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--></style></head><body lang=DE link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal>Dear all,<o:p></o:p></p><p class=MsoNormal><span lang=EN-US>I hope you had enjoyable holidays.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>I’m currently working on a large two-mode network (500 events, 9500 actors). My current specification includes mostly effects such as nodematch or b2star in combination with attributes and gives theoretically meaningful results. I initially thought that the fit (both upon visual inspection and based on p-values) was no reason for concern, but upon closer inspection it seems that the MCMC diagnostics point to model degeneracy.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>As far as I can tell the trace and density plots are all fine. The Geweke-diagnostics are all far from zero on all individual sample statistics but the auto-correlation, while constantly decreasing, takes quite a while to get close to zero (my understanding is that any statistic after lag 0 should be close to zero). To improve this, I tried adding geometrically weighted effects (gwb1degree, gwb1dsp, ..). It seems that no matter what combination of effects I try here (effects, decay and cutoff), I can’t get the models to converge. My guess is that this is because the network is highly formalized and not as organic as a school class for example. Now, I tried substituting the attribute-based b2star-effects with gwb2degree(attr=”covar_name”, decay=0.5, fixed=T) and these work fine, which is good. However, I am at a loss on how to interpret this, as each gwb2degree-effect produces two significant parameters (the attributes are dummy covariates, so one for each value) with basically the same parameter estimate. The estimated parameters are also very large.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>In sum, I have four questions:<o:p></o:p></span></p><ol style='margin-top:0cm' start=1 type=1><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>Are the MCMC-diagnostics as described here actually a reason for concern?<o:p></o:p></span></li></ol><p class=MsoListParagraph><span lang=EN-US><o:p> </o:p></span></p><ol style='margin-top:0cm' start=2 type=1><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>How do I interpret the parameters that gwb2degree produces?<o:p></o:p></span></li></ol><p class=MsoListParagraph><span lang=EN-US><o:p> </o:p></span></p><ol style='margin-top:0cm' start=3 type=1><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>Anything else I can do to improve my model? My controls currently are: (MCMLE.maxit=200, MCMC.burnin=3000, MCMC.interval=5000, MCMC.samplesize=10000)<o:p></o:p></span></li></ol><p class=MsoListParagraph><span lang=EN-US><o:p> </o:p></span></p><ol style='margin-top:0cm' start=4 type=1><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>How can I specify the constraint that actors have a minimum degree of 1 and events have a maximum degree of 40? This should also help with further estimations, I hope.<o:p></o:p></span></li></ol><p class=MsoListParagraph><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Cheers & enjoy the rest of the year,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Steffen<o:p></o:p></span></p></div></body></html>