Last updated: 2020-11-04
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Knit directory: 2020_ODI_network/
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if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse,
qgraph,
stats,
bootnet,
igraph,
mgm,
NetworkComparisonTest,
rio,
furrr,
cowplot,
doParallel,
huge,
EGAnet)rm(list = ls())
df <- readRDS("output/dat_odi_nest.RDS")temp1 <- df$raw %>% select (-data_mgm)
temp2 <- df$com %>% select (-data_mgm) %>% rename (data_com = data)
temp3 <- temp1 %>%
inner_join(temp2, by = "time")
temp4 <- df$raw %>% select (-data) %>% rename (data = data_mgm)
temp5 <- df$com %>% select (-data) %>% rename (data_com = data_mgm)
temp6 <- temp4 %>%
inner_join(temp5, by = "time")
df <- list (odi = temp3,
odi_mgm = temp6)
rm (list = ls(pattern = "temp"))B <- 2000stats_type <- c("edge", "strength", "betweenness", "expectedInfluence", "closeness")
my_huge <- function (df) {
df[, grepl ("Q", names(df))] <- huge.npn (df[, grepl ("Q", names(df))] )
return (df)
}dat <- df$odi
plan (multisession)
set.seed(1)
res_raw <- dat %>%
select (time, data) %>%
# Transform data
mutate (data = map(data, huge.npn)) %>%
# Estimate network
mutate (nw = map (data,
estimateNetwork,
default="EBICglasso",
corMethod = "cor",
tuning = 0.5,
lambda.min.ratio = 0.001,
corArgs =
list(method = "pearson",
use = "pairwise.complete.obs")),
# Get centrality measures
centr = map (nw, centralityTable),
# Bootstrap by case to get stability of centrality
centr_stb = future_map (nw,
bootnet,
default="EBICglasso",
corMethod = "cor",
tuning = 0.5,
lambda.min.ratio = 0.001,
nBoots = B,
type = "case",
statistics = stats_type,
corArgs =
list(method = "pearson",
use = "pairwise.complete.obs")),
# Stability measure
cor_stb = map (centr_stb,
corStability),
# Bootstrap to get edgeweights stability
edgewts = future_map (nw,
bootnet,
default="EBICglasso",
corMethod = "cor",
tuning = 0.5,
lambda.min.ratio = 0.001,
nBoots = B,
corArgs =
list(method = "pearson",
use = "pairwise.complete.obs")))
saveRDS(res_raw, "output/raw.RDS")plan (multisession)
set.seed(1)
res_com <- dat %>%
select (time, data_com) %>%
rename (data = data_com) %>%
# Transform data
mutate (data = map(data, huge.npn)) %>%
# Estimate network
mutate (nw = map (data,
estimateNetwork,
default="EBICglasso",
corMethod = "cor",
tuning = 0.5,
lambda.min.ratio = 0.001,
corArgs =
list(method = "pearson",
use = "pairwise.complete.obs")),
# Get centrality measures
centr = map (nw, centralityTable),
# Bootstrap by case to get stability of centrality
centr_stb = future_map (nw,
bootnet,
default="EBICglasso",
corMethod = "cor",
tuning = 0.5,
lambda.min.ratio = 0.001,
nBoots = B,
type = "case",
statistics = stats_type,
corArgs =
list(method = "pearson",
use = "pairwise.complete.obs")),
# Stability measure
cor_stb = map (centr_stb,
corStability),
# Bootstrap to get edgeweights stability
edgewts = future_map (nw,
bootnet,
default="EBICglasso",
corMethod = "cor",
tuning = 0.5,
lambda.min.ratio = 0.001,
nBoots = B,
corArgs =
list(method = "pearson",
use = "pairwise.complete.obs")))
saveRDS(res_com, "output/com.RDS")dat <- df$odi_mgm
set.seed(1)
res_mgm_raw <- dat %>%
select (time, data) %>%
# Transform data
mutate (data = map(data, my_huge)) %>%
# Estimate network
mutate (nw = map (data,
estimateNetwork,
default = "mgm",
type= c("c", rep("g", 10)),
level= c(2, rep(1, 10)),
criterion = "CV", # we used cross validation to select optimal tuning parameter
nFolds = 10, # using 10 folds
order = 2, # we only include second order interactions
binarySign = TRUE,
scale = TRUE,
.pbar = FALSE,
.signInfo = FALSE)) %>%
# Get centrality measures
mutate (centr = map (nw, centralityTable))
# Furrr throws up error with mgm, switched to doParallel
# Bootstrap by case to get stability of centrality
doParallel::registerDoParallel(4)
B <- 2000
centr_stb_list <- list()
centr_stb_list <- foreach (n = 1: nrow (dat)) %dopar% {
bootnet::bootnet (res_mgm_raw$data[[n]],
default = "mgm",
.type= c("c", rep("g", 10)),
level= c(2, rep(1, 10)),
criterion = "CV",
nFolds = 10,
order = 2,
binarySign = TRUE,
scale = TRUE,
.pbar = FALSE,
.signInfo = FALSE,
nBoots = B,
type = "case",
statistics = stats_type)
}
res_mgm_raw$centr_stb <- centr_stb_list
# Stability measure
res_mgm_raw <- res_mgm_raw %>%
mutate (cor_stb = map (centr_stb,
corStability))
# Bootstrap by case to get stability of centrality
edgewts_list <- list()
edgewts_list <- foreach (n = 1: nrow (dat)) %dopar% {
bootnet::bootnet (res_mgm_raw$nw[[n]],
default = "mgm",
.type= c("c", rep("g", 10)),
level= c(2, rep(1, 10)),
criterion = "CV",
nFolds = 10,
order = 2,
binarySign = TRUE,
scale = TRUE,
.pbar = FALSE,
.signInfo = FALSE,
nBoots = B)
}
res_mgm_raw$edgewts <- edgewts_list
saveRDS(res_mgm_raw, "output/mgm_raw.RDS")set.seed(1)
res_mgm_com <- dat %>%
select (time, data_com) %>%
rename (data = data_com) %>%
# Transform data
mutate (data = map(data, my_huge)) %>%
# Estimate network
mutate (nw = map (data,
estimateNetwork,
default = "mgm",
type= c("c", rep("g", 10)),
level= c(2, rep(1, 10)),
criterion = "CV", # we used cross validation to select optimal tuning parameter
nFolds = 10, # using 10 folds
order = 2, # we only include second order interactions
binarySign = TRUE,
scale = TRUE,
.pbar = FALSE,
.signInfo = FALSE)) %>%
# Get centrality measures
mutate (centr = map (nw, centralityTable))
# Furrr throws up error with mgm, switched to doParallel
# Bootstrap by case to get stability of centrality
doParallel::registerDoParallel(4)
B <- 2000
centr_stb_list <- list()
centr_stb_list <- foreach (n = 1: nrow (dat)) %dopar% {
bootnet::bootnet (res_mgm_com$data[[n]],
default = "mgm",
.type= c("c", rep("g", 10)),
level= c(2, rep(1, 10)),
criterion = "CV",
nFolds = 10,
order = 2,
binarySign = TRUE,
scale = TRUE,
.pbar = FALSE,
.signInfo = FALSE,
nBoots = B,
type = "case",
statistics = stats_type)
}
res_mgm_com$centr_stb <- centr_stb_list
# Stability measure
res_mgm_com <- res_mgm_com %>%
mutate (cor_stb = map (centr_stb,
corStability))
# Bootstrap by case to get stability of centrality
doParallel::registerDoParallel(4)
edgewts_list <- list()
edgewts_list <- foreach (n = 1: nrow (dat)) %dopar% {
bootnet::bootnet (res_mgm_com$nw[[n]],
default = "mgm",
.type= c("c", rep("g", 10)),
level= c(2, rep(1, 10)),
criterion = "CV",
nFolds = 10,
order = 2,
binarySign = TRUE,
scale = TRUE,
.pbar = FALSE,
.signInfo = FALSE,
nBoots = B)
}
res_mgm_com$edgewts <- edgewts_list
saveRDS(res_mgm_com, "output/mgm_com.RDS")
sessionInfo()