Chapter 5 Fit the Single Trait Networks

Now, we will fit networks for each trait separately, which means they will not account for relationships between traits to which the indicators putatively belong.

ST_net_nested <- ipip20 %>% mutate(inventory = "IPIP20") %>%
  setNames(c("RID", "age", "gender", all_cols20, "age2", "inventory")) %>%
  gather(key = item, value = value, -RID, -age, -gender, -inventory, na.rm = T) %>%
full_join(
  ipip50 %>% mutate(inventory = "IPIP50") %>% 
  setNames(c("RID", "age", "gender", all_cols50, "age2", "inventory")) %>%
  gather(key = item, value = value, -RID, -age, -gender, -inventory, na.rm = T)
) %>%
full_join(
  ipip100 %>% mutate(inventory = "IPIP100") %>%
  setNames(c("RID", "age", "gender", all_cols100, "age2", "inventory")) %>%
  gather(key = item, value = value, -RID, -age, -gender, -inventory, na.rm = T)
) %>% tbl_df %>%
  separate(item, c("trait", "number"), 1, remove = F) %>%
  select(inventory, RID, age, trait, item, value) %>%
  group_by(inventory, age, trait) %>%
  nest() %>%
  mutate(data = map(data, function(x){x %>% spread(key = item, value = value)}),
         cols = map(data, colnames), 
         results  = map(data, EDBqgraph2),
         mat = map(results, ~.[[1]]),
         net = map(results, ~.[[2]]),
         centrality = map(net, centrality_auto)) 

# save(ST_net_nested, file = sprintf("%s/results/st_nested_nets.RData", data_path))

load(url(sprintf("%s/results/st_nested_nets.RData?raw=true", data_path)))
rm(list = ls()[(grepl("data", ls()) | grepl("ipip", tolower(ls()))) & ! grepl("colors", ls())])
ST_net_nested <- ST_net_nested %>%
  mutate(edges.df = map2(net, cols, getEdges.df),
         cors.df = map2(mat, cols, getEdges.df))