[Rsiena-help] Help interpreting the estimates

[Nathan Abe]

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<https://stats.stackexchange.com/questions/296424/how-to-interpret-saom-estimates#>

I have been reviewing many articles and manuals and have a question
regarding the interpretations of SAOM and ERGM-based models. From what I
can tell, it seems the SAOM values, say just at the most basic edge level,
seem to be systematically different from the edge values in the ERGM-based
models in that the SAOM estimates are not as extreme as say TERGM or STERGM
estimates. I am observing this in my own research and see the same pattern
in Desmarais & Cranmer (2012) article (Micro-Level Interpretation of
Exponential Random Graph Models with Application to Estuary Networks). My
understanding is that both parameters are reported in log-odds. My
intuition is that the interpretation will be different due to the
actor-orientation of the SAOM and considering ties from the perspective of
each actor, but I can't quite wrap my mind around what the exact
interpretation would be in such a way that it explains the systematic
difference in values.

Below is an example of some R code and output for the SAOM model.

seed1 <- sample(1:10000, 1, replace = T)

g0 <- network(20, density = 0.36, directed = TRUE)

g1 <- simulate(~edges + mutual, nsim = 1,
                            coef = c(0.1025, 0),
                            basis = g0,
                            control = control.simulate(MCMC.burnin = 1000,
                                      MCMC.interval = 100), seed = seed1)

SAOM

betterAlgorithm <- sienaAlgorithmCreate(projname = "D", diagonalize = 0.2,
                                n3 = 4000)

X1 <- as.matrix(g0)
X2 <- as.matrix(g1)

mynet1 <- sienaDependent(array(c(X1, X2), dim = c(20, 20, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, recip)
ans3 <- siena07(betterAlgorithm, data = mydata, effects = myeff, batch = T)
summary(ans3)## Estimates, standard errors and convergence t-ratios##
##                               Estimate   Standard   Convergence ##
                                          Error      t-ratio   ## ##
Rate parameters: ##   0       Rate parameter      18.6356  ( 2.8187
)             ##   1. eval outdegree (density)  0.0640  ( 0.1067   )
0.0040    ##   2. eval reciprocity          0.2621  ( 0.1626   )
0.0231    ## ## Total of 5011 iteration steps.## ## Overall maximum
convergence ratio:    0.0369 ## ## Covariance matrix of estimates
(correlations below diagonal)## ##        0.011       -0.013##
-0.753        0.026## ## Derivative matrix of expected statistics X by
parameters:## ##      137.587      146.741##       69.844
115.650## ## Covariance matrix of X (correlations below diagonal):##
##       93.473      101.707##        0.844      155.425

I would greatly appreciate any insight on this that you have!
Best,
Nate
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