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May 19, 2021  04:05 AM | Diego Ramírez
Spearman Correlation
I want to study the association between FC and a behavioral measure with NBS. I am wondering if I can perform the univariate step of NBS as a spearman correlation by ranking the data (each FC edge and the behavioral measure) prior to entering it into the analysis, is that right? If that's the case, would it still make sense to add other covariates or should the design matrix be just the intercept and the behavioral measure of interest? Although NBS is nonparametric, the effect sizes could be different when working with ranked data and should be less susceptible to outliers, and I guess ranks make a bit more sense for some behavioral measures too.

edit: i also have two other off-topic questions:

1) Can we set a random seed inside an NBS script to make the results reproducible?
2) How does the one-sample test threshold works? My guess is that its a threshold for the raw FC values of the matrices rather than a t or f value?

Diego
May 20, 2021  07:05 AM | Andrew Zalesky
RE: Spearman Correlation
Hi Diego,

yes - if you normalize the ranks (subtract mean and divide by standard deviation), the beta coefficient from the NBS will approximate the Spearman rank correlation coefficient. I write approximation because Spearman may treat tied ranks differently. Including covariates is fine but this won't strictly be a rank correlation anymore.

1) Yes that's possible, but this would need careful inspection of the code to ensure there are not other re-seeding of the number generator.

2) I think that the threshold is still a t-statistic. Note that one-sample is rarely used.

Andrew

An alternative is to explicitly remove outliers.
Originally posted by Diego Ramírez:
I want to study the association between FC and a behavioral measure with NBS. I am wondering if I can perform the univariate step of NBS as a spearman correlation by ranking the data (each FC edge and the behavioral measure) prior to entering it into the analysis, is that right? If that's the case, would it still make sense to add other covariates or should the design matrix be just the intercept and the behavioral measure of interest? Although NBS is nonparametric, the effect sizes could be different when working with ranked data and should be less susceptible to outliers, and I guess ranks make a bit more sense for some behavioral measures too.