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I did a regression analysis with the following variables: Predictor = dummy variable, dependent Variable = metric, moderator variable = metric. I now want to show my results in a figure. The interaction should be shown by three regression lines. One for moderator = Mean (0), one for moderator = -1 SD, and one for moderator = + 1 SD.

How can I do this in R?

gung - Reinstate Monica
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Stefanie Brimmers
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  • possible duplicate of How can one plot continuous by continuous interactions in ggplot2? – smillig Jan 28 '15 at 15:51
  • For future reference, be aware that questions only asking for code, are off-topic here (but can be on-topic on [SO] w/ a reproducible example for people to work with). – gung - Reinstate Monica Jan 28 '15 at 17:07
  • @smillig, I think the question merits discussion of how best to plot data like that (see my answer below). I think that aspect makes it on-topic & sufficiently distinct from the linked thread. – gung - Reinstate Monica Jan 28 '15 at 17:08
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    @smillig, it is phrased as 'how... in R?', but my opinion has always been that the determination should be based on what the OP needs to understand. If the required concepts are strictly about programming / R-usage, it's off-topic, but if they're statistical (etc) ideas, it's on-topic, no matter how the Q is phrased. (NB, this is my opinion; others seem to disagree.) B/c what the OP needs to understand here are principles of data vis (& that are distinct from what is explained in the linked thread), I think this can stay here / open. Of course, you are not obliged to agree w/ me. – gung - Reinstate Monica Jan 28 '15 at 17:33
  • Regarding whether the study is experimental in nature, that is orthogonal to both the data vis principles & the coding required. Instead of control vs experimental, it could just as well be male vs female, & everything else would be the same. – gung - Reinstate Monica Jan 28 '15 at 17:35

1 Answers1

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Your suggestion would not display most of the information you have. Instead, display two lines: one for the control group and one for the experimental group. Then you can see all of the information you have, rather than just six points / bars. To see how much more informative the former can be, let's make some pretend data: 

set.seed(4255)                          # this makes the example exactly reproducible

predictor = rep(c("C", "E"), each=50)   # here I'm generating the data & fitting the 
moderator = rnorm(100, mean=20, sd=12)  #  model
dependent = 13 + 5*I(predictor=="E") + 1*moderator + 
            -1*I(predictor=="E")*moderator + rnorm(100, mean=0, sd=4)
model     = lm(dependent~predictor+moderator+predictor:moderator)
summary(model)
# ...
# Coefficients:
#                      Estimate Std. Error t value Pr(>|t|)    
# (Intercept)           13.2640     1.5817   8.386  4.3e-13 ***
# predictorE             2.9518     1.9279   1.531    0.129    
# moderator              0.9869     0.0680  14.513  < 2e-16 ***
# predictorE:moderator  -0.9512     0.0866 -10.983  < 2e-16 ***
# ...

windows()            # this plots the data w/ different symbols & colors
  plot(  moderator[predictor=="C"], dependent[predictor=="C"], 
         col="gray40", xlim=c(-15,50), ylim=c(5,60), xlab="moderator",
         ylab="predictor")
  points(moderator[predictor=="E"], dependent[predictor=="E"], 
         col="cornflowerblue", pch=2)
    # below I calculate the predicted values & plot them as lines over the data
  new.df = data.frame(moderator=-15:50, predictor="C")  
  lines(-15:50, predict(model, new.df))
  new.df = data.frame(moderator=-15:50, predictor="E")
  lines(-15:50, predict(model, new.df), col="darkblue", lty=2)
  legend("topleft", legend=c("C", "E"), col=c("black", "darkblue"), pch=1:2, lty=1:2)

enter image description here

Now let's make a barplot: 

mm     = mean(moderator)  # [1] 19.20544
sdm    = sd(moderator)    # [1] 10.67953
new.df = data.frame(moderator=rep(c(mm-sdm, mm, mm+sdm), times=2), 
                    predictor=rep(c("C","E"), each=3)             )
tab = matrix(predict(model, new.df), ncol=2)
colnames(tab) = c("C","E")

windows()
  barplot(tab, beside=T, col=c("gray25","gray50","gray75"))
  legend("topright", legend=c("-1 SD", "mean", "+1 SD"), pch=15,
         col=c("gray25","gray50","gray75"))

enter image description here

gung - Reinstate Monica
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