Variances in MMMC model with pseudo-level for overdispersion

[BramVanLeeuwen]

Dear all,

I am running a multiple membership multiple classification model with individuals nested in schools, areas and (one or more) friendship networks (cf., Tranmer et al. 2014). My outcome is a count variable. To account for overdispersion, I have added a pseudo-level as suggested elsewhere on this forum (http://www.cmm.bristol.ac.uk/forum/view ... ?f=3&t=774). My code for a model without predictors looks as follows:

sort school area egonet individual individual
quietly runmlwin count cons, ///
level5(school:cons) ///
level4(area:cons) ///
level3(egonet:cons) ///
level2(individual:cons) level1(individual:) ///
discrete(distribution(poisson) link(log)) rigls nopause

runmlwin count cons, ///
level5(school:cons) ///
level4(area:cons) ///
level3(egonet:cons, mmids(egonet-egonet7) mmweights(weight1-weight7)) ///
level2(individual:cons) level1(individual:) ///
discrete(distribution(poisson) link(log)) mcmc(cc burnin(10000) chain(100000)) initsprevious

The model output contains, in addition to an estimate for the intercept (cons), a random effect parameter for school, area, and egonet, and an individual-level random effect that’s supposed to take care of the overdispersion, like this:

Level 5: school - var(cons) - estimate
Level 4: area - var(cons) - estimate
Level 3: egonet - var(cons) - estimate
Level 2: individual - var(cons) - estimate

Given this output, I want to compare the amount of variance at different levels – individual, school, area, egonet – and how much these can be accounted for by predictors to be added later, but I am struggling to understand how to do this for this particular model, given the Poisson distribution, the inclusion of a pseudo-level, and the use of a multiple membership structure for the egonets. So that is my question. Help would be much appreciated!

Many thanks,
Bram van Leeuwen


Tranmer, M., Steel, D., & Browne, W. J. (2014). Multiple‐membership multiple‐classification models for social network and group dependences. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177(2), 439-455.

[GeorgeLeckie]

Dear Bram,

I think you would be best posting this on the JISC multilevel email list as it more a general question about interpreting changes in the absolute and relative magnitude of variance parameters as you add covariates rather than a runmlwin specific question. Needless to say, as soon as you move from continuous to discrete response models these issues are no longer straightforward.

Best wishes

George

[BramVanLeeuwen]

Dear George,

Thank you for the advice. I'll post the question at JISCMail.

Best,
Bram