[R] Jagged ROC curves?

[Brian Smith]

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!

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