Establish whether each of the corresponding edges are significantly different in two groups, with the de-sparsified estimator of (Jankova and Van De Geer 2015) .
compare_edges(object_1, object_2, method = "fdr", alpha = 0.05, ...)
object_1 | object of class |
---|---|
object_2 | An object of class |
method | Character string. A correction method for
multiple comparisons (defaults to |
alpha | Numeric. Significance level (defaults to |
... | Currently ignored. |
P_diff
De-sparsified partial correlation differences
adj
Adjacency matrix based on the p-values.
pval_uncorrected
Uncorrected p-values
pval_corrected
Corrected p-values
method
The approach used for multiple comparisons
alpha
Significance level
For low-dimensional settings, i.e., when the number of observations far exceeds the number of nodes, this function likely has limited utility and a non regularized approach should be used for comparing edges (see for example GGMnonreg).
Further, whether the de-sparsified estimator provides nominal error rates remains to be seen, at least across a range of conditions. For example, the simulation results in Williams (2021) demonstrated that the confidence intervals can have (severely) compromised coverage properties (whereas non-regularized methods had coverage at the nominal level).
Jankova J, Van De Geer S (2015).
“Confidence intervals for high-dimensional inverse covariance estimation.”
Electronic Journal of Statistics, 9(1), 1205--1229.
Williams DR (2021).
“The Confidence Interval that Wasn't: Bootstrapped "Confidence Intervals" in L1-Regularized Partial Correlation Networks.”
PsyArXiv.
doi: 10.31234/osf.io/kjh2f
.
# data # note: all edges equal Y1 <- MASS::mvrnorm(250, rep(0, 10), Sigma = diag(10)) Y2 <- MASS::mvrnorm(250, rep(0, 10), Sigma = diag(10)) # fit models # note: atan penalty by default # group 1 fit1 <- ggmncv(cor(Y1), n = nrow(Y1), progress = FALSE) # group 2 fit2 <- ggmncv(cor(Y2), n = nrow(Y2), progress = FALSE) # compare compare_ggms <- compare_edges(fit1, fit2) compare_ggms #> Compare Edges #> fdr: 0.05 #> --- #> #> 1 2 3 4 5 6 7 8 9 10 #> 1 0 0 0 0 0 0 0 0 0 0 #> 2 0 0 0 0 0 0 0 0 0 0 #> 3 0 0 0 0 0 0 0 0 0 0 #> 4 0 0 0 0 0 0 0 0 0 0 #> 5 0 0 0 0 0 0 0 0 0 0 #> 6 0 0 0 0 0 0 0 0 0 0 #> 7 0 0 0 0 0 0 0 0 0 0 #> 8 0 0 0 0 0 0 0 0 0 0 #> 9 0 0 0 0 0 0 0 0 0 0 #> 10 0 0 0 0 0 0 0 0 0 0