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我正在使用ergm包R来探索网络数据的指数随机图模型。这是网络：

gn
 Network attributes:
  vertices = 678 
  directed = TRUE 
  hyper = FALSE 
  loops = FALSE 
  multiple = FALSE 
  bipartite = FALSE 
  total edges= 1663 
    missing edges= 0 
    non-missing edges= 1663 

 Vertex attribute names: 
    indegree membership num_tweets vertex.names 

 Edge attribute names not shown

edges在拟合更简单的模型后，一个带有and的术语，mutual一个带有edges, mutual, and的模型nodefactor("membership", base = 4)（其中membership是一个具有四个级别的因子），我拟合了一个nodematch("membership", diff = T)添加的模型，如下所示：

model1 <- ergm(gn ~ edges + 
                    mutual + 
                    nodefactor("membership", base = 4) +
                    nodematch("membership", diff = T)
                    )

虽然以前的模型融合了，但这个模型导致了这样的信息：

MCMLE estimation did not converge after 20 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.

按照指示，我看了看?control.ergm：Passing control.ergm(init=coef(prev.fit)) can be used to “resume” an uncoverged ergm run, but see enformulate.curved.

好的，这听起来不错，我搜索了如何使用，并从这个页面control.ergm中找到了一个示例，我通过作为参数传递来测试它是否有效，它将从 20 增加到 50。然后我做了以下事情：control = control.ergm(MCMLE.maxit = 50)number of times the parameters for the MCMC should be updated by maximizing the MCMC likelihood

model1a <- ergm(gn ~ edges + 
                    mutual + 
                    nodefactor("membership", base = 4) +
                    nodematch("membership", diff = T),
                    control = control.ergm(init=coef(prev.fit))
                    )

但是，返回此消息：Error in coef(prev.fit) : object 'prev.fit' not found。

我还尝试传递模型对象（即model1a）而不是prev.fit，但这导致了这个不太高效的错误：

Error in if (drop) { : argument is not interpretable as logical
In addition: Warning message:
In if (drop) { :
  the condition has length > 1 and only the first element will be used

那么，我如何通过传递control.ergm(init = coef(prev.fit))或通过不同的方法“恢复”未收敛的模型？

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