使用 100x 进行自动分析

Question

我有 100 个矩阵，我需要对所有矩阵进行相同的分析。

###DADOSEXEMPLO##
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)
matrix
###BOOT
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)))  
##ANALYSE
degree(BIPERMUT[[1]], normalized = FALSE, loops = FALSE)

我尝试了，但这并没有运行分析 100 倍，而是运行 100 倍重新采样：

for(i in seq.int(R)) {
indices <- sample(N, replace = TRUE)
 boot_degree= degree(g_boot_list[[i]], normalized = FALSE, loops = FALSE)

Answer 1

在

boot_degree

循环之前创建结果向量

for

。

boot_degree <- vector("list", R)
for(i in seq.int(R)) {
  boot_degree[[i]] <- degree(g_boot_list[[i]], normalized = FALSE, loops = FALSE)
}

另一种方法是

lapply

为

degree

的每个成员发挥

g_boot_list

的作用。

boot_degree2 <- lapply(g_boot_list, degree, normalized = FALSE, loops = FALSE)

identical(boot_degree, boot_degree2)  # TRUE