使用 R 中的传递权重对有向 ERGM 进行建模价值

Question

我目前正在 R 中使用 ergm.count 包来建模有价值的定向网络。邻接矩阵可以通过这个link下载。该网络由 84 个节点和 75 个加权关系组成。

library(ergm.count)
mat <- as.matrix(read.csv("./mat.csv",header=TRUE)[,-1])
net<-as.network(mat , directed=TRUE, matrix.type="a", 
                      ignore.eval=FALSE, names.eval="cnts"
                     )
m1<-ergm(net~ transitiveweights("min", "max", "min"),
         reference=~Poisson,
         response="cnts",
         control=control.ergm(parallel=6, parallel.type="PSOCK",
                              main.method="MCMLE")
)

但是，当我运行上面的代码时，进度感觉像是永远卡住了，而且cpu负载非常高。

我不知道发生了什么以及我的代码有什么问题。有什么方法可以加快或优化进度。顺便说一句，我的 R 版本是 4.3.2，ergm 版本是 4.5.0，这是拟合输出...

Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:1.5e-04
The log-likelihood improved by 0.07426.
Iteration 2 of at most 60:
Convergence test P-value:6.2e-03
The log-likelihood improved by 0.03396.
Iteration 3 of at most 60:
Convergence test P-value:5.9e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001075.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:

编辑：

等待三个小时并打印日志后，进程停止并显示：

> source("/root/transfer_analysis/test.R", encoding = "UTF-8")
Evaluating network in model.
Initializing unconstrained Metropolis-Hastings proposal: ‘ergm.count:MH_DiscTNT’.
Initializing model...
Model initialized.
Using initial method 'CD'.
Fitting initial model.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:1.5e-06
The log-likelihood improved by 0.1578.
Iteration 2 of at most 60:
Convergence test P-value:2.6e-02
The log-likelihood improved by 0.02119.
Iteration 3 of at most 60:
Convergence test P-value:4.6e-01
The log-likelihood improved by 0.002026.
Iteration 4 of at most 60:
Convergence test P-value:1e-01
The log-likelihood improved by 0.01116.
Iteration 5 of at most 60:
Convergence test P-value:1.4e-01
The log-likelihood improved by 0.007586.
Iteration 6 of at most 60:
Convergence test P-value:1e+00
Convergence detected. Stopping.
The log-likelihood improved by < 0.0001.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Density guard set to 10000 from an initial count of 75 edges.

Iteration 1 of at most 60 with parameter:
transitiveweights.min.max.min 
                    -1.483437 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Error in ergm.MCMLE(init, s, s.obs, control = control, verbose = verbose,  : 
  Unconstrained MCMC sampling did not mix at all. Optimization cannot continue.
Additional warning message:
In ergm_MCMC_sample(s, control, theta = mcmc.init, verbose = max(verbose -  :
  Unable to reach target effective size in iterations alotted.

我已经检查了另一个关于“无法在分配的迭代中达到目标有效大小”的问题，但是这个拟合模型和公式似乎非常简单

任何帮助将不胜感激！

Answer 1

我怀疑由于模型规范，估计会陷入困境。我建议您在拟合复杂模型（例如带有

transitiveweights

的模型）之前先探索数据。我不知道您要建模的系统是什么，所以显然下面的内容与物质无关：

1. 查看边缘值的分布形状。泊松参考测量是正确的规范吗？
2. 尝试拟合更简单的模型，从同质模型开始，即仅使用常数（例如

   sum

   ）。这些是否适合数据，如果不适合，如何？
3. 也许具有

   transitiveweights

   的模型也应该具有

   sum

   。
4. 您是否有任何节点协变量可用于对您拥有的三元效应旁边的网络进行建模？