Difference between revisions of "RSiena (tutorial)"

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==Defining longitudinal network data==
 
==Defining longitudinal network data==
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Stochastic Actor Oriented Models (SAOMs) are designed for analysing longitudinal network data given as network panel data, i.e., a sequence of networks representing one network observed at several moments in time.
  
 
==Adding individual or dyadic covariates==
 
==Adding individual or dyadic covariates==

Revision as of 16:16, 7 September 2011

This trail illustrates how to analyze longitudinal network data by using RSiena from within visone. We assume that you have installed R on your computer and configured the R connection as it is explained in the installation trail. We also assume that you have basic understanding about how to work with visone as it is, for instance, explained in the trail on visualization and analysis and basic knowledge of stochastic actor-oriented models (SOAM).

To follow the steps illustrated in this trail you should download the longitudinal network files classroom_graph1.graphml - classroom_graph4.graphml zipped in Classroom_graphmls.zip and explained on page Knecht Classroom (data).

Defining longitudinal network data

Stochastic Actor Oriented Models (SAOMs) are designed for analysing longitudinal network data given as network panel data, i.e., a sequence of networks representing one network observed at several moments in time.

Adding individual or dyadic covariates

Specifying missing data or structurally fixed values

This option is not yet included in the current release visone-2.6.3 but will be in the next release!

Model specification and estimation

Simulation

Visualize simulated netwoks