AE 11: Modelling fish

Suggested answers

Application exercise

For this application exercise, we will work with data on fish. The dataset we will use, called fish, is on two common fish species in fish market sales.

library(tidyverse)
library(tidymodels)

fish <- read_csv("data/fish.csv")

The data dictionary is below:

variable description
species Species name of fish
weight Weight, in grams
length_vertical Vertical length, in cm
length_diagonal Diagonal length, in cm
length_cross Cross length, in cm
height Height, in cm
width Diagonal width, in cm

Visualizing the model

We’re going to investigate the relationship between the weights and heights of fish.

  • Create an appropriate plot to investigate this relationship. Add appropriate labels to the plot.
ggplot(fish, aes(x = height, y = weight)) +
  geom_point() +
  labs(
    title = "Weights vs. lengths of fish",
    x = "Head-to-tail lentgh (cm)",
    y = "Weight of fish (grams)"
  )

  • If you were to draw a a straight line to best represent the relationship between the heights and weights of fish, where would it go? Why?

  • Now, let R draw the line for you.

ggplot(fish, aes(x = height, y = weight)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(
    title = "Weights vs. lengths of fish",
    x = "Head-to-tail lentgh (cm)",
    y = "Weight of fish (grams)"
  )
`geom_smooth()` using formula = 'y ~ x'

  • What types of questions can this plot help answer?

    Is there a relationship between fish heights and weights of fish?

  • We can use this line to make predictions. Predict what you think the weight of a fish would be with a height of 10 cm, 15 cm, and 20 cm. Which prediction is considered extrapolation?

    At 10 cm, we estimate a weight of 375 grams. At 15 cm, we estimate a weight of 600 grams At 20 cm, we estimate a weight of 975 grams. 20 cm would be considered extrapolation.
  • What is a residual?

    Difference between predicted and observed.

Model fitting

  • Demo: Fit a model to predict fish weights from their heights.
fish_hw_fit <- linear_reg() |>
  fit(weight ~ height, data = fish)

fish_hw_fit
parsnip model object


Call:
stats::lm(formula = weight ~ height, data = data)

Coefficients:
(Intercept)       height  
    -288.42        60.92
  • Predict what the weight of a fish would be with a height of 10 cm, 15 cm, and 20 cm using this model.
x <- c(10, 15, 20)
-288 + 60.92 * x
[1] 321.2 625.8 930.4
  • Demo: Calculate predicted weights for all fish in the data and visualize the residuals under this model.
fish_hw_aug <- augment(fish_hw_fit, new_data = fish)

ggplot(fish_hw_aug, aes(x = height, y = weight)) + 
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE, color = "lightgrey") +  
  geom_segment(aes(xend = height, yend = .pred), color = "gray") +  
  geom_point(aes(y = .pred), shape = "circle open") + 
  theme_minimal() +
  labs(
    title = "Weights vs. heights of fish",
    subtitle = "Residuals",
    x = "Height (cm)",
    y = "Weight (gr)"
  )
`geom_smooth()` using formula = 'y ~ x'

Model summary

  • Demo: Display the model summary including estimates for the slope and intercept along with measurements of uncertainty around them. Show how you can extract these values from the model output.
fish_hw_tidy <- tidy(fish_hw_fit)
fish_hw_tidy
# A tibble: 2 × 5
  term        estimate std.error statistic  p.value
  <chr>          <dbl>     <dbl>     <dbl>    <dbl>
1 (Intercept)   -288.      34.0      -8.49 1.83e-11
2 height          60.9      2.64     23.1  2.40e-29
  • Demo: Write out your model using mathematical notation.

Add response here.

Correlation

We can also assess correlation between two quantitative variables.

  • What is correlation? What are values correlation can take?

Add response here.

fish |>
  summarize(r = cor(height, weight))
# A tibble: 1 × 1
      r
  <dbl>
1 0.954

Adding a third variable

  • Demo: Does the relationship between heights and weights of fish change if we take into consideration species? Plot two separate straight lines for the Bream and Roach species.
ggplot(fish, 
       aes(x = height, y = weight, color = species)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(
    title = "Weights vs. heights of fish",
    x = "Height (cm)",
    y = "Weight (gr)"
  )
`geom_smooth()` using formula = 'y ~ x'

Fitting other models

  • Demo: We can fit more models than just a straight line. Change the following code below to read method = "loess". What is different from the plot created before?
ggplot(fish, 
       aes(x = height, y = weight)) +
  geom_point() +
  geom_smooth(method = "loess") +
  labs(
    title = "Weights vs. heights of fish",
    x = "Height (cm)",
    y = "Weight (gr)"
  )
`geom_smooth()` using formula = 'y ~ x'