Logistic regression on entries

Hi Hail!

I have been happily running linear regressions on entry data as such:
mt = mt.annotate_rows(GWAS= hl.agg.linreg( mt.pheno, [1.0, mt.entry1, mt.entry2, covariates]))

I now have several binary traits and am wondering if there’s a comparable way to run logistic regression on entries rather than rows?

Thanks so much!
Elizabeth

Just checking in again - any advice for handling this? Thanks!

Ack, sorry, I thought I’d responded. Unfortunately logistic regression doesn’t have a one-pass algorithm, so it doesn’t fit as naturally into the aggregator system. We’ll have a way to do this in the next 6-9 months, I think, but before then the rather inflexible interface of logistic_regression_rows is the only option.

ok good to know. Thanks, and keep please me in the loop when this is added! Would be great to package this feature into the Tractor framework for case/control phenotypes.

will do!

Hey again! I am hoping to run this pipeline for a consortium working group, and we will have some binary phenotypes. Is there a fix for this yet, or do you have suggestions for workarounds?

Similar question to the one I just answered here: Hail implementation of RUTH

Unfortunately, it’s hard to build this infrastructure and it’s not done yet. Hopefully soon!

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Back again to check if this is yet a feature :slight_smile: otherwise, advice on transforming linear results for these purposes?