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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$wt)
g2 <- ggplot(data=mtcars, aes(x=centred.wt, y=mpg)) +
geom_point(alpha = 0.7, colour = "#0971B2") +
ylab("Miles per gallon") +
ylim(10, 35) +
xlab("Centred Weight (`000 lbs)") +
ggtitle("Centred Weight") +
geom_vline(xintercept = 0) +
theme_bw()
grid.arrange(g1, g2, nrow = 1, ncol = 2)
centred.model.auto <- lm(mpg ~ I(wt - mean(mtcars$wt)), data = mtcars[mtcars$am == 0, ])
centred.model.manual <- lm(mpg ~ I(wt - mean(mtcars$wt)), data = mtcars[mtcars$am == 1, ])
manual.car <- data.frame(wt = 2.620)
auto.car <- data.frame(wt = 3.570)
# CI for mean
manual.ci <- predict(centred.model.manual, newdata = manual.car,
interval = ("confidence"), level = 0.95)
auto.ci <- predict(centred.model.auto, newdata = auto.car,
interval = ("confidence"), level = 0.95)
library(ggplot2)
gp <- ggplot(data=mtcars, aes(x=centred.wt, y=mpg, colour=am.f)) +
geom_point(alpha = 0.7) +
geom_abline(intercept = coef(centred.model)[1], slope = coef(centred.model)[3],
size = 1, color = "#B21212") +
geom_abline(intercept = coef(centred.model)[1] + coef(centred.model)[2],
slope = coef(centred.model)[3] + coef(centred.model)[4],
size = 1, color = "#0971B2") +
scale_colour_manual(name="Transmission", values =c("#B21212", "#0971B2")) +
ylab("Miles per gallon") +
xlab("Centred Weight (`000 lbs)") +
theme_bw()
gp
new.cars <- data.frame(wt = 2)
# Prediction interval for new values
manual.predict.ci <- predict(centred.model.manual, newdata = new.cars,
interval = ("prediction"), level = 0.95)
auto.predict.ci <- predict(centred.model.auto, newdata = new.cars,
interval = ("prediction"), level = 0.95)
centred.model <- lm(mpg ~ I(wt - mean(wt)) + am.f + I(wt - mean(wt)) * am.f, data = mtcars)
summary(centred.model)$coef
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
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