Author: Kevin Feasel

Nina Zumel compares a pair of methods for performing regression when income is the dependent variable:

Regressing against the log of the outcome will not be calibrated; however it has the advantage that the resulting model will have lower relative error than a Poisson regression against income. Minimizing relative error is appropriate in situations when differences are naturally expressed in percentages rather than in absolute amounts. Again, this is common when financial data is involved: raises in salary tend to be in terms of percentage of income, not in absolute dollar increments.

Unfortunately, a full discussion of the differences between Poisson regression and regressing against log amounts was outside of the scope of our book, so we will discuss it in this note.

This is an interesting post with a great teaser for the next post in the series.

Julia Silge announces a new package, tidylo:

The package contains examples in the README and vignette, but let’s walk though another, different example here. This weighted log odds approach is useful for text analysis, but not only for text analysis. In the weeks since we’ve had this package up and running, I’ve found myself reaching for it in multiple situations, both text and not, in my real-life day job. For this example, let’s look at the same data as my last post, names given to children in the US.

Which names were most common in the 1950s, 1960s, 1970s, and 1980?

This package looks like it’s worth checking out if you deal with frequency-based problems.