# Interaction plot from cell means

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I needed to produce a few a interaction plots for my book in R and, while the interaction.plot() function is useful it has a couple of drawbacks. First, the default output isn’t very pretty. Second, it works from the raw data, whereas I often need plots from cell means. For teaching purposes it is quite common to produce plots without raw data (for hypothetical data or from published examples).**Psychological Statistics**, 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.

My first attempts at the plots involved setting them up element by element. Just going over some examples I decided to turn the basic plot (for a 2 x 2 ANOVA) into a simple function. Nothing fancy, just a regular interaction plot in black and white that I think is prettier than the SPSS, Excel or R defaults. At some point I may have a go turning it into a general I x J ANOVA plot (or maybe even add CIs, but I’ll probably do that from raw data if I ever get round to it).

plot.2by2 <- function(A1B1,A1B2, A2B1, A2B2, group.names, legend = TRUE, leg.loc=NULL, factor.labels=c('Factor A', 'Factor B'), swap = FALSE, ylab= NULL, main = NULL){

group.means <- c(A1B1, A2B1, A1B2, A2B2)

if(missing(ylab)) ylab <- expression(italic(DV))

if(swap==TRUE) {

group.names <- list(group.names[[2]], group.names[[1]]) ; group.means <- c(A1B1, A1B2, A2B1, A2B2); factor.labels <- c(factor.labels[2], factor.labels[1])

}

plot(group.means, pch=NA, ylim=c(min(group.means)*.95, max(group.means)*1.025), xlim=c(0.8,2.2), ylab=ylab, xaxt=’n’, xlab=factor.labels[1], main=main)

points(group.means[1:2], pch = 21)

points(group.means[3:4], pch = 19)

axis(side = 1, at = c(1:2), labels = group.names[[1]])

lines(group.means[1:2], lwd = .6, lty = 2)

lines(group.means[3:4], lwd = .6)

if(missing(leg.loc)) leg.loc <- c(1,max(group.means))

if(legend ==TRUE) legend(leg.loc[1], leg.loc[2],legend = group.names[[2]], title = factor.labels[2], lty = c(3,1))

}

Call the function by entering the four cell means in conventional order: A1B1, A1B2 and so on where A1B1 is the mean of level 1 of factor A at level 1 of factor B. You also need a two item list containing text strings of the two level names of each factor. For instance:

lev.names <- list(c('A1', 'A2'), c('B1', 'B2'))

plot.2by2(5,15,10,20, lev.names)

You can swap the axes by adding the argument swap = TRUE:

plot.2by2(5,15,10,20, lev.names, swap = TRUE)

The default factor names are ‘Factor A’ and ‘Factor B’, but these are over-ridden in the call:

plot.2by2(5,15,10,20,lev.names, swap = TRUE, factor.labels= c(‘Factor 1′,’Factor 2’))

You can also change the

*y-*axis label with ylab or add a main title with main. The legend can be dropped (legend = FALSE) if you don’t want one or need it to be located outside the plot. To move the legend just specify coordinates with an argument such as leg.loc = c(1,10). You can also edit the source code directly. Here is an example with title and meaningful labels:

group.names <- list(c('placebo','drug'), c('male', 'female'))

As this just uses basic plotting functions in R you can also manipulate the plot in other ways: adding lines with segments(), adding text with text() changing graphical parameters with par() and so on. Depending on your platform it is also easy to extract the plot as a .pdf or .jpg file. On a mac I save it as a .pdf file and open it in preview which allows me to save it as .png, .gif or whatever I need.

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