Our target date for tuning on comments is Wednesday of next week. Don’t hold us to it, but we’re optimistic on meeting our deadline.
In the meantime, Bill Gould is so excited he’s put on hold his latest blog entry. It’s about linear algebra—matrices in particular—so it won’t exactly go out of date, and he’s looking forward to seeing the comments. Bill’s very proud of what he’s written, so be gentle. We’ll put up his posting right after comments are enabled.
From time to time, we get a question from a user puzzled about getting a positive log likelihood for a certain estimation. We get so used to seeing negative log-likelihood values all the time that we may wonder what caused them to be positive.
First, let me point out that there is nothing wrong with a positive log likelihood.
The likelihood is the product of the density evaluated at the observations. Usually, the density takes values that are smaller than one, so its logarithm will be negative. However, this is not true for every distribution. Read more…
In my previous posting last week, I explained how computers store binary floating-point numbers, how Stata’s %21x display format displays with fidelity those binary floating-point numbers, how %21x can help you uncover bugs, and how %21x can help you understand behaviors that are not bugs even though they are surpising to us base-10 thinkers. The point is, it is sometimes useful to think in binary, and with %21x, thinking in binary is not difficult.
This week, I want to discuss double versus float precision. Read more…
%21x is a Stata display format, just as are %f, %g, %9.2f, %td, and so on. You could put %21x on any variable in your dataset, but that is not its purpose. Rather, %21x is for use with Stata’s display command for those wanting to better understand the accuracy of the calculations they make. We use %21x frequently in developing Stata. Read more…