You probably know by now that often there are many, many ways to do something in MATLAB. For example, there are several different ways to draw lines and curves on a figure, including plot() and line(). You can add vertical lines using xline() and horizontal lines using yline().

refline()

But, what if you want to add a line with a particular slope and y-intercept, like in the example below?

Aerosol mass concentrations measured by two channels (“A” and “B”) of a PurpleAir sensor.

You can do this very easily by using the refline() function:

figure
scatter(PA_PM_data.PA_3_A,PA_PM_data.PA_3_B)
xlabel("PA 3A aerosol mass concentration (\mu g / m^3)")
ylabel("PA 3B aerosol mass concentration (\mu g / m^3)")
refline(1,0)

Note that we’ve passed the arguments 1 and 0 to refline() indicating the slope = 1 and the intercept = 0.

Does this example look familiar? These are the same data we saw in the post on using “flags” to filter data.

refline()’s superpower!

Sometimes you don’t know the values of the slope and intercept to use and you just want to plot a linear least squares best fit line to the data. Do you really need to go through the process of fitting the data (perhaps using fitlm() or fitrlinear())? Maybe you just want to see the line on the plot and don’t care about the regression coefficients.

Enter refline()’s super power! Just call refline() with no arguments - no slope or intercept - and it will plot the best fit linear least squares line!

As above but with a best fit linear least squares line added.

Now we can readily see that the best fit line has a slope greater than 1 indicating that the PA 3B channel is reading (slightly) systematically higher than the PA 3A channel.

What if you have more than one set of data plotted on a single figure and you want best fit lines for each? That’s where lsline() comes in …

Read more at the official MathWorks documentation for the refline() function.
Read more at the official MathWorks documentation for the lsline() function.