consensus {sna}R Documentation

Estimate a Consensus Structure from Multiple Observations

Description

consensus estimates a central or consensus structure given multiple observations, using one of several algorithms.

Usage

consensus(dat, mode="digraph", diag=FALSE, method="central.graph", 
    tol=0.01)

Arguments

dat An m x n x n graph stack
mode ``digraph'' for directed data, else ``graph''
diag A boolean indicating whether the diagonals (loops) should be treated as data
method One of ``central.graph'', ``single.reweight'', ``PCA.reweight''
tol Tolerance for the iterative reweighting algorithm (not currently supported)

Details

The term ``consensus structure'' is used by a number of authors to reflect a notion of shared or common perceptions of social structure among a set of observers. As there are many interpretations of what is meant by ``consensus'' (and as to how best to estimate it), several algorithms are employed here:

  1. central.graph: Estimate the consensus structure using the central graph. This correponds to a ``median response'' notion of consensus.
  2. single.reweight: Estimate the consensus structure using subject responses, reweighted by mean graph correlation. This corresponds to an ``expertise-weighted vote'' notion of consensus.
  3. PCA.reweight: Estimate the consensus using the (scores on the) first component of a network PCA. This corresponds to a ``shared theme'' or ``common element'' notion of consensus.

Note that the reweighted algorithms are not dichotomized by default; since these return valued graphs, dichotomization may be desirable prior to use.

It should be noted that a model for estimating an underlying criterion structure from multiple informant reports is provided in bbnam; if your goal is to reconstruct an ``objective'' network from informant reports, this may prove more useful.

Value

An adjacency matrix representing the consensus structure

Note

Eventually, this routine will also support the (excellent) consensus methods of Romney and Batchelder; since these are similar in many respects to the bbnam model, users may wish to try this alternative for now.

Author(s)

Carter T. Butts buttsc@uci.edu

References

Banks, D.L., and Carley, K.M. (1994). ``Metric Inference for Social Networks.'' Journal of Classification, 11(1), 121-49.

Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Inter-Structural Analysis.'' CASOS Working Paper, Carnegie Mellon University.

Krackhardt, D. (1987). ``Cognitive Social Structures.'' Social Networks, 9, 109-134.

See Also

bbnam, centralgraph

Examples


#Generate some test data
g<-rgraph(5)
g.pobs<-g*0.9+(1-g)*0.5
g.obs<-rgraph(5,5,tprob=g.pobs)

#Find some consensus structures
consensus(g.obs)                           #Central graph
consensus(g.obs,method="single.reweight")  #Single reweighting
consensus(g.obs,method="PCA.reweight")     #1st component in network PCA

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