在随机网络的 list() 的嵌套循环中使用 lapply 的错误消息

如何解决在随机网络的 list() 的嵌套循环中使用 lapply 的错误消息

我正在制作随机网络列表并生成其信息的日期框架。我在嵌套的代码循环中执行此操作。我的代码没有完成，当我在我的 .RMD 块中停止运行时（在 RStudio 崩溃之后），我收到以下错误消息：

no loop for break/next,jumping to top level
2.
FUN(X[[i]],...)
1.
lapply(random_nws,cluster_optimal)

这是相同格式的代码示例。

random_nw_metrics <- data.frame( #to fill in
 
# Community 
  "count_communities" = NA,"ave_membership" = NA,"sd_membership" = NA,"modularity" = NA,# General Network 
  # "assortativity" = NA,"ave_distance" = NA,"degree_ave" = NA,"degree_sd" = NA,"density" = NA,"diameter" = NA,"reciprocity" = NA,"transitivity" = NA
) 


for (i in 1:180){ # it was "i in 1:nrow(df)",but that's not useful to you
  
set.seed(1)
random_nws <- list()
for (j in seq_len(1000L)) {
  
    Start = sample(15,200,replace=TRUE)  # "15" and "20" change based on values from the df
    End   = sample(15,replace=TRUE)
    df = data.frame(Start,End)
    
    ass.label <- data.frame("node" = 1:15,"type" = 0) # for assortativity labels
    ass.label$type <- +(match(ass.label$node,sample(x = 15,size = 5) ) > 0)
    ass.label[c("node","type")][is.na(ass.label[c("node","type")])] <- 0
    
    random_nws[[j]]<- graph_from_data_frame(df,vertices = ass.label,directed=TRUE)
}


random_cs <- lapply(random_nws,cluster_optimal)
nw <- random_nws[[i]]

random_nw_metrics <- rbind(random_nw_metrics,data.frame(
    
# Community 
  "count_communities" = mean(sapply(random_cs,function(x) mean(length(x)))),"ave_membership" = mean(sapply(random_cs,function(x) mean(membership(x)))),"sd_membership" = mean(sapply(random_cs,function(x) sd(membership(x)))),"modularity" = mean(sapply(random_cs,function(x) mean(modularity(x)))),# General Network 
  # "assortativity" = mean(sapply(random_nws,function(x) mean(assortativity(x,V(x)$Type,directed = T)))),# this isn't working ignore it for now
  "ave_distance" = mean(sapply(random_nws,function(x) mean(mean_distance(x)))),"degree_ave" = mean(sapply(random_nws,function(x) mean(degree(x)))),"degree_sd" = mean(sapply(random_nws,function(x) sd(degree(x)))),"density" = mean(sapply(random_nws,function(x) mean(edge_density(x)))),"diameter" = mean(sapply(random_nws,function(x) mean(diameter(x)))),"reciprocity" = mean(sapply(random_nws,function(x) mean(reciprocity(x)))),"transitivity" = mean(sapply(random_nws,function(x) mean(transitivity(x))))
)) 

}

我将不胜感激，我不知道该怎么做，因为我的一些措施需要社区。​​p>

解决方法

我认为您的代码没有任何问题。你可以试试下面的代码，这样可能更有效地得到想要的输出

nw_metrics <- replicate(180,{
  random_nws <- replicate(
    1000L,{
      Start <- sample(15,200,replace = TRUE)
      End <- sample(15,replace = TRUE)
      df <- data.frame(Start,End)
      ass.label <- data.frame("node" = 1:15,"type" = 0) # for assortativity labels
      ass.label$type <- +(match(ass.label$node,sample(x = 15,size = 5)) > 0)
      ass.label[c("node","type")][is.na(ass.label[c("node","type")])] <- 0
      list(graph_from_data_frame(df,vertices = ass.label,directed = TRUE))
    }
  )
  random_cs <- lapply(random_nws,cluster_optimal)
  community <- rowMeans(
    sapply(
      random_cs,function(x) {
        mbx <- membership(x)
        c(
          count_communities = mean(length(x)),ave_membership = mean(mbx),sd_membership = sd(mbx),modularity = mean(modularity(x))
        )
      }
    )
  )
  generalnetwork <- rowMeans(
    sapply(
      random_nws,function(x) {
        degx <- degree(x)
        c(
          ave_distance = mean(mean_distance(x)),degree_ave = mean(degx),degree_sd = sd(degx),density = mean(edge_density(x)),diameter = mean(diameter(x)),reciprocity = mean(reciprocity(x)),transitivity = mean(transitivity(x))
        )
      }
    )
  )
  list(c(community,generalnetwork))
})
random_nw_metrics <- data.frame(do.call(rbind,nw_metrics))

输出看起来像

  count_communities ave_membership sd_membership modularity ave_distance
1               3.3       2.220000     0.9148421  0.2031175     1.421429
2               3.6       2.220000     1.0218422  0.1942025     1.403333
3               3.6       2.360000     0.9869272  0.2092750     1.415714
4               3.3       2.060000     0.9003820  0.1961875     1.400000
5               3.7       2.233333     1.0169882  0.1933500     1.413810
  degree_ave degree_sd  density diameter reciprocity transitivity
1   26.66667  4.702093 0.952381      2.4   0.4487476    0.8116972
2   26.66667  4.827348 0.952381      2.2   0.4631853    0.8439533
3   26.66667  5.058985 0.952381      2.0   0.4384173    0.8207699
4   26.66667  4.833340 0.952381      2.2   0.4490952    0.8464840
5   26.66667  4.816635 0.952381      2.2   0.4396883    0.8309121