R - ROC Curves/AUC Specificity vs 1-Specificity











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enter image description hereI have created a few predictive models and I am in the process of evaluating them by looking at the ROC Curve and AUC.



Currently, I have Specificity on X axis, however, when I researched ROC Curves, I saw 1 - Specificity on the X axis.



What is the difference and which should I use to validate my predictive models?
If Specificity is on the X-Axis, do I still want to maximize the AUC (from experience the answer is yes but I want to confirm)?



Here is how I am plotting it:



> library(pROC)
> g <- roc(Setup ~ Probs, data = Data)
> plot(g)
> auc(g)
> ci.auc(g)









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  • Does your ROC curve move towards the top-left corner with better performance? That's the convention. If you have specificity on the x-axis I guess the top-right corner would show better performance, but the area under the curve should be equal either way.
    – Marius
    Nov 21 at 23:58










  • @Marius I added an image of the RUC Curve. The curve moves towards the top right corner but the "peaks" approaches the top left (if that makes any sense).
    – MhQ-6
    Nov 22 at 0:05










  • In other words, it looks like any other ROC graph that I found on google, where the curve starts at the bottom left and moves towards the top right and the peaks extends towards the top left. Literally like any ROC curve you will find using google, only difference is, I have specificity on the x axis. All the ones I found on google have 1-Specificity. Not sure what to make of it.
    – MhQ-6
    Nov 22 at 0:07















up vote
0
down vote

favorite












enter image description hereI have created a few predictive models and I am in the process of evaluating them by looking at the ROC Curve and AUC.



Currently, I have Specificity on X axis, however, when I researched ROC Curves, I saw 1 - Specificity on the X axis.



What is the difference and which should I use to validate my predictive models?
If Specificity is on the X-Axis, do I still want to maximize the AUC (from experience the answer is yes but I want to confirm)?



Here is how I am plotting it:



> library(pROC)
> g <- roc(Setup ~ Probs, data = Data)
> plot(g)
> auc(g)
> ci.auc(g)









share|improve this question
























  • Does your ROC curve move towards the top-left corner with better performance? That's the convention. If you have specificity on the x-axis I guess the top-right corner would show better performance, but the area under the curve should be equal either way.
    – Marius
    Nov 21 at 23:58










  • @Marius I added an image of the RUC Curve. The curve moves towards the top right corner but the "peaks" approaches the top left (if that makes any sense).
    – MhQ-6
    Nov 22 at 0:05










  • In other words, it looks like any other ROC graph that I found on google, where the curve starts at the bottom left and moves towards the top right and the peaks extends towards the top left. Literally like any ROC curve you will find using google, only difference is, I have specificity on the x axis. All the ones I found on google have 1-Specificity. Not sure what to make of it.
    – MhQ-6
    Nov 22 at 0:07













up vote
0
down vote

favorite









up vote
0
down vote

favorite











enter image description hereI have created a few predictive models and I am in the process of evaluating them by looking at the ROC Curve and AUC.



Currently, I have Specificity on X axis, however, when I researched ROC Curves, I saw 1 - Specificity on the X axis.



What is the difference and which should I use to validate my predictive models?
If Specificity is on the X-Axis, do I still want to maximize the AUC (from experience the answer is yes but I want to confirm)?



Here is how I am plotting it:



> library(pROC)
> g <- roc(Setup ~ Probs, data = Data)
> plot(g)
> auc(g)
> ci.auc(g)









share|improve this question















enter image description hereI have created a few predictive models and I am in the process of evaluating them by looking at the ROC Curve and AUC.



Currently, I have Specificity on X axis, however, when I researched ROC Curves, I saw 1 - Specificity on the X axis.



What is the difference and which should I use to validate my predictive models?
If Specificity is on the X-Axis, do I still want to maximize the AUC (from experience the answer is yes but I want to confirm)?



Here is how I am plotting it:



> library(pROC)
> g <- roc(Setup ~ Probs, data = Data)
> plot(g)
> auc(g)
> ci.auc(g)






r data-science roc auc predictive






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edited Nov 22 at 0:04

























asked Nov 21 at 23:54









MhQ-6

255




255












  • Does your ROC curve move towards the top-left corner with better performance? That's the convention. If you have specificity on the x-axis I guess the top-right corner would show better performance, but the area under the curve should be equal either way.
    – Marius
    Nov 21 at 23:58










  • @Marius I added an image of the RUC Curve. The curve moves towards the top right corner but the "peaks" approaches the top left (if that makes any sense).
    – MhQ-6
    Nov 22 at 0:05










  • In other words, it looks like any other ROC graph that I found on google, where the curve starts at the bottom left and moves towards the top right and the peaks extends towards the top left. Literally like any ROC curve you will find using google, only difference is, I have specificity on the x axis. All the ones I found on google have 1-Specificity. Not sure what to make of it.
    – MhQ-6
    Nov 22 at 0:07


















  • Does your ROC curve move towards the top-left corner with better performance? That's the convention. If you have specificity on the x-axis I guess the top-right corner would show better performance, but the area under the curve should be equal either way.
    – Marius
    Nov 21 at 23:58










  • @Marius I added an image of the RUC Curve. The curve moves towards the top right corner but the "peaks" approaches the top left (if that makes any sense).
    – MhQ-6
    Nov 22 at 0:05










  • In other words, it looks like any other ROC graph that I found on google, where the curve starts at the bottom left and moves towards the top right and the peaks extends towards the top left. Literally like any ROC curve you will find using google, only difference is, I have specificity on the x axis. All the ones I found on google have 1-Specificity. Not sure what to make of it.
    – MhQ-6
    Nov 22 at 0:07
















Does your ROC curve move towards the top-left corner with better performance? That's the convention. If you have specificity on the x-axis I guess the top-right corner would show better performance, but the area under the curve should be equal either way.
– Marius
Nov 21 at 23:58




Does your ROC curve move towards the top-left corner with better performance? That's the convention. If you have specificity on the x-axis I guess the top-right corner would show better performance, but the area under the curve should be equal either way.
– Marius
Nov 21 at 23:58












@Marius I added an image of the RUC Curve. The curve moves towards the top right corner but the "peaks" approaches the top left (if that makes any sense).
– MhQ-6
Nov 22 at 0:05




@Marius I added an image of the RUC Curve. The curve moves towards the top right corner but the "peaks" approaches the top left (if that makes any sense).
– MhQ-6
Nov 22 at 0:05












In other words, it looks like any other ROC graph that I found on google, where the curve starts at the bottom left and moves towards the top right and the peaks extends towards the top left. Literally like any ROC curve you will find using google, only difference is, I have specificity on the x axis. All the ones I found on google have 1-Specificity. Not sure what to make of it.
– MhQ-6
Nov 22 at 0:07




In other words, it looks like any other ROC graph that I found on google, where the curve starts at the bottom left and moves towards the top right and the peaks extends towards the top left. Literally like any ROC curve you will find using google, only difference is, I have specificity on the x axis. All the ones I found on google have 1-Specificity. Not sure what to make of it.
– MhQ-6
Nov 22 at 0:07












1 Answer
1






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up vote
1
down vote



accepted










This is purely a labeling problem: note that the x axis goes decreasing from 1 to 0, which is exactly the same as plotting 1-specificity on an x axis increasing from 0 to 1.



I assume you are using the pROC package. This behavior is documented in the FAQ and you can set the legacy.axes argument to TRUE to change the behavior if the default one bothers you.



plot(g, legacy.axes = TRUE)





share|improve this answer





















  • Thank you for the response!
    – MhQ-6
    2 days ago











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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes








up vote
1
down vote



accepted










This is purely a labeling problem: note that the x axis goes decreasing from 1 to 0, which is exactly the same as plotting 1-specificity on an x axis increasing from 0 to 1.



I assume you are using the pROC package. This behavior is documented in the FAQ and you can set the legacy.axes argument to TRUE to change the behavior if the default one bothers you.



plot(g, legacy.axes = TRUE)





share|improve this answer





















  • Thank you for the response!
    – MhQ-6
    2 days ago















up vote
1
down vote



accepted










This is purely a labeling problem: note that the x axis goes decreasing from 1 to 0, which is exactly the same as plotting 1-specificity on an x axis increasing from 0 to 1.



I assume you are using the pROC package. This behavior is documented in the FAQ and you can set the legacy.axes argument to TRUE to change the behavior if the default one bothers you.



plot(g, legacy.axes = TRUE)





share|improve this answer





















  • Thank you for the response!
    – MhQ-6
    2 days ago













up vote
1
down vote



accepted







up vote
1
down vote



accepted






This is purely a labeling problem: note that the x axis goes decreasing from 1 to 0, which is exactly the same as plotting 1-specificity on an x axis increasing from 0 to 1.



I assume you are using the pROC package. This behavior is documented in the FAQ and you can set the legacy.axes argument to TRUE to change the behavior if the default one bothers you.



plot(g, legacy.axes = TRUE)





share|improve this answer












This is purely a labeling problem: note that the x axis goes decreasing from 1 to 0, which is exactly the same as plotting 1-specificity on an x axis increasing from 0 to 1.



I assume you are using the pROC package. This behavior is documented in the FAQ and you can set the legacy.axes argument to TRUE to change the behavior if the default one bothers you.



plot(g, legacy.axes = TRUE)






share|improve this answer












share|improve this answer



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answered Nov 22 at 7:57









Calimo

4,20722248




4,20722248












  • Thank you for the response!
    – MhQ-6
    2 days ago


















  • Thank you for the response!
    – MhQ-6
    2 days ago
















Thank you for the response!
– MhQ-6
2 days ago




Thank you for the response!
– MhQ-6
2 days ago


















 

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