I have been reading a lot about ROC curves, but honestly I still don't have it cleared in my mind. So I request anyone here who could explain me about it with respect to my dummy example below.
So lets say I have a microarray data as follows:
Sites Samp1 Samp2 Samp3 Samp4 Samp5 ... SampN siteA 0.675 0.344 0.543 0.564 0.098 0.433 siteB 0.345 0.432 0.454 0.122 0.789 0.332 siteC 0.322 0.234 0.987 0.455 0.433 0.765 siteD 0.876 0.455 0.654 0.987 0.332 0.093 siteE 0.543 0.345 0.234 0.123 0.127 0.654
and lets say I have a phenotype for each of the above samples.
Samples Pheno Samp1 43 Samp2 45 Samp3 56 Samp4 50 Samp5 41 .. SampN 59
And then i run a continuous linear regression for my above data to see which of the sites (may be genes or methylation sites) are highly significant with respect to my phenotype.
So I have my dummy results as follows.
Sites t-statistics p-value Bonferoni Holm FDR significant siteD 222.255.348.790.264 6,94E-70 TRUE TRUE 3,22E-64 siteA 160.991.598.630.311 4,30E-36 TRUE TRUE 9,96E-31 siteE 154.406.449.263.392 1,01E-32 TRUE TRUE 1,57E-27 siteB 153.199.072.926.937 4,13E-32 TRUE TRUE 4,79E-27 siteC 148.394.475.170.859 1,05E-29 TRUE TRUE 9,77E-25
So till now, I have this. Now I wish to draw an ROC curve for this. I do have an idea that ROC shows the amount of false positives and false negatives. So my questions are :
1) How do I get an ROC for the above ? What kind of input am i supposed to use to get an ROC curve for the above data. How to i get the FP and FN for my "Sites".
2) It would be really helpful if somebody can help me do a small ROC for the above. I am planning to use the package pROC. But I am confused with my inputs there.