RSiena-package: Simulation Investigation for Empirical Network Analysis

In RSiena: Siena - Simulation Investigation for Empirical Network Analysis

Simulation Investigation for Empirical Network Analysis

Description

Fits statistical models to longitudinal sets of networks, and to longitudinal sets of networks and behavioral variables. Not only one-mode networks but also two-mode networks and multivariate networks are allowed. The models are stochastic actor-oriented models, described in Snijders (2017).

Recent versions of the package are distributed through GitHub, see https://github.com/snlab-nl/rsiena/.

Details

The main flow of operations of this package is as follows.

Data objects can be created from matrices and vectors using sienaDependent, coCovar, varCovar, coDyadCovar, etc., and finally sienaDataCreate.

Effects are selected using an sienaEffects object, which can be created using getEffects and may be further specified by includeEffects, setEffect, and includeInteraction.

Control of the estimation algorithm requires a sienaAlgorithm object that defines the settings (parameters) of the algorithm, and which can be created by sienaAlgorithmCreate.

Function siena07 is used to fit a model. Function sienaGOF can be used for studying goodness of fit.

A general introduction to the method is available in the tutorial paper Snijders, van de Bunt, and Steglich (2010). Next to the help pages, more detailed help is available in the manual (see below) and a lot of information is at the website (also see below).

Package:	RSiena
Type:	Package
Version:	1.4.1
Date:	2023-11-01
Depends:	R (>= 3.5.0)
Imports:	Matrix, lattice, parallel, MASS, methods, xtable
Suggests:	network, tools, codetools, tcltk
SystemRequirements:	GNU make
License:	GPL-2 | GPL-3
LazyData:	yes
NeedsCompilation:	yes
BuildResaveData:	no

Author(s)

Ruth Ripley, Krists Boitmanis, Tom Snijders, Felix Schoenenberger, Nynke Niezink, Christian Steglich, Viviana Amati. Contributions by Josh Lospinoso, Charlotte Greenan, Johan Koskinen, Mark Ortmann, Natalie Indlekofer, Mark Huisman, Christoph Stadtfeld, Per Block, Marion Hoffman, Michael Schweinberger, Robert Hellpap, Alvaro Uzaheta, Robert Krause, James Hollway, and Steffen Triebel.

Maintainer: Tom A.B. Snijders <[e-mail]>

References

Amati, V., Schoenenberger, F., and Snijders, T.A.B. (2015), Estimation of stochastic actor-oriented models for the evolution of networks by generalized method of moments. Journal de la Societe Francaise de Statistique 156, 140–165.

Amati, V., Schoenenberger, F., and Snijders, T.A.B. (2019), Contemporaneous statistics for estimation in stochastic actor-oriented co-evolution models. Psychometrika 84, 1068–1096.

Greenan, C. (2015), Evolving Social Network Analysis: developments in statistical methodology for dynamic stochastic actor-oriented models. DPhil dissertation, University of Oxford.

Niezink, N.M.D., and Snijders, T.A.B. (2017), Co-evolution of Social Networks and Continuous Actor Attributes. The Annals of Applied Statistics 11, 1948–1973.

Schweinberger, M., and Snijders, T.A.B. (2007), Markov models for digraph panel data: Monte Carlo based derivative estimation. Computational Statistics and Data Analysis 51, 4465–4483.

Snijders, T.A.B. (2001), The statistical evaluation of social network dynamics. Sociological Methodology 31, 361–395.

Snijders, T.A.B. (2017), Stochastic Actor-Oriented Models for Network Dynamics. Annual Review of Statistics and Its Application 4, 343–363.

Snijders, T.A.B., Koskinen, J., and Schweinberger, M. (2010). Maximum likelihood estimation for social network dynamics. Annals of Applied Statistics 4, 567–588.

Snijders, T.A.B., Steglich, C.E.G., and Schweinberger, Michael (2007), Modeling the co-evolution of networks and behavior. Pp. 41–71 in Longitudinal models in the behavioral and related sciences, edited by van Montfort, K., Oud, H., and Satorra, A.; Lawrence Erlbaum.

Steglich, C.E.G., Snijders, T.A.B., and Pearson, M.A. (2010), Dynamic networks and behavior: Separating selection from influence. Sociological Methodology 40, 329–393. Information about the implementation of the algorithm is in https://www.stats.ox.ac.uk/~snijders/siena/Siena_algorithms.pdf. Further see https://www.stats.ox.ac.uk/~snijders/siena/ .

See Also

siena07

Examples

mynet1 <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip)
myeff
myalgorithm <- sienaAlgorithmCreate(nsub=3, n3=200)
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
summary(ans)

RSiena documentation built on Nov. 2, 2023, 5:19 p.m.