[statnet_help] ergm inquiry

[Michał Bojanowski]

Hi Julia,

Thanks. Some questions and hypotheses about model specification:

1. The network is directed. Are you sure you want nodefactor (nodes in
some groups have more ties than nodes in other groups irrespectively
of tie direction) rather than nodeofactor (same but for outgoing ties
only) or nodeifactor (same but for incoming ties only)?

2. Your model does not have any nodematch terms on the nodal
attributes. Is that intentional?

3. The ESP distribution is notably relatively flat. One mechanism that
might be responsible for that is strong community structure or
homophily on some observed attribute -- triangles get closed within
groups only and the count of ESP depends on group size. Do you have
several cohesive groups of varying size in the network?

m.

On Fri, Aug 11, 2023 at 4:51 PM Julia Vassey <vassey at usc.edu> wrote:

>

> Thank you Michal! Good to hear from you.

>

> Please, see the plots, attached. I have tried changing the decay

> parameter multiple times, and the model fit gets better when

> increasing the parameter: from 0 to 0.3 (gwesp(0.3, fixed = TRUE)),

> but beyond 0.3 the model starts having issues with convergence.

> p values for all esps are always very low.

>

> Thank you for helping!

>

> Julia

>

>

> On Fri, Aug 11, 2023 at 8:53 AM Michał Bojanowski <michal2992 at gmail.com> wrote:

> >

> > Hi Julia,

> >

> > Can you send the GOF plots attached? In what way does the ESP not fit

> > well? Perhaps it is a matter of changing the decay parameter?

> >

> > Michal

> >

> > On Wed, Aug 9, 2023 at 1:00 PM Julia Vassey <vassey at usc.edu> wrote:

> > >

> > >

> > > Dear All,

> > >

> > > This is my first time posting a question in statnet help. If the question needs to be posted/sent to a different email please let me know.

> > >

> > > I have a question related to ergm. I am running an ergm model on a directed unipartite network of ~100 nodes (certain social media users) and ~700 edges. The model includes geolocation attributes of the nodes (geographic regions) and themes the nodes post about on social media.  The model also includes terms (mutual, gwesp) provided in the code below.

> > >

> > > I am struggling with achieving a decent goodness of fit for edgewise shared partners using gof function. i/o degree, geodesic distance and model statistics look much better. I tried different things to try to improve edgewise shared partners, but nothing seems to work. The configuration below provides the best fit, however, I want to keep trying to improve the fit for edgewise shared partners. I appreciate any thoughts and comments on how to achieve this.

> > >

> > > model = ergm::ergm(netg_infl ~ edges + nodefactor('region_code', levels = -4) + nodematch('region_code', diff = T, levels = -3) + nodefactor('topic_sum_binary', levels = -LARGEST) + nodefactor('marijuana_recode', levels = -c(1,3)) + nodefactor('nature_recode', levels = -c(1,3)) + nodefactor('health_life_recode', levels = -c(1,3)) + nodefactor('gaming_recode', levels = -c(1,3)) + nodefactor('food_recode', levels = -c(1,3)) + nodefactor('clothing_recode', levels = -c(1,3)) + mutual  + gwesp(0.28, fixed = TRUE) + offset(isolates), offset.coef = -Inf, control=control.ergm(parallel=2, parallel.type="PSOCK"))

> > >

> > > Thank you,

> > >

> > >

> > > --

> > > Julia Vassey

> > > Health Behavior Research

> > > Department of Population and Public Health Sciences

> > > Keck School of Medicine

> > > University of Southern California

> > >

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