I just posted the lab exercise for week 5. You can find it from the Lab Schedule page, or you can click on the link below. As you’ll see, this week’s exercise is a bit different. Rather than analyzing data, you’ll be running a small simulation to explore some properties of drift. Provided that you show results from at least 10 different sets of parameters, you’ll get 10 out of 10 points for this exercise. The next two weeks will be similar, except that I’m liable to ask you to stretch a bit and explain some aspect of the results you see in weeks 6 and 7.

## Updated notes on natural selection

It didn’t take me as long to add notes on fertility selection as I thought it might. I found some old notes that I could borrow from. You can find links to the new versions on the lecture detail page or you can click on the links below to go directly to them. The fertility selection part of the notes comes at the end.

## Project 1 now available

Project 1 is now available. You can find it from the Lab Schedule, or you can download it directly from this link. You’ll be analyzing the same data as you did last week, but it’s in a different format. If you continue to use population genetics in your research after this course, one of the things you’ll discover is that there are a variety of different formats used by different packages. In addition to using R for analyses, you’ll find that you need to learn how to wrangle data in R (or in Python), but I’ll do my best to protect you from that this semester.

## Updated notes on Bayesian estimates of Fst

I included a few details about the Bayesian model that `Hickory` uses that weren’t in the notes I posted earlier – the among population allele frequency distribution and extending the model to includ locus- and population-specific effects. I’ve edited the notes to include those details, and you’ll find new HTML and PDF versions on the website. They’ll appear in the consolidated “book” version of the notes in mid-late December.

## Quick update on today’s lecture

In addition to the additional reading on *F*-statistics that I already provided in the lecture detail associated with today’s lecture, I just added a link to an R notebook that delves a bit more into how Bayesian inference is typically implemented and that illustrates a bit better how the likelihood, prior, and posterior are related to one another. We’ll spend some time exploring these ideas at the start of today’s lecture

## Lab exercise #3 posted

I just posted the notes and data for Lab #3. You’ll find them in the Lab Schedule as usual. As I’ll explain in lecture, we’re going to use `LEA` instead of `STRUCTURE` for this exercise. `STRUCTURE` has some significant advantages over `LEA`, but `LEA` runs much faster. More importantly, I haven’t been able to get `STRUCTURE` to run on my MacBook since I upgraded it to Big Sur. If you have a Mac and you’d like to try getting `STRUCTURE` to run, I’d be delighted if you figured out how. But even if you do figure it out, I recommend that you use `LEA` for this project. Analyzing the data with `STRUCTURE` would take a very long time.

Next week Project #1 will build on the analysis you do this week with LEA to compare it with a different method (DAPC) and to address some of the questions that the authors of the paper from which these data were extracted.

## A quick note about R notebooks

Many of you submitted lab assignments in a format that made it clear that you already know about R notebooks. We’ll spend a little time discussing them in class this morning, including the best way for you to send me an R notebook from a lab assignment. In the meantime, here’s a link to a quick introduction to R notebooks (and another to a blog post I made several years ago after I belatedly discovered how useful they are). I’ll also add the quick introduction link to the lab resources for this week’s lab.

## Lab for the week of 6 September

Yes, I know that Monday is Labor Day. I hope that you enjoy the day off and that you’re not working, but the week still starts on Monday, even if you don’t look at this until Tuesday. I’ve posted the link to the lab on the Lab Schedule page. You’ll see when you visit that one of the `R` packages you’ll be using this week is a bit more complicated than usual. Even if you don’t plan to work on the rest of the lab exercise until later in the week, please try to install `Hickory` as soon as you can so that we can troubleshoot any problems that arise.

Someday I may find the time to chant the incantations I need to chant to turn `Hickory` into a regular `R` package that can live on CRAN, but that won’t happen until sometime next year, if it happens at all.

## Using subset() in R

I just posted a short tutorial on using subset() in R. Here’s the link.

## Fixed a small error in the notes for Thursday

If you’ve downloaded the book version of the notes or downloaded the PDF version of the notes for Thursday, you’ll find that there’s an error in the `Stan` code used to illustrate Bayesian inference for allele frequencies. The line that reads

p ~ dunif(0.0, 1.0);

should read

p ~ uniform(0.0, 1.0);

Sorry about that. I’ve corrected both the PDF and the HTML versions of the notes here. The book version of notes will include the correction after the semester is over (because I’m likely to find other errors that I want to correct).