What’s a Bland-Altman Plot? In Base R

[This article was first published on Steve's Data Tips and Tricks, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.


Before we dive into the code, let’s briefly understand what a Bland-Altman plot is. It’s a graphical method to visualize the agreement between two measurement techniques, often used in fields like medicine or any domain with comparative measurements. The plot displays the differences between two measurements (Y-axis) against their means (X-axis).

Step 1: Data Preparation

Start by loading your data into R. In our example, we’ll create some synthetic data for illustration purposes. You’d replace this with your real data.

# Creating example data
method_A <- c(10, 12, 15, 18, 22, 25)
method_B <- c(9.5, 11, 14, 18, 22, 24.5)

# Calculate the differences and means
diff_values <- method_A - method_B
mean_values <- (method_A + method_B) / 2

df <- data.frame(method_A, method_B, mean_values, diff_values)

Step 2: Calculate Average Difference and CI

Now that we have our data prepared, let’s create the Bland-Altman plot.

mean_diff <- mean(df$diff_values)
[1] 0.5
lower <- mean_diff - 1.96 * sd(df$diff_values)
upper <- mean_diff + 1.96 * sd(df$diff_values)

[1] -0.3765386
[1] 1.376539

Step 3: Creating the Bland-Altman Plot

We are going to do this in base R.

# Create a scatter plot
plot(df$mean_values, df$diff_values, 
     xlab = "Mean of Methods A and B",
     ylab = "Difference (Method A - Method B)",
     main = "Bland-Altman Plot",
     ylim = c(lower + (lower * .1), upper * 1.1))

# Add a horizontal line at the mean difference
abline(h = mean(diff_values), col = "red", lty = 2)

# Add Confidence Intervals
abline(h = upper, col = "blue", lty = 2)
abline(h = lower, col = "blue", lty = 2)

This code will generate a simple Bland-Altman plot, and here’s what each part does:

  • plot(): Creates the scatter plot with means on the X-axis and differences on the Y-axis.
  • abline(h = mean(diff_values), col = "red", lty = 2): Adds a red dashed line at the mean difference.
  • abline(h = upper, col = "green", lty = 2): Adds blue dashed lines representing the 95% limits of agreement.

Step 4: Interpretation

Now that you’ve generated your Bland-Altman plot, let’s interpret it:

  • The red line represents the mean difference between the two methods.
  • The blue dashed lines show the 95% limits of agreement, which help you assess the spread of the differences.

If most data points fall within the blue lines, it indicates good agreement between the two methods. If data points are scattered widely outside the lines, there may be a systematic bias or inconsistency between the methods.

Step 5: Exploration

I encourage you to try this out with your own data. Replace the example data with your measurements and see what insights your Bland-Altman plot reveals.

In conclusion, creating a Bland-Altman plot in R is a valuable technique to visualize agreement or bias between two measurement methods. It’s an essential tool for quality control and validation in various fields. I hope this step-by-step guide helps you get started. Happy plotting!

To leave a comment for the author, please follow the link and comment on their blog: Steve's Data Tips and Tricks.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)