RSiena interface: Difference between revisions

From visone manual
Jump to navigation Jump to search
No edit summary
No edit summary
Line 2: Line 2:
Supported is the analysis of one-mode networks with fixed set of actors. Not supported are two-mode networks, multiple networks, multilevel (multi-group) networks, composition change of actors, and multi-parameter tests.
Supported is the analysis of one-mode networks with fixed set of actors. Not supported are two-mode networks, multiple networks, multilevel (multi-group) networks, composition change of actors, and multi-parameter tests.
For using the RSiena interface you need to define a [[network collection |network collection]] that contains your longitudinal networks.  
For using the RSiena interface you need to define a [[network collection |network collection]] that contains your longitudinal networks.  
There are two ways for defining a [[network collection |network collection]]. First, by connecting several open networks in the [[network collection manager|network collection manager]] and second, by loading an [[RSiena session file|RSiena session file]].
There are two ways for defining a [[network collection |network collection]]. First, by connecting several open networks in the [[collection manager|network collection manager]] and second, by loading an [[RSiena session file|RSiena session file]].





Revision as of 13:16, 4 February 2011

Longitudinal networks can be statistically analysed by using RSiena from within visone. Supported is the analysis of one-mode networks with fixed set of actors. Not supported are two-mode networks, multiple networks, multilevel (multi-group) networks, composition change of actors, and multi-parameter tests. For using the RSiena interface you need to define a network collection that contains your longitudinal networks. There are two ways for defining a network collection. First, by connecting several open networks in the network collection manager and second, by loading an RSiena session file.



add all of the open networks that belong to your longitudinal network. Here, the order of adding defines the oder of observations.