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Modeling an interaction

In this exercise, you will use interactions to model the effect of gender and gastric activity on alcohol metabolism.

The alcohol data frame has been pre-loaded, and has the columns:

  • Metabol: the alcohol metabolism rate
  • Gastric: the rate of gastric alcohol dehydrogenase activity
  • Sex: the sex of the drinker (Male or Female)

In the video, we fit three models to the alcohol data:

  • one with only additive (main effect) terms : Metabol ~ Gastric + Sex
  • two models, each with interactions between gastric activity and sex

You saw that one of the models with interaction terms had a better R-squared than the additive model, suggesting that using interaction terms gives a better fit. In this exercise, you will compare the R-squared of one of the interaction models to the main-effects-only model.

Recall that the operator : designates the interaction between two variables. The operator * designates the interaction between the two variables, plus the main effects.

x*y = x + y + x:y

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Supervised Learning in R: Regression

ดูคอร์ส

คำแนะนำการฝึกหัด

  • Write a formula that expresses Metabol as a function of Gastric and Sex with no interactions.
    • Assign the formula to the variable fmla_add and print it.
  • Write a formula that expresses Metabol as a function of the interaction between Gastric and Sex.
    • Add Gastric as a main effect, but not Sex.
    • Assign the formula to the variable fmla_interaction and print it.
  • Fit a linear model with only main effects: model_add to the data.
  • Fit a linear model with the interaction: model_interaction to the data.
  • Call summary() on both models. Which has a better R-squared?

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ

ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์

# alcohol is available
summary(alcohol)

# Create the formula with main effects only
(fmla_add <- ___ )

# Create the formula with interactions
(fmla_interaction <- ___ )

# Fit the main effects only model
model_add <- ___

# Fit the interaction model
model_interaction <- ___

# Call summary on both models and compare
___
___
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