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Occurring cliques in association graphs represent connected
components of dependent variables, and by comparing the graphs for
different thresholds, specific structural models of multivariate
dependence can be suggested and tested. The function
div_gof() allows such hypothesis tests for pairwise
independence of \(X\) and \(Y\): \(X \bot
Y\), and pairwise independence conditional a third variable \(Z\): \(X\bot
Y|Z\).
For the running example using
## status gender office years age practice lawschool cowork advice friend
## 1 3 3 0 8 8 1 0 0 3 2
## 2 3 3 3 5 8 3 0 0 0 0
## 3 3 3 3 5 8 2 0 0 1 0
## 4 3 3 0 8 8 1 6 0 1 2
## 5 3 3 0 8 8 0 6 0 1 1
## 6 3 3 1 7 8 1 6 0 1 1To test friend\(\bot\)
cowork\(|\)advice, that is whether
dyad variable friend is independent of cowork
given advice we use the function as shown below:
## the specified model of conditional independence cannot be rejected## D df(D)
## 1 0.94 12Not specifying argument var_cond would instead test
friend\(\bot\)cowork without any
conditioning.
Frank, O., & Shafie, T. (2016). Multivariate entropy analysis of network data. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 129(1), 45-63. link
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