ROC Curve Plot using R (Error code: Predictor must be numeric or ordered)

Question

I am trying to make a ROC Curve using pROC with the 2 columns as below: (the list goes on to over >300 entries)

Actual_Findings_%	Predicted_Finding_Prob
0.23	0.6
0.48	0.3
0.26	0.62
0.23	0.6
0.48	0.3
0.47	0.3
0.23	0.6
0.6868	0.25
0.77	0.15
0.31	0.55

The code I tried to use is:

roccurve<- plot(roc(response = data$Actual_Findings_% <0.4, predictor = data$Predicted_Finding_Prob >0.5),
                legacy.axes = TRUE, print.auc=TRUE, main = "ROC Curve", col = colors)

Where the threshold for positive findings is Actual_Findings_% <0.4 AND Predicted_Finding_Prob >0.5 (i.e to be TRUE POSITIVE, actual_finding_% would be LESS than 0.4, AND predicted_finding_prob would be GREATER than 0.5)

but when I try to plot this roc curve, I get the error:

"Setting levels: control = FALSE, case = TRUE
Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'x' in selecting a method for function 'plot': Predictor must be numeric or ordered."

Any help would be much appreciated!

Sirius · Accepted Answer · 2021-04-11 11:38:48Z

1

This should work:


data <- read.table( text=
"Actual_Findings_%  Predicted_Finding_Prob
0.23    0.6
0.48    0.3
0.26    0.62
0.23    0.6
0.48    0.3
0.47    0.3
0.23    0.6
0.6868  0.25
0.77    0.15
0.31    0.55
", header=TRUE, check.names=FALSE )

library(pROC)

roccurve <- plot(
    roc(
        response = data$"Actual_Findings_%" <0.4,
        predictor = data$"Predicted_Finding_Prob"
    ),
    legacy.axes = TRUE, print.auc=TRUE, main = "ROC Curve"
)

Now importantly - the roc curve is there to show you what happens when you varry your classification threshold. So one thing you do do wrong is to go and enforce one, by setting predictions < 0.5

This does however give a perfect separation, which is nice I guess. (Though bad for educational purposes.)

edited Apr 11, 2021 at 11:38

answered Apr 11, 2021 at 10:28

Sirius

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2 Comments

fireplush Over a year ago

converted the input data (as editted in the dataframes above) but they still won't do the trick unfortunately! Do you have any other suggestions?

Sirius Over a year ago

I was completely off with my first answer. Notice how you shouldn't threshold the predictor. The whold idea behind with the ROC is to see what happens when this varries.

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