homework.Rmd

---
title: "Labs and Lab Write-ups"
output:
  html_document:
    toc: true
    toc_float: true
    toc_depth: 3
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

The labs for each week are listed on the [Computer Labs](comp_labs.html) tab. There are 6 labs with write-ups to turn in, listed below. The first lab on March 30 will get you oriented with GitHub, R, and RMarkdown that we will use during the course. There is an optional tutorial for you to work through on matrix math in R. You need to know how to work with matrices in R for the course material. The labs on May 18 & 25 will not have write-ups to turn in so that you can work on your final projects, and the last lab on June 1 is for student presentations.

* Lab #1 Modeling and forecasting salmon returns with ARIMA models. Due: Apr 18 Tues 11:59 PM PDT 
* Lab #2 Multivariate state-space models. Due: April 25 Tues 11:59 PM PDT
* Lab #3 Dynamic Factor Analysis. Due: May 2 Tues 11:59 PM PDT
* Lab #4 Hidden Markov Models. Due: May 9 Tues 11:59 PM PDT
* Lab #5 Turn in a draft of your [Project Methods](final_proj.html#Project_Methods). Due: Fri May 12 11:59pm PDT
* Lab #6 Dynamic Linear Models. Due: Tues May 23 11:59pm PDT

## Lab write-up structure

We will share some data for your group to work with. You'll use those data and the techniques being studied that week to do an analysis of the data. The work on your lab write-up should not take more than **8 hours** total and you might finish much quicker (so don't feel like you have to spend 8 hours!). The lab template will guide you through the elements that your analysis should have, but beyond those elements, your group will come up with its own research question or objective.

## Group work and collaboration

For each lab write-up, a team of 3-4 will be chosen randomly for each week. Each team will submit one lab write-up. 

The lab write-ups are intended for you to learn techniques in the class by doing a real analysis. You are encouraged to share code with other groups and ask each other questions about how to approach an analysis. Feel free to share your work as you go along on the class GitHub repository. You are not competing with your classmates. Labs are a collaborative learning experience and can learn from seeing each others work. [Discusssion board](https://github.com/atsa-es/fish550-2023/discussions)

## Lab write-up evaluation

Each lab will have a template of elements to include and a minimal set of techniques and diagnostics to use for the analysis. Each lab will also have a section for lab members to discuss their contributions to the write-up and analysis. See the template for examples.

Full marks if each element is addressed. Reach out to the instructors on the [discusssion board](https://github.com/atsa-es/fish550-2023/discussions) if you run into any trouble with the elements. The lab write-ups are intended to assist your learning of the material by doing a practical analysis. Extra credit for analyses that go above and beyond.

## Lab write-up format

Please submit your lab write-up as an Rmarkdown document (.Rmd) or Quarto document (.qmd), which will allow you to combine text, equations, and R code into a pdf or html file. The easiest way to do so is to use the built-in capabilities of RStudio; note for Quarto you need a recent version of RStudio. For those unfamiliar with Rmarkdown, there is a nice introduction [here](https://qerm514.github.io/website/comp_labs.html) on the first QERM 514 lab. There is also help available in RStudio. 

**Lab template** The lab template along with the group names for each lab will be found in the [Class GitHub repo](https://github.com/atsa-es/fish550-2023) folder for the lab. You will find a link to the lab repo on the  [computer labs page](https://atsa-es.github.io/atsa/comp_labs.html) in the "repo" column.  Push your team's completed write-up to the [Class GitHub repo](https://github.com/atsa-es/fish550-2023) by the deadline. If you want comments on your write-up before the deadline: create a branch, make a pull request and use the review feature or `@` the instructors in the comments of the pull request.