# Example 7.3: Simple jittered scatterplot with smoother for dichotomous outcomes with continuous predictors

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It’s useful to look at scatterplots even when the “y” variable is dichotomous. For example, this can help determine whether categorization or linear assumptions would be more plausible. However, an unmodified scatterplot is less than helpful, since all of the “y” values are either 0 or 1, and are hard to separate visually. Some jittering (section 5.2.4) is useful in that regard. In addition, it is often useful to plot a smoothed line through the data. We use the data generated in section 7.2 to demonstrate.**SAS and R**, 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.

**SAS**

In SAS, we add jitter, then plot the jittered values and the observed values on the same plot using the

`overlay`option. We display the jittered values as dots and add a smoothed line through the real (not jittered) data without displaying their values using

`symbol`statements (sections 5.2.2, 5.2.6).

data ds2; set test; yplot = ytest + uniform(0) * .2; run; symbol1 i = sm50s v = none c = black; symbol2 i = none v = dot c = black; proc gplot data = ds2; plot (ytest yplot) * xtest / overlay; run;And the resulting plot is:

**R**

In R, we display a scatterplot (section 5.1.1) of the jittered values against the covariate. The

`jitter()`function (section 5.2.4) is called within the

`plot()`function. We then add the smoothed line, based on the real (not jittered) data using the

`lines()`function (section 5.2.1), called with the appropriate

`lowess()`(section 5.2.6) object as input.

plot(xtest,jitter(ytest)) lines(lowess(xtest,ytest))

And the resulting plot is:

These plots are useful, but fairly unattractive. In our next example, we’ll make them prettier.

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