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

Here is a modified example from Peake and Quinn (1993). Peake and Quinn (1993) investigated the relationship between the number of individuals of invertebrates living in amongst clumps of mussels on a rocky intertidal shore and the area of those mussel clumps.

Format of peakquinn.csv data files

The aim of the analysis is to investigate the relationship between mussel clump area and the number of non-mussel invertebrate individuals supported in the mussel clump.

Read in the data

peake = read_csv('data/peakquinn.csv', trim_ws=TRUE)

## Parsed with column specification:
## cols(
##   AREA = col_double(),
##   INDIV = col_integer()
## )

glimpse(peake)

## Observations: 25
## Variables: 2
## $ AREA  <dbl> 516.00, 469.06, 462.25, 938.60, 1357.15, 1773.66, 1686.0...
## $ INDIV <int> 18, 60, 57, 100, 48, 118, 148, 214, 225, 283, 380, 278, ...

Exploratory data analysis

Model formula: \[ y_i \sim{} \mathcal{Pois}(\lambda_i)\\ ln(\lambda_i) = \beta_0 + \beta_1 ln(x_i) \]

Fit the model

Model validation

Model investigation / hypothesis testing

Predictions

Summary figures