These functions calculate the respective network statistic for
ego. When multiplied with the importance of each statistic (the
'parameters') this constitutes the network evaluation of ego. See:
ts_eval()
.
Usage
ts_degree(net, ego)
ts_recip(net, ego)
ts_outAct(net, ego)
ts_inAct(net, ego)
ts_outPop(net, ego)
ts_inPop(net, ego)
ts_transTrip(net, ego)
ts_transMedTrip(net, ego)
ts_transRecTrip(net, ego)
ts_cycle3(net, ego)
ts_egoX(net, ego, cov)
ts_altX(net, ego, cov)
ts_diffX(net, ego, cov)
ts_simX(net, ego, cov)
ts_absDiffX(net, ego, cov)
ts_sameX(net, ego, cov)
ts_egoXaltX(net, ego, cov)
Arguments
- net
matrix, the adjacency matrix representing the relations between actors. Valid values are 0 and 1.
- ego
numeric, the ego for which we want to calculate the network statistic.
- cov
numeric, covariate scores
Details
For examples on how to use these statistics see:
vignette("1.Introduction_RsienaTwoStep", package="RsienaTwoStep")
.
For the mathematical definition of these network statistics see chapter 12 of the RSiena manual (Ripley et al. 2022) .
References
Ripley RM, Snijders TA, Boda Z, Vörös A, Preciado P (2022). Manual for SIENA version 4.0 (version August 11, 2022). http://www.stats.ox.ac.uk/~snijders/siena/RSiena_manual.pdf.