It can be pretty tricky to interpret the results of statistical analysis sometimes, and particularly so when just gazing at a table of regression coefficients that include multiple interactions. I wrote a post recently on visualising these interactions when variables are continuous; in that instance, I computed the effect of X on Y when the moderator variables (Z, W) are held constant at high and low values (which I set as +/- 1 standard deviation from the mean). This gave 4 different slopes to visualise: low Z & low W, low Z & high W, high Z & low W, and high Z & high W. It was simple enough to plot these on a single figure and see what effect the interaction of Z and W had on the relationship between Y and X.

I had a comment underneath from someone who had a similar problem, but where the interacting variables consisted of a single continuous and 2 categorical variables (rather than 3 continuous variables). Given that we have distinct levels for the modifier variables Z and W then our job is made a little easier, as the analysis provides coefficients for every main effect and interaction. However, each of the categorical modifier variables here has 3 levels, giving 9 different combinations of Y ~ X. While we could take the same approach as last time (creating predicted slopes and plotting them on a single figure), that wouldn’t produce a very intuitive figure. Instead, let’s use ggplot2’s excellent ‘facets‘ function to produce multiple figures within a larger one.

This approach is termed ‘small multiples’, a term popularised by statistician and data visualisation guru Edward Tufte. I’ll hand it over to him to describe the thinking behind it:

“Illustrations of postage-stamp size are indexed by category or a label, sequenced over time like the frames of a movie, or ordered by a quantitative variable not used in the single image itself. Information slices are positioned within the eyespan, so that viewers make comparisons at a glance — uninterrupted visual reasoning. Constancy of design puts the emphasis on changes in data, not changes in data frames.”

*–Edward Tufte, ‘Envisioning Information‘*

Each small figure should have the same measures and scale; once you’ve understood the basis of the first figure, you can then move across the others and see how it responds to changes in a third variable (and then a fourth…). The technique is particularly useful for multivariate data because you can compare and contrast changes between the main relationship of interest (Y ~ X) as the values of other variables (Z, W) change. Sounds complex, but it’s really very simple and intuitive, as you’ll see below!

Ok, so the commenter on my previous post included the regression coefficients from her mixed model analysis that was specified as lmer(Y ~ X*Z*W+(1|PPX),data=Matrix). W and Z each have 3 levels: low, medium, and high, but these are to be treated as categorical rather than continuous – i.e., we get coefficients for each level. We’ll also disregard the random effects here, as we’re interested in plotting only the main effects.

I don’t have the raw data, so I’m simply going to plot the predicted slopes on values of X from 0 to 100; I’m first going to make this sequence, and enter all my coefficients as variables:

x <- seq(-100, 100) int_global <- -0.0293 X_coef <- 0.0007 WHigh <- -0.0357 WMedium <- 0.0092 ZHigh <- -0.0491 ZMedium <- -0.0314 X_WHigh <- 0.0007 X_WMedium <- 0.0002 X_ZHigh <- 0.0009 X_ZMedium <- 0.0007 WHigh_ZHigh <- 0.0004 WMedium_ZHigh <- -0.0021 WHigh_ZMedium <- -0.0955 WMedium_ZMedium <- 0.0143 X_WHigh_ZHigh <- -0.0002 X_WMedium_ZHigh <- -0.0004 X_WHigh_ZMedium <- 0.0013 X_WMedium_ZMedium <- -0.0004

The reference values are Low Z and Low W; you can see that we only have coefficients for Medium/High values of these variables, so they are offset from the reference slope. The underscore in my variable names denotes an interaction; when X is involved then it’s an effect on the slope of Y on X, whereas otherwise it affects the intercept.

Let’s go ahead and predict Y values for each value of X when Z and W are both Low (i.e., using the global intercept and the coefficient for the effect of X on Y):

y.WL_ZL <- int_global + (x * X_coef) df.WL_ZL <- data.frame(x = x, W = "W:Low", Z = "Z:Low", y = y.WL_ZL)

Now, let’s change a single variable, and plot Y on X when Z is at ‘Medium’ level (still holding W at ‘Low’):

# Change Z y.WL_ZM <- int_global + ZMedium + (x * X_coef) + (x * X_ZMedium) df.WL_ZM <- data.frame(x = x, W = "W:Low", Z = "Z:Medium", y = y.WL_ZM)

Remember, because the coefficients of Z/W are offsets, we add these effects on top of the reference levels. You’ll notice that I specified the level of Z and W as ‘Z:Low’ and ‘W:Low’; while putting the variable name in the value is redundant, you’ll see later why I’ve done this.

We can go ahead and make mini data frames for each of the different interactions:

y.WL_ZH <- int_global + ZHigh + (x * X_coef) + (x * X_ZHigh) df.WL_ZH <- data.frame(x = x, W = "W:Low", Z = "Z:High", y = y.WL_ZM) # Change W y.WM_ZL <- int_global + WMedium + (x * X_coef) + (x * X_WMedium) df.WM_ZL <- data.frame(x = x, W = "W:Medium", Z = "Z:Low", y = y.WM_ZL) y.WH_ZL <- int_global + WHigh + (x * X_coef) + (x * X_WHigh) df.WH_ZL <- data.frame(x = x, W = "W:High", Z = "Z:Low", y = y.WM_ZL) # Change both y.WM_ZM <- int_global + WMedium + ZMedium + WMedium_ZMedium + (x * X_coef) + (x * X_ZMedium) + (x * X_WMedium) + (x * X_WMedium_ZMedium) df.WM_ZM <- data.frame(x = x, W = "W:Medium", Z = "Z:Medium", y = y.WM_ZM) y.WM_ZH <- int_global + WMedium + ZHigh + WMedium_ZHigh + (x * X_coef) + (x * X_ZHigh) + (x * X_WMedium) + (x * X_WMedium_ZHigh) df.WM_ZH <- data.frame(x = x, W = "W:Medium", Z = "Z:High", y = y.WM_ZH) y.WH_ZM <- int_global + WHigh + ZMedium + WHigh_ZMedium + (x * X_coef) + (x * X_ZMedium) + (x * X_WHigh) + (x * X_WHigh_ZMedium) df.WH_ZM <- data.frame(x = x, W = "W:High", Z = "Z:Medium", y = y.WM_ZH) y.WH_ZH <- int_global + WHigh + ZHigh + WHigh_ZHigh + (x * X_coef) + (x * X_ZHigh) + (x * X_WHigh) + (x * X_WHigh_ZHigh) df.WH_ZH <- data.frame(x = x, W = "W:High", Z = "Z:High", y = y.WH_ZH)

Ok, so we now have a mini data frame giving predicted values of Y on X for each combination of Z and W. Not the most elegant solution, but fine for our purposes! Let’s go ahead and concatenate these data frames into one large one, and throw away the individual mini frames:

Now all that’s left to do is to plot the predicted regression slopes from the analysis!

First, I call the ggplot2 library. The main function call to ggplot specifies our data frame (df.XWZ), and the variables to be plotted (x,y). Then, ‘geom_line()‘ indicates that I want the observations to be connected by lines. Next, ‘facet_grid‘ asks for this figure to be laid out in a grid of panels; ‘W~Z’ specifies that these panels are to be laid out in rows for values of W and columns for values of Z. Setting ‘as.table’ to FALSE simply means that the highest level of W is at the top, rather than the bottom (as would be the case if it were laid out in table format, but as a figure I find it more intuitive to have the highest level at the top). Finally, ‘theme_bw()’ just gives a nice plain theme that I prefer to ggplot2’s default settings.

There we have it! A small multiples plot to show the results of a multiple regression analysis, with each panel showing the relationship between our response variable (Y) and continuous predictor (X) for a unique combination of our 3-level moderators (Z, W).

You’ll notice here that the facet labels have things like ‘Z:Low’ etc in them, which I coded into the data frames. This is purely because ggplot2 doesn’t automatically label the outer axes (i.e., as Z, W), and I find this easier than remembering which variable I’ve defined as rows/columns…

Hopefully this is clear – any questions, please leave a comment. The layout of R script was created by Pretty R at inside-R.org. Thanks again to Rebekah for her question on my previous post, and letting me use her analysis as an example here.

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*Want to know more about understanding and visualising interactions in multiple linear regression? Check out my related posts:*

Understanding three-way interactions between continuous variables

Three-way interactions between 2 continuous and 1 categorical variable

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Great post, it kind of helped me and my analysis woes…

Any suggestions for a 3 way interaction with 1 categorical and 2 continuous variables? I use Stata and have been trying to figure out how I can test if there are ethnic differences between muscle (predictor) and bone (outcome) with age?

any help would be greatly appreciated

Cheers,

Ayse

Based on your question, I’ve written a new post – hope it helps a little?! Let me know if you have any problems… https://tomhouslay.com/2015/06/02/understanding-3-way-interactions-between-continuous-and-categorical-variables-part-ii-2-cats-1-con/

This is perhaps a belated comment given when the post was written, but I am struggling with a similar issue, and found your approach very helpful. However, I have two questions, both related to inference. Do you have some suggestion to add a histogram of the continuous predictor (X in your example) to the background of each grid? Secondly, do you have any suggestion to add confidence intervals to the plots?

In my specific case I am running glmer models, I am interested in a continuousXdummyXdummy interation.

Also: given that it’s a glmer multivariate model, should I include in the calculation all the fixed effects coefficients of the control variables? how about the random effects?

Hi Endre,

These are interesting points – I have been hoping to get the time to update some of these posts, but that hasn’t happened as yet! I would not recommend adding a histogram of X in the background, but instead I think overlaying low-alpha raw data points on your predictions would show the spread of the data (while also indicating some level of confidence in your predictions). The one thing to be careful of, given that you are using glmer, is that you will either have to back-transform predicted values, or transform your raw data into parameter space (transforms depending on which error family you are using). I actually use the ‘predict’ function much more now than extracting coefficients, so I should really update these posts to reflect that (see this one for at least some general use of it in action for a standard lm: https://tomhouslay.com/2015/06/02/understanding-3-way-interactions-between-continuous-and-categorical-variables-part-ii-2-cats-1-con/ )

Confidence interval wise, it’s a difficult subject because of uncertainty around the random effects etc. Check out the glmm wiki for more info (search for ‘confidence intervals’, a section about halfway down the page): http://glmm.wikidot.com/faq

When you say multivariate, I assume you mean that you have multiple predictors (rather than multiple responses)? Using the ‘predict’ function enables you to generate predictions based on user-set combinations of the fixed effects, averaging over the random effects (or for certain levels of the random effects, if that’s your interest). For your control variables, the easiest thing to do is mean-centre and scale them in your model (or, if they are binary, code them as -0.5 and 0.5 such that they have a mean of 0 and difference of 1, useful for comparing effect sizes), and then your dummy data frame can just have 0s for those variables – meaning your predictions are based on average values for all control factors.

Hopefully this helps – I do aim to write an updated post on predictions using lme4 at some point, so will comment again here once that’s up…