# Load the CO2 dataset. We used it during workshop 4!
data(CO2)
head(CO2)
# Build a linear model of plant CO2 uptake as a function of CO2 ambient concentration
model.CO2 <- lm(uptake ~ conc, data = CO2)
# Extract the design matrix of the model with the model.matrix() function.
X <- model.matrix(model.CO2)
# And the estimated coefficients.
B <- model.CO2$coefficients
# Let’s multiply both X and B matrices to obtain the linear predictor.
# The "%*%" symbol indicates that it is a matrix product.
XB <- X %*% B
# Compare the values of XB to the values obtained with the predict() function.
# All statements should be TRUE.
# We use the round() function so that all elements have 5 digits.
round(fitted(model.CO2), digits = 5) == round(XB, digits = 5)