lpda.fit

<code>lpda.fit</code> computes the discriminating hyperplane for two groups, giving as a result the coefficients of the hyperplane.

classification

Classification method obtained through linear programming.
It is advantageous with respect to the classical developments when the distribution of the variables
involved is unknown or when the number of variables is much greater than the number of individuals.
Mathematical details behind the method are published in Nueda, et al. (2022) "LPDA: A new classification method based on linear programming".
<doi:10.1371/journal.pone.0270403>.

Maria Jose Nueda

lpda

Linear Programming Discriminant Analysis

lpda.fit function

<dl><dt>data</dt>
<dd>Matrix containing data. Individuals in rows and variables in columns</dd>
<dt>group</dt>
<dd>Vector with the variable group</dd>
<dt>f1</dt>
<dd>Vector with weights for individuals of the first group</dd>
<dt>f2</dt>
<dd>Vector with weights for individuals of the second group</dd></dl>

Arguments

Maria Jose Nueda, <a href="/link/mj.nueda%40ua.es?package=lpda&version=1.2.0" data-mini-rdoc="lpda::mj.nueda@ua.es">mj.nueda@ua.es</a>

Author

lpda.fit computes the discriminating hyperplane for two groups — lpda.fit

<dl>

<dt>data</dt>
<dd>Matrix containing data. Individuals in rows and variables in columns</dd>


<dt>group</dt>
<dd>Vector with the variable group</dd>


<dt>f1</dt>
<dd>Vector with weights for individuals of the first group</dd>


<dt>f2</dt>
<dd>Vector with weights for individuals of the second group</dd>

</dl>

Maria Jose Nueda, <a href='mailto:mj.nueda@ua.es'>mj.nueda@ua.es</a>

lpda.fit: lpda.fit computes the discriminating hyperplane for two groups

Description

Usage

Value

Arguments

Author

References

See Also