box_cox_shift

<ul>
<li>T the transformation with arguments Y, the data, lambda the parameter, and boolean inverse to calculate inverse transformation. The parameter lambda has two real elements (1) the power and (2) the additive shift to the data.</li>
<li>T_deriv the transformation with arguments Y, the data, lambda the parameter.</li>
</ul>

datasets

Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.

Gregory Hunt

rrscale

Robust Re-Scaling to Better Recover Latent Effects in Data

Johann Gagnon-Bartsch

box_cox_shift function

An object of class <code>list</code> of length 2.

box_cox_shift: Box-cox transformation of shifted variable

Description

Usage

Arguments

Format