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This is a question about model choice with categorical independent
I have categorical attributes, and network data.
When I look at the attributes one-by-one, and produce contingency tables
with the dyadic data, each attribute has a highly significant chi-squared.
When I look at them with QAP multiple regressions approach, the R-squared is
practically zero. The network is sparse with about a thousand nodes.
Of course, a QAP regression with a binary dependent variables is not really
appropriate. I think when coefficients are referred to as probabilities in
the QAP linear regression context, it is not appropriate. Like they are
referred to here:
But even when I calculate geodesics (and have an interval scale dependent
variable with an approximately normal distribution) I have no R-squared.
Is there a way to model multiple, node level categorical independents, and a
dyadic dependent variable?
A logistic regression model?
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