Can TERGM be used to fit bunch of nodecov(metrics)? #107
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SuchandraC
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Like we do with one network using ERGM.
fit<ergm(network~edges+nodecov("degree")+nodecov("betweenness")+nodecov("closeness")+nodecov("pagerank")+nodecov("eigen_c")+nodecov("clustcoeff")) summary(fit)
Can we do the same with bunch of network, using TERGM?
I did something like the following for 49 network files
tergm_fit <- tergm(net_y.dyn~edges +nodecov("authority")+nodecov("degree"), estimate = 'CMLE', times = c(0:48))
Got the following warning whenever I tried to use more than 1 nodecov matrics.
`Starting maximum pseudolikelihood estimation (MPLE):
Evaluating the predictor and response matrix.
Maximizing the pseudolikelihood.
Finished MPLE.
Warning: Model statistics ‘nodecov.degree’ are linear combinations of some set of preceding statistics at the current stage of the estimation. This may indicate that the model is nonidentifiable.
Stopping at the initial estimate.
Evaluating log-likelihood at the estimate.
Warning message:
In NetSeries(nw, times, NA.impute = control$CMLE.NA.impute) :
Active vertex set varies from time point to time point. Estimation may not work.
summary(res)
Call:
tergm(formula = net_y.dyn ~ edges + nodecov("authority") + nodecov("degree"),
estimate = "CMLE", times = c(0:48))
Conditional Maximum Likelihood Results:
Estimate Std. Error MCMC % z value Pr(>|z|)
edges -4.218242 0.013958 0 -302.21 <1e-04 ***
nodecov.authority 0.288308 0.009926 0 29.05 <1e-04 ***
nodecov.degree NA 0.000000 0 NA NA
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual Deviance: 868447 on 4223517 degrees of freedom
AIC: 868453 BIC: 868493 (Smaller is better. MC Std. Err. = 0)`
What am I doing wrong?
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