```
## Concordance correlation plot:
set.seed(seed = 1234)
method1 <- rnorm(n = 100, mean = 0, sd = 1)
method2 <- method1 + runif(n = 100, min = 0, max = 1)
## Introduce some missing values:
method1[50] <- NA
method2[75] <- NA
tmp.ccc <- CCC(method1, method2, ci = "z-transform",
conf.level = 0.95)
lab <- paste("CCC: ", round(tmp.ccc$rho.c[,1], digits = 2), " (95% CI ",
round(tmp.ccc$rho.c[,2], digits = 2), " - ",
round(tmp.ccc$rho.c[,3], digits = 2), ")", sep = "")
z <- lm(method2 ~ method1)
par(pty = "s")
plot(method1, method2, xlim = c(0, 5), ylim = c(0,5), xlab = "Method 1",
ylab = "Method 2", pch = 16)
abline(a = 0, b = 1, lty = 2)
abline(z, lty = 1)
legend(x = "topleft", legend = c("Line of perfect concordance",
"Reduced major axis"), lty = c(2,1), lwd = c(1,1), bty = "n")
text(x = 1.55, y = 3.8, labels = lab)
## Bland and Altman plot (Figure 2 from Bland and Altman 1986):
x <- c(494,395,516,434,476,557,413,442,650,433,417,656,267,
478,178,423,427)
y <- c(512,430,520,428,500,600,364,380,658,445,432,626,260,
477,259,350,451)
tmp.ccc <- CCC(x, y, ci = "z-transform", conf.level = 0.95)
tmp.mean <- mean(tmp.ccc$blalt$delta)
tmp.sd <- sqrt(var(tmp.ccc$blalt$delta))
plot(tmp.ccc$blalt$mean, tmp.ccc$blalt$delta, pch = 16,
xlab = "Average PEFR by two meters (L/min)",
ylab = "Difference in PEFR (L/min)", xlim = c(0,800),
ylim = c(-140,140))
abline(h = tmp.mean, lty = 1, col = "gray")
abline(h = tmp.mean - (2 * tmp.sd), lty = 2, col = "gray")
abline(h = tmp.mean + (2 * tmp.sd), lty = 2, col = "gray")
legend(x = "topleft", legend = c("Mean difference",
"Mean difference +/ 2SD"), lty = c(1,2), bty = "n")
legend(x = 0, y = 125, legend = c("Difference"), pch = 16,
bty = "n")
```

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