Ana Marija
2020-Sep-15 15:57 UTC
[R] How to represent the effect of one covariate on regression results?
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? 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.
David Winsemius
2020-Sep-16 00:59 UTC
[R] 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.
Ana Marija
2020-Sep-16 01:11 UTC
[R] How to represent the effect of one covariate on regression results?
Hi David, thanks for the useful insight I did of course wrote to plink user group but no answer there. I guess they are more concerned about how to run commands with plink as oppose to interpret results. What I can tell about my cohort is that about 80% of cases had Type 2 diabetes while about 8% had Type 1. (my TD covariate is reference for the type of diabetes) In the attach is the description of the data. Cheers, Ana On Tue, Sep 15, 2020 at 7:59 PM David Winsemius <dwinsemius at comcast.net> wrote:> > > 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.-------------- next part -------------- A non-text attachment was scrubbed... Name: data.png Type: image/png Size: 57291 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20200915/2045e2f5/attachment.png>