################################################ # WLoss2 Example -- Source code file wloss2.r # ################################################ loss_a = c(12.4, 10.7, 11.9, 11.0, 12.4, 12.3, 13.0, 12.5, 11.2, 13.1) loss_b = c( 9.1, 11.5, 11.3, 9.7, 13.2, 10.7, 10.6, 11.3, 11.1, 11.7) loss_c = c( 8.5, 11.6, 10.2, 10.9, 9.0, 9.6, 9.9, 11.3, 10.5, 11.2) loss_d = c( 8.7, 9.3, 8.2, 8.3, 9.0, 9.4, 9.2, 12.2, 8.5, 9.9) loss_e = c(12.7, 13.2, 11.8, 11.9, 12.2, 11.2, 13.7, 11.8, 11.5, 11.7) loss = c(loss_a, loss_b, loss_c, loss_d, loss_e) center = c(rep('A', 10), rep('B', 10), rep('C', 10), rep('D', 10), rep('E', 10)) # Create and print data frame. wloss = data.frame(center=center, loss=loss) cat("wloss data frame:\n") print(wloss) # Compute anova model. model1 = aov(loss ~ center, data=wloss) cat("Classical ANOVA summary:\n") print(summary(model1)) # Perform multiple comparisons for groups # using Bonferroni's method: print(pairwise.t.test(loss, center, "bonferroni")) # Create vector of dummy values dummy_a = rep(0, 5 * 10) dummy_b = rep(0, 5 * 10) dummy_c = rep(0, 5 * 10) dummy_e = rep(0, 5 * 10) dummy_a[center == 'A'] = 1 dummy_b[center == 'B'] = 1 dummy_c[center == 'C'] = 1 dummy_e[center == 'E'] = 1 # Create data frame named with_dummy with_dummy = data.frame(dummy_a=dummy_a, dummy_b=dummy_b, dummy_c=dummy_c, dummy_e=dummy_e, loss=loss) cat("\nwith_dummy data frame\n") print(with_dummy) # Compute regression model with dummy variables. model2 = lm(loss ~ dummy_a + dummy_b + dummy_c + dummy_e, data=with_dummy) cat("Regression model with dummy variables\n") print(summary(model2)) p = fitted(model2) r = residuals(model2) pdf("wloss2.pdf") # Residual Plot plot(p, r, main="Residual Plot for Model with Dummy Variables", xlab="Predicted Values", ylab="Residuals") abline(h=0, lty="dashed") # Normal Plot qqnorm(r, main="Normal Plot", ylab="Residuals") dev.off( )