plot.netrankr_interval

Implements methods for centrality related analyses of networks.
While the package includes the possibility to build more than 20 indices,
its main focus lies on index-free assessment of centrality via partial
rankings obtained by neighborhood-inclusion or positional dominance. These
partial rankings can be analyzed with different methods, including
probabilistic methods like computing expected node ranks and relative
rank probabilities (how likely is it that a node is more central than another?).
The methodology is described in depth in the vignettes and in
Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.

David Schoch

netrankr

Analyzing Partial Rankings in Networks

Julian Müller

plot.netrankr_interval function

<dl><dt>x</dt>
<dd>A netrank object</dd>
<dt>cent_scores</dt>
<dd>A data frame containing centrality scores of indices (optional)</dd>
<dt>cent_cols</dt>
<dd>colors for centrality indices. If NULL a default palette is used. Length must be equal to columns in cent_scores.</dd>
<dt>ties.method</dt>
<dd>how to treat ties in the rankings. see <a href="/link/rank?package=netrankr&version=1.2.4" data-mini-rdoc="netrankr::rank">rank</a> for details</dd>
<dt>...</dt>
<dd>additional arguments to plot</dd></dl>

Arguments

Author

plot netrankr_interval objects — plot.netrankr_interval

<dl>

<dt>x</dt>
<dd>A netrank object</dd>


<dt>cent_scores</dt>
<dd>A data frame containing centrality scores of indices (optional)</dd>


<dt>cent_cols</dt>
<dd>colors for centrality indices. If NULL a default palette is used. Length must be equal to columns in cent_scores.</dd>


<dt>ties.method</dt>
<dd>how to treat ties in the rankings. see <a href='https://rdrr.io/r/base/rank.html'>rank</a> for details</dd>


<dt>...</dt>
<dd>additional arguments to plot</dd>

</dl>

plot.netrankr_interval: plot netrankr_interval objects

Description

Usage

Arguments

Author