-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path6.Community_simulation.R
165 lines (137 loc) · 6.3 KB
/
6.Community_simulation.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
# Community simulation
# setwd('E:/Shangri-La_2024/network')
# 加载包
library(readxl)
library(tidyverse)
library(bipartite)
library(psych)
rm(list=ls())
design <- read_excel("data/design.xlsx", sheet="Sheet1")
head(design)
# design <- subset(design,Plant_Species!="Euphorbia_jolkinii")
design <- design[,1:8]
network <- read_excel("data/network_matrix.xlsx", sheet="Sheet1")
head(network)
# network <- subset(network,Plant_Species!="Euphorbia_jolkinii")
# network$lower <- network$AMFungi_OTUID
# network$higher <- network$Plant_Species
# network$webID_o <- paste0(network$Site,network$Plot)
# network$freq <- network$Frequency
network_matrix <- network
network_matrix <- as.data.frame(network_matrix)
rm(list=ls()[!ls() %in% c("design","network","network_matrix")])
########
set.seed(123)
# 定义函数进行随机化抽样
simulation_sampling <- function(design, network_matrix, site, num_samples = 1000) {
design_Site <- subset(design, Site == site)
network_Site <- subset(network_matrix, Site == site)
sample_results <- vector("list", num_samples)
for (i in 1:num_samples) {
sample_data <- design_Site
link_data <- network_Site
# 随机抽样一半的sample
sample_indices <- sample(1:nrow(sample_data), size=nrow(sample_data)/2, replace=FALSE)
S1.sample <- sample_data[sample_indices, ]$Sample
S1 <- link_data[link_data$Sample %in% S1.sample, ]
S1$webID <- paste0(site, "_S1")
# 将剩余的sample作为 S2
S2.sample <- sample_data[-sample_indices, ]$Sample
S2 <- link_data[link_data$Sample %in% S2.sample, ]
S2$webID <- paste0(site, "_S2")
simu_new <- rbind(S1, S2)
sample_results[[i]] <- simu_new
}
# 返回抽样结果
return(sample_results)
}
# 对每个样地进行随机化抽样
simulation_all <- list()
for (site in unique(design$Site)) {
simulation_all[[site]] <- simulation_sampling(design, network_matrix, site)
}
########
# 将网络数据，转化为网络矩阵
simulation_network_all <- list()
for (site in unique(design$Site)) {
simulation_network_all[[unique(simulation_all[[site]][[1]]$webID)[1]]] <- list()
simulation_network_all[[unique(simulation_all[[site]][[1]]$webID)[2]]] <- list()
for (i in 1:1000){
simulation_network_all[[unique(simulation_all[[site]][[i]]$webID)[1]]][[i]] <- frame2webs(subset(simulation_all[[site]][[i]],webID==unique(simulation_all[[site]][[i]]$webID)[1]),type.out= "list")[[1]]
simulation_network_all[[unique(simulation_all[[site]][[i]]$webID)[2]]][[i]] <- frame2webs(subset(simulation_all[[site]][[i]],webID==unique(simulation_all[[site]][[i]]$webID)[2]),type.out= "list")[[1]]
}
}
########
# 计算每个网络的1000个simulation的mean、sd
simulations_result <- function(simulation_network, index){
simul.value <- unlist(sapply(simulation_network, networklevel, index=index))
mean.simul <- mean(simul.value)
sd.simul <- sd(simul.value)
return(cbind(mean.simul, sd.simul))
}
# weighted connectance
Wcon_results <- list()
for (i in names(simulation_network_all)) {
Wcon_results[[i]] <- simulations_result(simulation_network_all[[i]], index="weighted connectance")
}
Wcon_results
Wcon_results.all <- do.call(rbind,Wcon_results)
Wcon_results.all <- as.data.frame(Wcon_results.all)
Wcon_results.all <- cbind(webID=names(simulation_network_all), Wcon_results.all)
Wcon_results.all$simulation_index <- "weighted connectance"
Wcon_results.all
# interaction evenness (intereven="prod")
evenness_results <- list()
for (i in names(simulation_network_all)) {
evenness_results[[i]] <- simulations_result(simulation_network_all[[i]], index="interaction evenness")
}
evenness_results
evenness_results.all <- do.call(rbind,evenness_results)
evenness_results.all <- as.data.frame(evenness_results.all)
evenness_results.all <- cbind(webID=names(simulation_network_all), evenness_results.all)
evenness_results.all$simulation_index <- "interaction evenness"
evenness_results.all
# weighted NODF
Wnodf_results <- list()
for (i in names(simulation_network_all)) {
Wnodf_results[[i]] <- simulations_result(simulation_network_all[[i]], index="weighted NODF")
}
Wnodf_results
Wnodf_results.all <- do.call(rbind,Wnodf_results)
Wnodf_results.all <- as.data.frame(Wnodf_results.all)
Wnodf_results.all <- cbind(webID=names(simulation_network_all), Wnodf_results.all)
Wnodf_results.all$simulation_index <- "weighted NODF"
Wnodf_results.all
simulation_data <- bind_rows(evenness_results.all,Wnodf_results.all,Wcon_results.all)
simulation_data
write.csv(simulation_data,"results/network_analyses_simulation.csv")
# write.csv(simulation_data,"results/network_analyses_simulation_Eup.jo_excluded.csv")
save.image("results/network_analyses_simulation.RData")
# save.image("results/network_analyses_simulation_Eup.jo_excluded.RData")
#################################################################################
# weighted modularity，计算每个网络的1000个simulation的mean、sd
simulations_mod_result <- function(simulation_network){
simul.value <- unlist(sapply(simulation_network,function(x)computeModules(x, method="Beckett")@likelihood))
mean.simul <- mean(simul.value)
sd.simul <- sd(simul.value)
return(cbind(mean.simul, sd.simul))
}
Wmod_results <- list()
for (i in names(simulation_network_all)) {
Wmod_results[[i]] <- simulations_mod_result(simulation_network_all[[i]])
}
Wmod_results
Wmod_results.all <- do.call(rbind,Wmod_results)
Wmod_results.all <- as.data.frame(Wmod_results.all)
Wmod_results.all <- cbind(webID=names(simulation_network_all), Wmod_results.all)
Wmod_results.all$simulation_index <- "weighted modularity"
Wmod_results.all
simulation_data2 <- bind_rows(evenness_results.all,Wnodf_results.all,Wcon_results.all,Wmod_results.all)
simulation_data2
write.csv(simulation_data2,"results/network_analyses_simulation2.csv")
# write.csv(simulation_data,"results/network_analyses_simulation_Eup.jo_excluded2.csv")
save.image("results/network_analyses_simulation2.RData")
# save.image("results/network_analyses_simulation_Eup.jo_excluded2.RData")
#################################################################################
#################################################################################
#################################################################################