Adjusting for non-linear relationship
The next three examples work with datasets where the underlying data structure violates the linear regression technical conditions. For each example, you will apply a transformation to the data in order to create residual plots that look scattered.
In this first example, it appears as though the variables are not linearly related.
Cet exercice fait partie du cours
Inference for Linear Regression in R
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Run this to see how the model looks
ggplot(hypdata_nonlinear, aes(x = explanatory, y = response)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
# Model response vs. explanatory
model <- ___
# Extract observation-level information
modeled_observations <- ___
# See the result
modeled_observations
# Using modeled_observations, plot residuals vs. fitted values
___ +
# Add a point layer
___ +
# Add horizontal line at y = 0
___