create_transformer_skeleton

Creates skeleton objects. This generates objects without fitting
parameters. This is primarily intended for creating transformers
externally, where fitting parameters are known.

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations
allow for transforming variables with distributions distant from 0 to
normality. Transformers are implemented as S4 objects. These allow for
transforming new instances to normality after optimising fitting parameters
on other data. A test for central normality allows for rejecting
transformations that fail to produce a suitably normal distribution,
independent of sample number.

Alex Zwanenburg

power.transform

Location and Scale Invariant Power Transformations

Steffen Löck

German Cancer Research Center (DKFZ) 

create_transformer_skeleton function

<dl><dt>method</dt>
<dd>Transformation method. Can be <code>"none"</code>, <code>"box_cox"</code> or
<code>"yeo_johnson"</code>.</dd>
<dt>lambda</dt>
<dd>Value of the transformation parameter lambda. Can also be
changed using the <code>set_lambda</code> method.</dd>
<dt>shift</dt>
<dd>Value of the shift parameter. Can also be changed using the
<code>set_shift</code> method.</dd>
<dt>scale</dt>
<dd>Value of the scale parameter. Can also be changed using the
<code>set_scale</code> method.</dd></dl>

Arguments

Create transformation object skeleton — create_transformer_skeleton

<dl>

<dt>method</dt>
<dd>Transformation method. Can be <code>"none"</code>, <code>"box_cox"</code> or
<code>"yeo_johnson"</code>.</dd>


<dt>lambda</dt>
<dd>Value of the transformation parameter lambda. Can also be
changed using the <code>set_lambda</code> method.</dd>


<dt>shift</dt>
<dd>Value of the shift parameter. Can also be changed using the
<code>set_shift</code> method.</dd>


<dt>scale</dt>
<dd>Value of the scale parameter. Can also be changed using the
<code>set_scale</code> method.</dd>

</dl>

create_transformer_skeleton: Create transformation object skeleton

Description

Usage

Value

Arguments