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Hi,

I have a similar problem. In my networks, by design each person has a
fixed (or very close) number of connections. That means no main
effects of any covariate will be significant. So our interested is in
segregation in friendship networks. Indeed, we find almost no
covariates has significant predictive effect, but a lot of the
nodematch terms are significant, when both of the terms are included.
We are thinking about dropping the nodefactor terms as well, for
simpler and clearer interpretation.

If we keep only the nodematch terms, the base ties will be the
asymmetric ties (i.e., the ties that are between people with different
binary attributes, assuming the factors are binary). If we keep both
nodefactor and nodematch, the base ties seem to be the ties from/to
those with the passive attribute (zero in the binary case). So if our
understanding is correct, whether to include nodefactor in a model
depends on what base ties you want to have and what theoretical
questions you want to ask.

Does this make sense to people with similar questions?


best,

Weihua

On Tue, Aug 6, 2013 at 1:16 PM, martina morris <[log in to unmask]> wrote:
> ***** To join INSNA, visit http://www.insna.org *****
>
> Hi Jeff,
>
> In general, you probably wouldn't want to fit a model with nodematch but not
> nodefactor. It's a bit like fitting an interaction without the main effects
> -- to the extent that the two are correlated, the interpretation of the
> terms changes when one is excluded. If the nodematch becomes insignificant
> when nodefactor is added, it could mean a couple of different things.
>
> I'd suggest taking a look at the actual mixing pattern (using the
> "mixingmatrix" function), and getting some sense of what is going on in your
> data.
>
> best,
> Martina
>
>
> On Tue, 6 Aug 2013, Jeff Webb wrote:
>
>> ***** To join INSNA, visit http://www.insna.org ***** Dear list members,
>>
>> I'm fitting an ergm model to a small network with 20 actors. The feature
>> of the
>> network in which I'm most interested is homophily among members of a
>> subgroup,
>> designated "ea." I test this with nodematch("ea"), which is included in
>> the model
>> along with structural terms, edges and GWESP ; the effect is positive and
>> significant. However, Goodreau et al. (2008) note: "if one is including
>> nodematch
>> terms in a model, one would typically also include nodefactor terms for
>> the same
>> attributes." When I add nodefactor("ea") to the model, the effect of
>> nodematch("ea") is no longer significant. Any thoughts on when—if ever—one
>> would
>> fit a model using nodematch() without nodefactor()?
>>
>> A related question. I would also like to fit a tergm model to two waves
>> of the
>> above network. In this case, unfortunately, the formation part of the
>> model will
>> not converge when I include the interaction term (node match()) along with
>> the
>> main effect, but it will converge if I include only the the interaction
>> term.
>> Would it be reasonable to leave out the main effect in order to get a
>> coefficient
>> for the interaction? Thanks in advance for your replies.
>> Jeff
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>
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--
Weihua (Edward) An

Assistant Professor of Sociology and Statistics
Indiana University Bloomington
752 Ballantine Hall
1020 East Kirkwood Avenue
Bloomington, IN 47405-7103
http://mypage.iu.edu/~weihuaan/

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