# An Intuitive Approach to ROC Curves (with SAS & R)

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I developed the following schematic (with annotations) based on supporting documents (link) from the article cited below. The authors used R for their work. The ROC curve in my schematic was output from PROC LOGISTIC in SAS, the scatterplot with marginal histograms was created in R (code below) using the scored data from PROC LOGISTIC exported using my SAS MACRO **Econometric Sense**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

*(link to SAS macro code)*

**%EXPORT_TO_R**(click to enlarge)

**Reference:**

Selection of Target Sites for Mobile DNA Integration in the Human Genome

Berry C, Hannenhalli S, Leipzig J, Bushman FD, 2006 Selection of Target Sites for Mobile DNA Integration in the Human Genome. PLoS Comput Biol 2(11): e157. doi:10.1371/journal.pcbi.0020157

quote

*“The data were analyzed using the R language and environment for statistical computing and graphics “*

R code for plot was adapted from code provided via the addicted to R graph gallery : http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=78

# *------------------------------------------------------------------ # | # | import scored logit data from SAS - code generated by SAS MACRO %EXPORT_TO_R # | # | # *----------------------------------------------------------------- # set R working directory setwd("C:\\Documents and Settings\\wkuuser\\Desktop\\PROJECTS\\Stats Training") # get data dat.from.SAS <- read.csv("fromSAS_delete.CSV", header=T) # check data dimensions dim(dat.from.SAS) names(dat.from.SAS) # *------------------------------------------------------------------ # | # | scatter plot with marginal histograms # | # | # *----------------------------------------------------------------- # # model predicts P(G) so we want these probabilities for each group # # get p(G) data set for the group that is actually green green <- dat.from.SAS[ dat.from.SAS$class=="G",] dim(green) # get p(G) data set for group that is actually red red <- dat.from.SAS[ dat.from.SAS$class=="R",] dim(red) # just look at regular histograms for each group hist(green$P_G, main = 'histogram for green') hist(red$P_G, main = 'histogram for red') # in order to do scatter plots n must be the same for each # group, randomly sample n = n(green) from red # Total number of red observations to match green N <- 24 print(N) # Randomly arrange the data and select out N size sample for red # and test set. dat <- red[sample(1:N),] red.rs <- dat[1:N,] dim(red.rs) # does the distribution retain original properties? Yes hist(red.rs$P_G, main = 'histogram for red sample') plot(green$P_G, red.rs$P_G) # *------------------------------------------------------------------ # | # | create the marginal plots # | # | # *----------------------------------------------------------------- def.par <- par(no.readonly = TRUE) # save default, for resetting... # define histograms Ghist <- hist(green$P_G,plot=FALSE) Rhist <- hist(red.rs$P_G, plot=FALSE) top <- max(c(Ghist$counts, Rhist$counts)) Grange <- c(0,1) Rrange <- c(0,1) nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE) #layout.show(nf) par(mar=c(3,3,1,1)) plot(green$P_G, red.rs$P_G, xlim=Grange, ylim=Rrange, xlab="green", ylab="red") par(mar=c(0,3,1,1)) barplot(Ghist$counts, axes=FALSE, ylim=c(0, top), space=0, main = 'green') par(mar=c(3,0,1,1)) barplot(Rhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE, main = 'red') par(def.par)

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