Overview

Please provide a reproducible .Rmd script to address the scenario below and produces all of your analysis and plots. Some reminders:

  • Reproducible: This means when I run your .Rmd file, it will run all analyses and create all plots without errors, without me having to reset my working directory, and without forcing me to install anything on my machine (i.e., use the require(librarian); shelf(your packages, lib = tempdir()) approach as in the code block below). This also means there should be NO ERRORS when I run it!
  • Format: In the .Rmd file, arrange and format similar to a scientific article. Specifically, I want to see 1) an analytical methods section, 2) a results section that includes figures, tables, and text interpreting the figures and tables and model(s)!, and 3) a brief conclusion (1 - 2 paragraphs) based on the results. NOTE: You do not need to create an introduction, reiterate the data collection methods, or produce a literature cited section!

Background

For our final homework, you will be creating, running diagnostics, doing model selection, and predicting with Bayesian models. Because this Bayesian stuff is pretty new territory for us, we will be asking some familiar friends for help–the Palmer Penguins. Use our Review of linear models lab for modeling ideas, creating your priors, and expectations for model outputs.

Artwork by @allison_horst
Artwork by @allison_horst

Data description

Information the “penguins” data can be found here: https://allisonhorst.github.io/palmerpenguins/articles/intro.html

Your assignment

Using the ‘brms’ package and the Palmer Penguins dataset, create hierarchical Bayesian models to explain penguin body mass. These models should attempt to explain body mass for all species and sexes and islands in the dataset (i.e., don’t create separate models for the sexes, species, or islands). To complete this assignment, will need to do five main things:

  1. Create three candidate models to explain penguin body mass. IMPORTANT: you will need to specify priors for each population-level effect (fixed effect) and provide reasoning for your choices.
  2. Use a Bayesian model selection technique (e.g., LOO or WAIC) to compare models and identify the “top” model.
  3. Assess the top model’s fit (e.g., Rhat, posterior predictive check, traceplots).
  4. Use the top model to make posterior predictions. Specifically, I want to see marginal effects plot(s) that include any hierarchical levels (i.e., random effects).
  5. Write out your hypotheses (your candidate models–but with words). Compile all the modeling steps and outputs into Methods and Results sections, and then interpret the results in a 1 - 2 paragraph Conclusions section. IMPORTANT: the Methods should include reasoning for your probability distribution choices, choices on priors, etc.
# List of packages necessary to run this script:
require(librarian, quietly = TRUE)
shelf(tidyverse, 
      palmerpenguins, 
      here,
      brms,
      quiet = TRUE)

# Set the web address where R will look for files from this repository
# Do not change this address
repo_url <- "https://raw.githubusercontent.com/LivingLandscapes/Course_EcologicalModeling/master/"

# Read in a data file
data(package = 'palmerpenguins', verbose = FALSE)