Category: R

Nina Zumel takes us through what each element of a ROC curve means:

In our data science teaching, we present the ROC plot (and the area under the curve of the plot, or AUC) as a useful tool for evaluating score-based classifier models, as well as for comparing multiple such models. The ROC is informative and useful, but it’s also perhaps overly concise for a beginner. This leads to a lot of questions from the students: what does the ROC tell us about a model? Why is a bigger AUC better? What does it all mean?

Read on for the answer.

Nagdev Amruthnath covers a topic which brings me joy:

Benford’s Law is one of the most underrated and widely used techniques that are commonly used in various applications. United States IRS neither confirms nor denies their use of Benford’s law to detect any number of manipulations in income tax filing. Across the Atlantic, the EU is very open and proudly claims its use of Benford’s law. Today, this is widely used in accounting to detect any fraud. Nigrini, a professor at the University of Cape Town, also used this law to identify financial discrepancies in Enron’s financial statement. In another case, Jennifer Golbeck, a professor at the University of Maryland, was able to identify bot accounts on twitter using Benford’s law. Xiaoyu Wang from the University of Winnipeg even published a report on how to use Benford’s law on images. In the rest of this article, we will take about Benford’s law and how it can be applied using R.

The applications to images and music were new to me. Very cool. H/T R-Bloggers

Mattan Ben-Shachar gives us an intuitive understanding of multicollinearity and how it can affect an analysis:

The common and almost default approach is to fix age to a constant. This is really what our model does in the first place: the coefficient of height represents the expected change in weight while age is fixed and not allowed to vary. What constant? A natural candidate (and indeed emmeans’ default) is the mean. In our case, the mean age is 14.9 years. So the expected values produced above are for three 14.9 year olds with different heights. But is this data plausible? If I told you I saw a person who was 120cm tall, would you also assume they were 14.9 years old?

No, you would not. And that is exactly what covariance and multicollinearity mean – that some combinations of predictors are more likely than others.

I liked the explanation Mattan provides us. Also be sure to read the warnings near the end of the post around other things to try. H/T R-bloggers

Joshua Entrop shows off optimx() in R to perform a survival analysis:

In this blog post, we will fit a Poisson regression model by maximising its likelihood function using optimx() in R. As an example we will use the lung cancer data set included in the {survival} package. The data set includes information on 228 lung cancer patients from the North Central Cancer Treatment Group (NCCTG). Specifically, we will estimate the survival of lung cancer patients by sex and age using a simple Poisson regression model. You can download the code that I will use throughout post here. 

Read the whole thing. H/T R-bloggers

Holger von Jouanne-Diedrich explains how randomized response can protect any single person’s opinion from a pollster while providing insight on the whole population:

So, is there a method to find the respective proportion of people without putting them on the spot? Actually, there is! If you want to learn about randomized response (and how to create flowcharts in R along the way) read on!

The question is how can you get a truthful result overall without being able to attribute a certain answer to any single individual. As it turns out, there is a very elegant and ingenious method, called randomized response. The big idea is to, as the name suggests, add noise to every answer without compromising the overall proportion too much, i.e. add noise to every answer so that it cancels out overall!

Click through for the process. It’s definitely a clever idea.