How to represent the effect of one covariate on regression results?
On 9/15/20 8:57 AM, Ana Marija wrote:
Hi Abby and David, Thanks for the useful tips! I will check those. I completed the regression analysis in plink (as R would be very slow for my sample size) but as I mentioned I need to determine the influence of a specific covariate in my results and Plink is of no help there. I did Pearson correlation analysis for P values which I got in regression with and without my covariate of interest and I got this:
cor.test(tt$P_TD, tt$P_noTD, method = "pearson", conf.level = 0.95)
Pearson's product-moment correlation
data: tt$P_TD and tt$P_noTD
t = 20.17, df = 283, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.7156134 0.8117108
sample estimates:
cor
0.7679493
I can see the p values are very correlated in those two instances. Can
I conclude that my covariate then doesn't have a huge effect or what
kind of conclusion I can draw from that?
I do not think it follows from the correlation of p-values that your covariate "does not have a huge effect". P-values are not really data, although they are random values. A simulation study of this would require a much better description of the original dataset. Again, that is something that the users of Plink are more likely to be able to intuit than are we. I still do not see why this question is not being addressed to the users of the software from which you are deriving your "data".
David. > > Thanks for all your help > Ana > > > > On Tue, Sep 15, 2020 at 1:26 AM David Winsemius <dwinsemius at comcast.net> wrote: >> There is a user-group for PLINK, easily found by looking at the page you >> cited. This is not the correct place to submit such questions. >> >> >> https://groups.google.com/g/plink2-users?pli=1 >> >> >> -- >> >> David. >> >> On 9/14/20 6:29 AM, Ana Marija 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 at 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.