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I'm having trouble making a simulation that re-creates this issue, but in multiple different data sources, I often see the Z-score going negative.
In a simulation, I can kinda recreate it via fastRG by making the non-zero values of A all 1's. but it is not as severe as I see here.
set.seed(1)
library(fastRG)
mod = fastRG::dcsbm(n = 10000, k = 10,expected_degree = 50)
diag(mod$S) = diag(mod$S) + mean(mod$S)
A = sample_sparse(mod)+0
mean(rowSums(A))
out_A = gdim::eigcv(A, k_max = 50, num_bootstraps = 1, laplacian = FALSE)
# notice how the first 5 p-values are small,
# then they become uniform.
# This is what we expect.
out_A$summary$pvals %>% plot
# if you threshold non-zero entries of A to be one...
A1 = A
A1@x[] = 1
out_A1 = gdim::eigcv(A1, k_max = 50, num_bootstraps = 1, laplacian = FALSE)
# then you get 4 small p-values... then they jump to 1!
# they should be uniform, but the thresholding
# makes the test biased.
out_A1$summary$pvals %>% plot
The text was updated successfully, but these errors were encountered:
This is not a request for any code fix... just documenting a strange behavior that I think we understand from a different perspective... we will want to give clear guidance to users on how to interpret this.
I'm having trouble making a simulation that re-creates this issue, but in multiple different data sources, I often see the Z-score going negative.
In a simulation, I can kinda recreate it via fastRG by making the non-zero values of A all 1's. but it is not as severe as I see here.
The text was updated successfully, but these errors were encountered: