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    <p>You need to create a vector of x-values in the domain of your plot and predict their corresponding y-values from your model. To do this, you need to inject this vector into a dataframe comprised of variables that match those in your model. You stated that you are OK with keeping the other variables fixed at their mean values, so I have used that approach in my solution. Whether or not the x-values you are predicting are actually <em>legal</em> given the other values in your plot should probably be something you consider when setting this up. </p> <p>Without sample data I can't be sure this will work exactly for you, so I apologize if there are any bugs below, but this should at least illustrate the approach.</p> <pre><code># Setup xmin = 0; xmax=10 # domain of your plot D = my.data plot( D$probCategorySame, D$posttestScore, xlim=c(xmin,xmax) ) lmMultiple &lt;- lm( posttestScore ~ pretestScore + probCategorySame + probDataRelated + practiceAccuracy + practiceNumTrials, data=D ) # create a dummy dataframe where all variables = their mean value for each record # except the variable we want to plot, which will vary incrementally over the # domain of the plot. We need this object to get the predicted values we # want to plot. N=1e4 means = colMeans(D) dummyDF = t(as.data.frame(means)) for(i in 2:N){dummyDF=rbind(dummyDF,means)} # There's probably a more elegant way to do this. xv=seq(xmin,xmax, length.out=N) dummyDF$probCSBinned = xv # if this gives you a warning about "Coercing LHS to list," use bracket syntax: #dummyDF[,k] = xv # where k is the column index of the variable `posttestScore` # Getting and plotting predictions over our dummy data. yv=predict(lmMultiple, newdata=subset(dummyDF, select=c(-posttestScore))) lines(xv, yv) </code></pre>
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    1. COThanks! The approach worked except I had to use "newdata" instead of "data" inside "predict".
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