在这里，我展示了数据集。

library(igraph)
vetor <- c(1, 5, 3, 8, 2, 9, 3, 2:15, 1, 5, 3, 6, 5, 9, 3 )
matrix(vetor, 7, 7)
matrix<-matrix(vetor, 7, 7)
matcor <- cor(matrix, method = "spearman")

matrixnetwork = graph.adjacency(matcor, mode="undirected", weighted = 
TRUE, add.colnames=NULL, diag=FALSE)
plot(matrixnetwork)

我需要通过随机排列每个感兴趣区域内的值的顺序来计算 Bootstrap 相关矩阵，然后计算每个 Bootstrap 矩阵的图形值。我将运行随后的比较分析（t检验）

Corr_fun = function(matrix, indices) {
return(cor(matrix, method= "spearman")) }
BIboot= boot(matrix, statistic= Corr_fun, R = 100)

AIBOOT<-BIboot[["t"]]

这个形式返回一个49 x100的矩阵，当我运行下面的命令时，说：图形中的错误。邻接。密集（adjmatrix，mode = mode，weighted = weighted，：而不是一个方阵。

g_boot <- graph_from_adjacency_matrix(AIBOOT, mode = "undirected", weighted = TRUE)

此外，它返回重复的值

我也试着：

library(bootstrap)
theta = function(matrix, indices) {
return(as.matrix(matrix)) }  #funcao para centralidade
BOOTSAVE<-bootstrap(matrix, nboot = 100, theta)

结果如下：

我也尝试置换图，这里的“matrixnetwork”是来自comand的adjacency_matrix，但这种形式我需要做1乘1，我需要它100倍的方式，使它很容易测量图，并进行后续比较：

matrixnetwork2<-permute(matrixnetwork, sample(vcount(matrixnetwork)))

我尝试这个形式，似乎工作，这里“aibi_imuno_BI_1”是我的数据。我不把“cm <- cormat[指数，指数]"，因为这是重复相同的面积2x

N <- 7L set.seed(2023) 
R <- 100L 
BIboot <- vector("list", length = R) 
for(i in seq.int(R)) { indices <- sample(N, replace = TRUE) 
BIboot[[i]] <- cor(aibi_imuno_BI_1, method = "spearman") } 
 g_boot_list <- lapply(BIboot, graph_from_adjacency_matrix, mode = 
"undirected", weighted = TRUE, add.colnames=NULL, diag=FALSE) plot 
 (g_boot_list[[1]])

更正后的格式如下：

# BOOT 1
N <- 7L
set.seed(2023)
R <- 100L
BIboot <- vector("list", length = R)
for(i in seq.int(R)) {
indices <- sample(N, replace = TRUE)
BIboot[[i]] <- cor(aibi_imuno_BI_1[indices, ], method = "spearman")
}
g_boot_list <- lapply(BIboot, graph_from_adjacency_matrix, mode = 
"undirected", weighted = TRUE, add.colnames=NULL, diag=FALSE)
plot (g_boot_list[[1]])

这里我有100个 Bootstrap 相关矩阵，
最后我试着：

#############BOTPERMUTATION FORMA 2 ####################
N <- 7L
set.seed(2023)
R <- 100L
BIPERMUT<- vector("list", length = R)
for(i in seq.int(R)) {
  indices <- sample(N, replace = TRUE)
  BIPERMUT[[i]] <- permute(matrixnetwork, 
 sample(vcount(matrixnetwork)))  
}

plot (BIPERMUT[[1]])
degree(BIPERMUT[[1]], normalized = FALSE, loops = FALSE)

 #################FORMA 3 #######################
 N <- 7L
set.seed(2023)
R <- 100L
BIboot2 <- vector("list", length = R)
for(i in seq.int(R)) {
  indices <- sample(N, replace = TRUE)
  BIboot2[[i]] <- matrixnetwork
}

plot (BIboot2[[1]])
degree(BIboot2[[1]], normalized = FALSE, loops = FALSE)