizes the out-of-bag performance of an SDForest
for different complexity parameters. Can be used to choose the optimal
complexity parameter.

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

plotOOB function

<dl><dt>object</dt>
<dd>A paths object with loss_path <code>matrix</code> 
with the out-of-bag performance for each complexity parameter.</dd>
<dt>sqrt_scale</dt>
<dd>If TRUE the x-axis is on a square root scale.</dd></dl>

Arguments

Author

Visualize the out-of-bag performance of an SDForest — plotOOB

<dl>

<dt>object</dt>
<dd>A paths object with loss_path <code>matrix</code> 
with the out-of-bag performance for each complexity parameter.</dd>


<dt>sqrt_scale</dt>
<dd>If TRUE the x-axis is on a square root scale.</dd>

</dl>

plotOOB: Visualize the out-of-bag performance of an SDForest

Description

Usage

Value

Arguments

Author

See Also

Examples