An incredibly useful tool in evaluating and comparing predictive models is the ROC curve. Its name is indeed strange. ROC stands for receiver operating characteristic. Its origin is from sonar back in the 1940s; ROCs were used to measure how well a sonar signal (e.g., from a submarine) could be detected from noise (a school of fish). In its current usage, ROC curves are a nice way to see how any predictive model can distinguish between the true positives and negatives. In order to do this, a model needs to not only correctly predict a positive as a positive, but also a negative as a negative. The ROC curve does this by plotting sensitivity, the probability of predicting a real positive will be a positive, against 1-specificity, the probability of predicting a real negative will be a positive. (A previous newsletter article covered the specifics of sensitivity and specificity, in case you need a review about what they mean–and why it’s important to know how accurately the model is predicting positives and negatives separately.) The best decision rule is high on sensitivity and low on 1-specificity. It’s a rule that predicts most true positives will be a positive and few true negatives will be a positive. I’ve been talking about decision rules, but what about models?

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In this webinar, we’ll provide a gentle introduction to generalized linear mixed models (or GLMMs). You’ll become familiar with the major issues involved in working with GLMMs so you can more easily transition to using these models in your work.

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