[statnet_help] Fwd: help

Filippo Santi filippo.santi at unifi.it
Mon Feb 17 01:32:08 PST 2020


Dear all,

I am running an ERGM model on world level bilateral trade flows, using a
few additional covariates at node as well as at dyad level. I also add the
GWESP term.

I have several questions related to mcmc.diagnostic(), which I launch to
check for degeneracy. I am sure that they might turn out to be very simple
those who know the principle of MCMC, but unfortunately, it is not my case,
and I am a bit stuck for what concerns the interpretation.

In particular:

1) what do the axes refer to?
2) How should I interpret the fact that my estimates are not fluctuating
around zero, but still relatively stable around a certain value?
3) My distribution is skewed (often to the left) and this is reflected also
in the left hand side plot, with the initialised value lying below the
central value. What is the exact meaning of this? Where should I look to
try to solve this?
4) To make things faster, I used multiple cores to perform the estimation.
The diagnostic of some of the cores is often completely shifted with
respect to the other. Is it normal? if it is not, what could explain that?

I report below the model. The MCMC.DIAGNOSTIC() plots are instead attached
as PNG.

Thank you in advance for any help.

--
*Filippo Santi*
*Phd in Development Economics*


*-----------------------------------------------------*









*grav.model.trade.12 <- ergm(net.trade.2010.redux ~ edges + mutual
+ edgecov(net.migration.2010, 'weight')
+ nodeicov('gdpcap') + nodeocov('gdpcap')
+ edgecov(net.distance.2010, 'log_dist') +
edgecov(net.colony.2010, 'colony') +
edgecov(net.contiguity.2010, 'contig') +
edgecov(net.language.2010, 'comlang_ethno') +
gwesp(decay = 0.25, fixed = TRUE) # Didier et al. 2019: decay = 0.25
standard in literature... , eval.loglik = T,
control=control.ergm(seed=1,MCMC.burnin = 50000,MCMC.interval = 3000,

MCMC.samplesize = 70000, MCMLE.maxit = 10000,
main.method = "Stepping",
parallel=np,
parallel.type
="PSOCK"))summary(grav.model.trade.12)==========================Summary of
model fit==========================Formula: net.trade.2010.redux ~ edges
+ mutual + edgecov(net.migration.2010, "weight") + nodeicov("gdpcap") +
nodeocov("gdpcap") + edgecov(net.distance.2010, "log_dist") +
edgecov(net.colony.2010, "colony") + edgecov(net.contiguity.2010,
"contig") + edgecov(net.language.2010, "comlang_ethno") + gwesp(decay =
0.25, fixed = TRUE)Iterations: NA Stepping MLE Results:
Estimate Std. Error MCMC % z value Pr(>|z|) edges
-6.675e+00 8.859e-12 0 -7.534e+11 < 1e-04 ***mutual
2.584e+00 3.346e-12 0 7.722e+11 < 1e-04 ***edgecov.weight
6.874e-07 1.842e-07 0 3.731e+00 0.000191 ***nodeicov.gdpcap
6.671e-06 1.057e-06 0 6.313e+00 < 1e-04 ***nodeocov.gdpcap
2.436e-06 1.081e-06 0 2.253e+00 0.024287 * edgecov.log_dist
-3.065e-01 7.275e-11 0 -4.213e+09 < 1e-04 ***edgecov.colony
6.794e-01 3.086e-13 0 2.202e+12 < 1e-04 ***edgecov.contig
8.230e-01 5.504e-13 0 1.495e+12 < 1e-04 ***edgecov.comlang_ethno
2.955e-01 1.770e-12 0 1.670e+11 < 1e-04 ***gwesp.fixed.0.25
4.617e+00 1.125e-11 0 4.105e+11 < 1e-04 ***---Signif. codes: 0
‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Null Deviance: 36606 on
26406 degrees of freedom Residual Deviance: 11473 on 26396 degrees of
freedom AIC: 11493 BIC: 11575 (Smaller is better.)#
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