ircNorm

The aim is to obtain a matrix with row and column sums equal to 1.
This is achieved by iterating row and column normalization. This is usually not possible if any row or column has only 1 non-zero cell.

manip

This is primarily meant as an implementation of generalized blockmodeling for valued networks.
In addition, measures of similarity or dissimilarity based on structural equivalence and
regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted:
Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>,
Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>.

Ales Ziberna

blockmodeling

Generalized and Classical Blockmodeling of Valued Networks

Aleš Žiberna

Marjan Cugmas

ircNorm function

<dl><dt>M</dt>
<dd>A non-negative valued matrix to be normalized.</dd>
<dt>eps</dt>
<dd>The maximum allows squared deviation of a row or column's maximum from 1 (if not exactly 0).
Also, if the all deviations in two consequtive iterations are smaller, the process is terminated.</dd>
<dt>maxiter</dt>
<dd>Maximum number of iterations. If reached, the process is terminated and the current solution returned.</dd></dl>

Arguments

Author

Function for iterated row and column normalization of valued matrices — ircNorm

<dl>

<dt>M</dt>
<dd>A non-negative valued matrix to be normalized.</dd>


<dt>eps</dt>
<dd>The maximum allows squared deviation of a row or column's maximum from 1 (if not exactly 0).
Also, if the all deviations in two consequtive iterations are smaller, the process is terminated.</dd>


<dt>maxiter</dt>
<dd>Maximum number of iterations. If reached, the process is terminated and the current solution returned.</dd>

</dl>

ircNorm: Function for iterated row and column normalization of valued matrices

Description

Usage

Value

Arguments

Author

Examples