CCplot | R Documentation |
Plotting function for reliability measure.
CCplot( method1, method2, Ptype = "None", metrics = FALSE, xlabel = "", ylabel = "", title = "", subtitle = NULL, xrange = NULL, yrange = NULL, MArange = c(-3.5, 5.5) )
method1 |
measurements obtained in batch 1 or using method 1 |
method2 |
measurements obtained in batch 2 or using method 2 |
Ptype |
type of plot to be outputted c("scatter", "MAplot") |
metrics |
if |
xlabel |
x-axis label for scatterplot |
ylabel |
y-axis label for scatterplot |
title |
title for the main plot |
subtitle |
subtitle of plot |
xrange |
range of x axis |
yrange |
range of y axis |
MArange |
MA range |
Either a scatterplot or MA plot showing concordance correlation.
Aline Talhouk
# Simulate normally distributed data set.seed(12) a1 <- rnorm(20) + 2 a2 <- a1 + rnorm(20, 0, 0.15) a3 <- a1 + rnorm(20, 0, 0.15) + 1.4 a4 <- 1.5 * a1 + rnorm(20, 0, 0.15) a5 <- 1.3 * a1 + rnorm(20, 0, 0.15) + 1 a6 <- a1 + rnorm(20, 0, 0.8) # One scatterplot CCplot(a1, a2, Ptype = "scatter") m2 <- list(a1, a2, a3, a4, a5, a6) mains <- c("Perfect Agreement", "Very Good Agreement", "Location Shift", "Scale Shift", "Location and Scale Shift", "Measurement Error") subs <- letters[1:6] par(mfrow = c(3, 2), mar = c(5.1, 4.1, 1.5, 1.5)) # Scatterplots mapply(function(y, t, s) CCplot(method1 = a1, method2 = y, Ptype = "scatter", xlabel = "X", ylabel = "Y", title = t, subtitle = s), y = m2, t = mains, s = subs) # MAplots and show metrics mapply(function(y, t, s) CCplot(method1 = a1, method2 = y, Ptype = "MAplot", title = t, subtitle = s, metrics = TRUE), y = m2, t = mains, s = subs)
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