如何在igraph for R中排列网络?
我正试图为R中的Monte Carlo程序编写一个代码。我的目标是估计为包igraph格式化的加权、单方、无向网络计算的度量的重要性 到目前为止,我在代码中包括了以下步骤: 1.创建加权、单方、无向网络,并计算观察到的Louvain模块化 3.排列保留其特征的原始网络,计算所有随机化网络的Louvain模性,并在向量中编译结果如何在igraph for R中排列网络?,r,network-programming,igraph,montecarlo,R,Network Programming,Igraph,Montecarlo,我正试图为R中的Monte Carlo程序编写一个代码。我的目标是估计为包igraph格式化的加权、单方、无向网络计算的度量的重要性 到目前为止,我在代码中包括了以下步骤: 1.创建加权、单方、无向网络,并计算观察到的Louvain模块化 3.排列保留其特征的原始网络,计算所有随机化网络的Louvain模性,并在向量中编译结果 i您的错误是由于维度与随机化模块化矩阵和一些随机化模块化结果不匹配造成的。在您的示例中,您的矩阵最终是[1 x Nperm],但有时在排列过程中返回2个模块化分数。要解决
i您的错误是由于维度与随机化模块化矩阵和一些随机化模块化结果不匹配造成的。在您的示例中,您的矩阵最终是[1 x Nperm]
,但有时在排列过程中返回2个模块化分数。要解决这个问题,我只需将结果存储在一个列表中。由于模块化分数不匹配,因此需要调整分析的其余部分
library(igraph)
nodes <- read.delim("nodes.txt")
links <- read.delim("links.txt")
anurosnet <- graph_from_data_frame(d=links, vertices=nodes, directed=F)
anurosnet
modularity1 = cluster_louvain(anurosnet)
modularity1$modularity #observed value
obs <- modularity1$modularity
obs
real<-data.frame(obs)
real
Nperm = 100 #I am starting with a low n, but intend to use at least 1000 permutations
#randomized.modularity <- matrix(nrow=length(obs),ncol=Nperm+1)
#row.names(randomized.modularity) <- names(obs)
randomized.modularity <- list()
randomized.modularity[1] <- obs
randomized.modularity
for(i in 1:Nperm){
randomnet <- rewire(anurosnet, with=each_edge(0.5)) #rewire vertices with constant probability
E(randomnet)$weight <- sample(E(anurosnet)$weight) #shuffle initial weights and assign them randomly to edges
mod <- (cluster_louvain(randomnet))
mod$modularity
linha = mod$modularity
randomized.modularity <- c(randomized.modularity, list(linha))
}
randomized.modularity
库(igraph)
非常感谢!这很奇怪,我不知道cluster_louvain函数一次可以产生2个值。它应该是一个单一的值。我要问问它的作者。同时也感谢您提供了更干净的循环版本!如果您的排列创建了两个未连接的子图,那么可能会返回两个分数啊,好的。我认为它应该始终是一个分数,与图形完全连接或划分为多个分区无关。是否有方法将结果列表转换为数据帧(Nperm rows x 1 column),以便具有两个或更多值的插槽成为数据帧中的单行?类似这样:rm_df
Nperm = 9 #I am starting with a low n, but intend to use at least 1000 permutations
randomized.modularity=matrix(nrow=length(obs),ncol=Nperm+1)
row.names(randomized.modularity)=names(obs)
randomized.modularity[,1]=obs
randomized.modularity
i<-1
while(i<=Nperm){
randomnet <- rewire(anurosnet, with=each_edge(0.5)) #rewire vertices with constant probability
E(randomnet)$weight <- sample(E(anurosnet)$weight) #shuffle initial weights and assign them randomly to edges
mod<-(cluster_louvain(randomnet))
mod$modularity
linha = mod$modularity
randomized.modularity[,i+1]=linha
print(i)
i=i+1
}
randomized.modularity #Here the result is not as expected
niveis<-row.names(randomized.modularity)
for(k in niveis)
{
if(any(is.na(randomized.modularity[k,]) == TRUE))
{
print(c(k, "metrica tem NA"))
} else {
nome.arq<- paste("modularity",k,".png", sep="")
png(filename= nome.arq, res= 300, height= 15, width=21, unit="cm")
plot(density(randomized.modularity[k,]), main="Observed vs. randomized",)
abline(v=obs[k], col="red", lwd=2, xlab="")
dev.off()
print(k)
nome.arq<- paste("Patefield_Null_mean_sd_",k,".txt", sep="")
write.table(cbind(mean(randomized.modularity[k,]),sd(randomized.modularity[k,])), file=paste(nome.arq,sep=""),
sep=" ",row.names=TRUE,col.names=FALSE)
}
}
significance=matrix(nrow=nrow(randomized.modularity),ncol=3)
row.names(significance)=row.names(randomized.modularity)
colnames(significance)=c("p (rand <= obs)", "p (rand >= obs)", "p (rand=obs)")
signif.sup=function(x) sum(x>=x[1])/length(x)
signif.inf=function(x) sum(x<=x[1])/length(x)
signif.two=function(x) ifelse(min(x)*2>1,1,min(x)*2)
significance[,1]=apply(randomized.modularity,1,signif.inf)
significance[,2]=apply(randomized.modularity,1,signif.sup)
significance[,3]=apply(significance[,-3],1,signif.two)
significance
library(igraph)
nodes <- read.delim("nodes.txt")
links <- read.delim("links.txt")
anurosnet <- graph_from_data_frame(d=links, vertices=nodes, directed=F)
anurosnet
modularity1 = cluster_louvain(anurosnet)
modularity1$modularity #observed value
obs <- modularity1$modularity
obs
real<-data.frame(obs)
real
Nperm = 100 #I am starting with a low n, but intend to use at least 1000 permutations
#randomized.modularity <- matrix(nrow=length(obs),ncol=Nperm+1)
#row.names(randomized.modularity) <- names(obs)
randomized.modularity <- list()
randomized.modularity[1] <- obs
randomized.modularity
for(i in 1:Nperm){
randomnet <- rewire(anurosnet, with=each_edge(0.5)) #rewire vertices with constant probability
E(randomnet)$weight <- sample(E(anurosnet)$weight) #shuffle initial weights and assign them randomly to edges
mod <- (cluster_louvain(randomnet))
mod$modularity
linha = mod$modularity
randomized.modularity <- c(randomized.modularity, list(linha))
}
randomized.modularity
randomized.modularity <- lapply(seq_len(Nperm), function(x){
randomnet <- rewire(anurosnet, with=each_edge(0.5)) #rewire vertices with constant probability
E(randomnet)$weight <- sample(E(anurosnet)$weight) #shuffle initial weights and assign them randomly to edges
return(cluster_louvain(randomnet)$modularity)
})