RSiena (version 1.1-232)

RSiena-package: 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.

Package "RSienaTest" is the development version, and is distributed through R-Forge, see http://r-forge.r-project.org/R/?group_id=461. Package "RSiena" is the official release.

Details

Use siena07 to fit a model.

Data objects can be created from matrices and vectors using sienaDependent, coCovar etc., and finally sienaDataCreate. Another possibility (but with less flexibility) is via the Gui displayed by siena01Gui, or via sienaDataCreateFromSession.

Effects are selected using an effects object, which can be created using getEffects.

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

More detailed help is available in the manual which you can display using RShowDoc("RSiena_Manual", package="RSiena")

Package:	RSiena
Type:	Package
Version:	1.1-232
Date:	2013-06-18
Depends:	R (>= 2.15.0)
Imports:	Matrix
Suggests:	tcltk, network, codetools, lattice, MASS, parallel, xtable, tools
SystemRequirements:	GNU make, tcl/tk 8.5, Tktable
License:	GPL-2
LazyData:	yes
NeedsCompilation: yes	BuildResaveData:
no	Package:

References

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

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

• Snijders, Tom A.B., Steglich, Christian 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 Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum.

• Steglich, Christian E. G., Snijders, Tom A. B., and Pearson, Michael A. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40, 329-393.

• Further see the extensive manual accessible by the command RShowDoc("RSiena_Manual", package="RSiena") and the website http://www.stats.ox.ac.uk/~snijders/siena/ .

See Also

siena07

Examples

# NOT RUN {
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)
# }