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