huber_estimate

Iteratively computes M-estimates for location and scale. These
are robust estimates of the mean and standard deviation of the data.

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) 

huber_estimate function

<dl><dt>x</dt>
<dd>Vector of numeric values for which the location and scale should be
estimated.</dd>
<dt>k</dt>
<dd>Numeric value &gt; 0 that the determines the value beyond which the
signal is winsorized.</dd>
<dt>tol</dt>
<dd>Tolerance for the iterative procedure.</dd></dl>

Arguments

Huber M-estimate — huber_estimate

<dl>

<dt>x</dt>
<dd>Vector of numeric values for which the location and scale should be
estimated.</dd>


<dt>k</dt>
<dd>Numeric value &gt; 0 that the determines the value beyond which the
signal is winsorized.</dd>


<dt>tol</dt>
<dd>Tolerance for the iterative procedure.</dd>

</dl>

huber_estimate: Huber M-estimate

Description

Usage

Value

Arguments