I'm wondering if you want one of these: (1) Plots of "Main Effects". (2) "Partial Residual Plots".
Search for them, and you should be able to tell if they're what you want. But a word of warning: Many people (including many senior statisticians) misinterpret this kind of information. Because, it's always the effect of xj on Y, while holding the other variables *constant*. That's not as simple as it sounds, and people have a tendency of disregarding the importance of the second half of that sentence, in their final interpretations. P.S. John Fox, announced a package with support for Regression Diagnostics, about 11 days ago: https://stat.ethz.ch/pipermail/r-help/2020-September/468609.html I'm not sure how relevant it is to your question, but I just glanced at the vignette, and it's pretty slick... On Tue, Sep 15, 2020 at 1:30 AM Ana Marija <sokovic.anamar...@gmail.com> wrote: > > Hello, > > I was running association analysis using --glm genotypic from: > https://www.cog-genomics.org/plink/2.0/assoc with these covariates: > sex,age,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,TD,array,HBA1C. The > result looks like this: > > #CHROM POS ID REF ALT A1 TEST OBS_CT BETA > SE Z_OR_F_STAT P ERRCODE > 10 135434303 rs11101905 G A A ADD 11863 > -0.110733 0.0986981 -1.12193 0.261891 . > 10 135434303 rs11101905 G A A DOMDEV 11863 > 0.079797 0.111004 0.718868 0.472222 . > 10 135434303 rs11101905 G A A sex=Female > 11863 -0.120404 0.0536069 -2.24605 0.0247006 . > 10 135434303 rs11101905 G A A age 11863 > 0.00524501 0.00391528 1.33963 0.180367 . > 10 135434303 rs11101905 G A A PC1 11863 > -0.0191779 0.0166868 -1.14928 0.25044 . > 10 135434303 rs11101905 G A A PC2 11863 > -0.0269939 0.0173086 -1.55957 0.118863 . > 10 135434303 rs11101905 G A A PC3 11863 > 0.0115207 0.0168076 0.685448 0.493061 . > 10 135434303 rs11101905 G A A PC4 11863 > 9.57832e-05 0.0124607 0.0076868 0.993867 . > 10 135434303 rs11101905 G A A PC5 11863 > -0.00191047 0.00543937 -0.35123 0.725416 . > 10 135434303 rs11101905 G A A PC6 11863 > -0.0103309 0.0159879 -0.646172 0.518168 . > 10 135434303 rs11101905 G A A PC7 11863 > 0.00790997 0.0144025 0.549207 0.582863 . > 10 135434303 rs11101905 G A A PC8 11863 > -0.00205639 0.0142709 -0.144096 0.885424 . > 10 135434303 rs11101905 G A A PC9 11863 > -0.00873771 0.0057239 -1.52653 0.126878 . > 10 135434303 rs11101905 G A A PC10 11863 > 0.0116197 0.0123826 0.938388 0.348045 . > 10 135434303 rs11101905 G A A TD 11863 > -0.670026 0.0962216 -6.96337 3.32228e-12 . > 10 135434303 rs11101905 G A A array=Biobank > 11863 0.160666 0.073631 2.18205 0.0291062 . > 10 135434303 rs11101905 G A A HBA1C 11863 > 0.0265933 0.00168758 15.7583 6.0236e-56 . > 10 135434303 rs11101905 G A A GENO_2DF 11863 > NA NA 0.726514 0.483613 . > > This results is shown just for one ID (rs11101905) there is about 2 > million of those in the resulting file. > > My question is how do I present/plot the effect of covariate "TD" in > the example it has "P" equal to 3.32228e-12 for all IDs in the > resulting file so that I show how much effect covariate "TD" has on > the analysis. Should I run another regression without covariate "TD" > and than do scatter plot of P values with and without "TD" covariate > or there is a better way to do this from the data I already have? > > Thanks > Ana > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
