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On Wed, 7 Aug 2013, Weihua An wrote:

> ***** To join INSNA, visit http://www.insna.org *****
>
> 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

Nice. This is a perfect example: if, by design, your nodes have a fixed
number of edges, you don't need nodefactor terms.

> 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 the variation in edges by group is insignificant, then this is
justifiable.

> 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).

You can make the reference category (base) be either level.

> 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.

Not really -- in your case, there is no correlation btwn nodefactor and
nodematch (b/c there is essentially no variation in nodefactor). So
dropping the nodefactor term will have no impact on the estimate of
nodematch (try it and see if this is true). But this absence of
correlation will not be true in general. When it there is a correlation,
you can't just drop the nodefactor terms and interpret the nodematch terms
naively. The interpretation of nodematch terms will be unclear if, for
example, there is a difference in the level of homophily for groups that
are more (or less) active.

The kind of interpretability you're pointing to here -- the reference
category for factor level comparisons -- can be handled by directly
setting which factor level to use as a reference category.

You haven't mentioned looking at differential homophily, but that's
another possible option.

best,
mm
>
> 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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>>
>> ****************************************************************
>> Professor of Sociology and Statistics
>> Director, UWCFAR Sociobehavioral and Prevention Research Core
>> Box 354322
>> University of Washington
>> Seattle, WA 98195-4322
>>
>> Office: (206) 685-3402
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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/
>
> _____________________________________________________________________
> SOCNET is a service of INSNA, the professional association for social
> network researchers (http://www.insna.org). To unsubscribe, send
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****************************************************************
  Professor of Sociology and Statistics
  Director, UWCFAR Sociobehavioral and Prevention Research Core
  Box 354322
  University of Washington
  Seattle, WA 98195-4322

  Office: (206) 685-3402
  Dept Office: (206) 543-5882, 543-7237
  Fax: (206) 685-7419

[log in to unmask]
http://faculty.washington.edu/morrism/

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
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