[R] Jagged ROC curves?
Brian Smith
bsmith030465 at gmail.com
Mon Jun 26 20:10:01 CEST 2017
Hi Marc,
I tried to attache the png file of the plot, but the mailing list blocked
it!
"For the attached two png files (test_roc.png & test_roc_smooth.png)
1. Using 'plot' function:
plot(c(1,0),c(0,1), type='l', lty=3, xlim=c(1.01,-0.01),
ylim=c(-0.01,1.01), xaxs='i', yaxs='i', ylab='', xlab='')
plot(roc_1,col="brown3", lwd=2, add=T, lty=1)
2. Using the 'smooth' function:
plot(c(1,0),c(0,1), type='l', lty=3, xlim=c(1.01,-0.01),
ylim=c(-0.01,1.01), xaxs='i', yaxs='i', ylab='', xlab='')
plot(smooth(roc_1),col="brown3", lwd=2, add=T, lty=1)
I guess most ROCs that I've seen are somewhere in between, i.e. they have a
little jaggedness, but not as much as in plot #1 above"
thanks!
On Mon, Jun 26, 2017 at 12:59 PM, Marc Schwartz <marc_schwartz at me.com>
wrote:
>
> > On Jun 26, 2017, at 11:40 AM, Brian Smith <bsmith030465 at gmail.com>
> wrote:
> >
> > Hi,
> >
> > I was trying to draw some ROC curves (prediction of case/control status),
> > but seem to be getting a somewhat jagged plot. Can I do something that
> > would 'smooth' it somewhat? Most roc curves seem to have many incremental
> > changes (in x and y directions), but my plot only has 4 or 5 steps even
> > though there are 22 data points. Should I be doing something differently?
> >
> > How can I provide a URL/attachment for my plot? Not sure if I can provide
> > reproducible code, but here is some pseudocode, let me know if you'd like
> > more details:
> >
> > #####
> > ## generate roc and auc values
> > #####
> > library(pROC)
> > library(AUCRF)
> >
> > getROC <- function(d1train,d1test){
> > my_model <- AUCRF(formula= status ~ ., data=d1train,
> > ranking='MDA',ntree=1000,pdel=0.05)
> > my_opt_model <- my_model$RFopt
> >
> > my_probs <- predict(my_opt_model, d1test, type = 'prob')
> > my_roc <- roc(d1test[,resp_col] ~ my_probs[,2])
> > aucval <- round(as.numeric(my_roc$auc),4)
> > return(my_roc)
> > }
> >
> >
> > roc_1 <- getROC(dat1,dat1test)
> > plot.roc(roc_1,col="brown3")
> >
> >
> >> roc_1
> >
> > Call:
> > roc.formula(formula = d1test[, resp_col] ~ ibd_probs[, 2])
> >
> > Data: ibd_probs[, 2] in 3 controls (d1test[, resp_col] 0) < 19 cases
> > (d1test[, resp_col] 1).
> > Area under the curve: 0.8596
> >
> >
> >> roc_1$sensitivities
> > [1] 1.00000000 0.94736842 0.94736842 0.94736842 0.89473684 0.84210526
> > 0.78947368 0.73684211 0.68421053 0.68421053
> > [11] 0.63157895 0.57894737 0.52631579 0.47368421 0.42105263 0.36842105
> > 0.31578947 0.26315789 0.21052632 0.15789474
> > [21] 0.10526316 0.05263158 0.00000000
> >
> >
> >> roc_1$specificities
> > [1] 0.0000000 0.0000000 0.3333333 0.6666667 0.6666667 0.6666667 0.6666667
> > 0.6666667 0.6666667 1.0000000 1.0000000
> > [12] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
> 1.0000000
> > 1.0000000 1.0000000 1.0000000 1.0000000
> > [23] 1.0000000
> >
> >
> > many thanks!
>
>
> ROC curves are typically step functions of some nature, depending upon
> your thresholds, so the default behavior is not going to be smoothed.
>
> I am not sure how they (AUCRF and pROC) may interact, but look at the
> ?smooth function in the latter package to see if it might help.
>
> To your second point, if your plot is a png/jpg file, you could attach it
> to your post here, if that was your desire. Otherwise, you could post it to
> a cloud based repository, like Dropbox, and provide the URL for public
> sharing here. The R lists support limited binary attachment types and
> png/jpg/pdf/ps are supported.
>
> Regards,
>
> Marc Schwartz
>
>
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