Code associated with blog post

centred_chart.R

library(ggplot2); library(gridExtra)
g1 <- ggplot(data=mtcars, aes(x=wt, y=mpg)) + 
        geom_point(alpha = 0.7, colour = "#0971B2") +
        ylab("Miles per gallon") + 
        ylim(10, 35) +
        xlab("Weight (`000 lbs)") +
        ggtitle("Untransformed Weight") + 
        geom_vline(xintercept = 0) +
        theme_bw()

mtcars$centred.wt <- mtcars$wt - mean(mtcars$wt)
g2 <- ggplot(data=mtcars, aes(x=centred.wt, y=mpg)) + 
        geom_point(alpha = 0.7, colour = "#0971B2") +
        ylab("Miles per gallon") + 
        ylim(10, 35) +
        xlab("Centred Weight (`000 lbs)") +
        ggtitle("Centred Weight") +  
        geom_vline(xintercept = 0) +
        theme_bw()

grid.arrange(g1, g2, nrow = 1, ncol = 2)

confidence_intervals.R

centred.model.auto <- lm(mpg ~ I(wt - mean(mtcars$wt)), data = mtcars[mtcars$am == 0, ])
centred.model.manual <- lm(mpg ~ I(wt - mean(mtcars$wt)), data = mtcars[mtcars$am == 1, ])

manual.car <- data.frame(wt = 2.620)
auto.car <- data.frame(wt = 3.570)

# CI for mean
manual.ci <- predict(centred.model.manual, newdata = manual.car, 
                     interval = ("confidence"), level = 0.95)
auto.ci <- predict(centred.model.auto, newdata = auto.car, 
                   interval = ("confidence"), level = 0.95)

interaction_model.R

library(ggplot2)
gp <- ggplot(data=mtcars, aes(x=centred.wt, y=mpg, colour=am.f)) + 
        geom_point(alpha = 0.7) +
        geom_abline(intercept = coef(centred.model)[1], slope = coef(centred.model)[3], 
                    size = 1, color = "#B21212") +
        geom_abline(intercept = coef(centred.model)[1] + coef(centred.model)[2], 
                    slope = coef(centred.model)[3] + coef(centred.model)[4], 
                    size = 1, color = "#0971B2") +
        scale_colour_manual(name="Transmission",  values =c("#B21212", "#0971B2")) +
        ylab("Miles per gallon") +    
        xlab("Centred Weight (`000 lbs)") +
        theme_bw()
gp

prediction_intervals.R

new.cars <- data.frame(wt = 2)

# Prediction interval for new values
manual.predict.ci <- predict(centred.model.manual, newdata = new.cars, 
                             interval = ("prediction"), level = 0.95)
auto.predict.ci <- predict(centred.model.auto, newdata = new.cars, 
                           interval = ("prediction"), level = 0.95)

running_the_centred_model.R

centred.model <- lm(mpg ~ I(wt - mean(wt)) + am.f + I(wt - mean(wt)) * am.f, data = mtcars)
summary(centred.model)$coef

running_the_model.R

data(mtcars)
mtcars$am.f <- as.factor(mtcars$am); levels(mtcars$am.f) <- c("Automatic", "Manual")

# Build the model
model <- lm(mpg ~ wt + am.f + wt * am.f, data = mtcars)
summary(model)$coef