Logistic Regression - ROC curve plots Sensitivity vs Specificity instead of (1-Specificity) I am new to Machine Learning and have been doing some practice on Logistic Regression. To evaluate the models, I've been trying to create some ROC plots. 
The package that i used is pROC. 

The model name is - 'model' dataset is 'data'.

The code I used in R is:

library(pROC)  
predictionData &lt;- predict(model, newdata = data, type = "response")  
rocModel &lt;- roc(data$y ~ as.numeric(predictionData&gt;0.5))  
plot(rocModel)


Ideally, from what i have learned, ROC should plot Sensitivity or TPR (True Positive Rate) vs 1 - Specificity. But as shown in the picture below, it shows Sensitivity vs Specificity. 



Am I missing some obvious trick here or is something wrong with what I have done ?
As you can see, the specificity in the x-axis goes from 1.0 to 0.0 (backwards).

So the effect is the same, you are seeing 1-Specificity