linearity_test_r.py

%%R -i y

p1 <- ggplot(lin_reg, aes(.fitted, .resid)) + geom_point()
p1 <- p1 + stat_smooth(method="loess") + geom_hline(yintercept=0, col="red", linetype="dashed")
p1 <- p1 + xlab("Predicted") + ylab("Residuals")
p1 <- p1 + ggtitle("Residuals vs. Predicted Values") + theme_bw()

df_plt <- data.frame("fitted" = fitted(lin_reg), "observed" = X$medv)
p2 <- ggplot(df_plt, aes(x=fitted, y=observed)) + geom_point()
p2 <- p2 + stat_smooth(method="loess") + geom_abline(intercept = 1, col="red", linetype="dashed")
p2 <- p2 + xlab("Predicted") + ylab("Observed")
p2 <- p2 + ggtitle("Observed vs. Predicted Values") + theme_bw()

grid.arrange(p2, p1, ncol=2)