Preparations

Load the necessary libraries

library(tidyverse)

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

Scenario

Starlings

Format of starling_full.RSV data files

Read in the data

starling = read_csv('data/starling_full.csv', trim_ws=TRUE)

## Parsed with column specification:
## cols(
##   MONTH = col_character(),
##   SITUATION = col_character(),
##   subjectnum = col_integer(),
##   BIRD = col_character(),
##   MASS = col_integer()
## )

glimpse(starling)

## Observations: 80
## Variables: 5
## $ MONTH      <chr> "Nov", "Nov", "Nov", "Nov", "Nov", "Nov", "Nov", "N...
## $ SITUATION  <chr> "tree", "tree", "tree", "tree", "tree", "tree", "tr...
## $ subjectnum <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7,...
## $ BIRD       <chr> "tree1", "tree2", "tree3", "tree4", "tree5", "tree6...
## $ MASS       <int> 78, 88, 87, 88, 83, 82, 81, 80, 80, 89, 78, 78, 85,...

Exploratory data analysis

Model formula: \[ y_i \sim{} \mathcal{N}(\mu_i, \sigma^2)\\ \mu_i =\boldsymbol{\beta} \bf{X_i} + \boldsymbol{\gamma} \bf{Z_i} \]

where \(\boldsymbol{\beta}\) and \(\boldsymbol{\gamma}\) are vectors of the fixed and random effects parameters respectively and \(\bf{X}\) is the model matrix representing the overall intercept and effects of roosting situation and month on starling mass. \(\bf{Z}\) represents a cell means model matrix for the random intercepts associated with individual birds.

Fit the model

Model validation

Model investigation / hypothesis testing

Predictions

Summary figures

References