Robust Estimation?

[emstruong]

Hello,

I've been trying to run networktree on the attached data (which I understand to be a random sample of the publicly available BIG 5 data from IPIP), I'm guessing that the reason why networktree fails to run is because there are some sub-groups that have too small a sample relative to the number of nodes in the network.

Would there be a way to perhaps persist on computing the network on the sub-group either by using some variant of GLASSO or simply throwing up a warning message for the sub-groups on which a correlational network could not be estimated?

library(data.table)
library(networktree)
d_efa <- data.table::fread(input = here::here("data/EFA_BIG5.csv"))
col_idx <- colnames(d_efa)[-1:-6]
networktree::networktree(
  nodevars = d_efa[, col_idx, with = FALSE],
  splitvars = d_efa[, c("race", "age", "engnat", "gender", "hand", "country")],
  method = "mob",
  model = "correlation",
  transform = "cor"
)

#> Error in fit(y = y, x = x, start = start, weights = weights, offset = offset, : mvnfit: n < k*(k-1)/2, correlation matrix is not identified.

^{Created on 2024-04-29 with reprex v2.1.0}