Last updated: 2021-04-14
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Knit directory: 2020_ODI_network/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ee49c01 | Liew | 2021-01-08 | redo analysis post update |
| Rmd | b243fbf | Liew | 2021-01-07 | redo analysis after updating bootneet |
| Rmd | 661e55c | Liew | 2020-12-18 | augmented mgm code to furrr and removed missing first |
| html | 8bd4b9a | bernard-liew | 2020-11-04 | Build site. |
| Rmd | 502ad53 | bernard-liew | 2020-11-04 | build contents for web |
| html | 502ad53 | bernard-liew | 2020-11-04 | build contents for web |
| Rmd | 5f1623a | bernard-liew | 2020-10-13 | initial network analysis |
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::huge.npn (df[, grepl ("Q", names(df))] )
return (df)
}#### Analyse data with missing
dat <- df$odi_mgm
set.seed(1)
res_mgm_raw <- dat %>%
select (time, data) %>%
# remove cases with missing columns
mutate (data = map (data, na.omit)) %>%
# 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))
# Bootstrap by case to get stability of centrality
B <- 2000
plan (multisession)
res_mgm_raw <- res_mgm_raw %>%
mutate (centr_stb = future_map (nw,
bootnet,
nBoots = B,
type = "case",
statistics = stats_type,
.options = furrr_options(seed = TRUE)))
# Stability measure
res_mgm_raw <- res_mgm_raw %>%
mutate (cor_stb = map (centr_stb,
corStability))
# Bootstrap by case to get stability of centrality
plan (multisession)
res_mgm_raw <- res_mgm_raw %>%
mutate (edgewts = future_map (nw,
bootnet,
nBoots = B,
.options = furrr_options(seed = TRUE)))
saveRDS(res_mgm_raw, "output/mgm_raw.RDS")
sessionInfo()