title: How can I superimpose modified loess lines on a ggplot2 qplot?
body: <h3>Background</h3>
  
  <p>Right now, I'm creating a multiple-predictor linear model and generating diagnostic plots to assess regression assumptions. (It's for a multiple regression analysis stats class that I'm loving at the moment :-)</p>
  
  <p>My textbook (Cohen, Cohen, West, and Aiken 2003) recommends plotting each predictor against the residuals to make sure that:</p>
  
  <ol>
  <li>The residuals don't systematically covary with the predictor</li>
  <li>The residuals are homoscedastic with respect to each predictor in the model</li>
  </ol>
  
  <p>On point (2), my textbook has this to say:</p>
  
  <blockquote>
    <p>Some statistical packages allow the analyst to plot lowess fit lines at the mean of the residuals (0-line), 1 standard deviation above the mean, and 1 standard deviation below the mean of the residuals....In the present case {their example}, the two lines {mean + 1sd and mean - 1sd} remain roughly parallel to the lowess {0} line, consistent with the interpretation that the variance of the residuals does not change as a function of X. (p. 131)</p>
  </blockquote>
  
  <h3>How can I modify loess lines?</h3>
  
  <p>I know how to generate a scatterplot with a "0-line,":</p>
  
  <pre><code>    # First, I'll make a simple linear model and get its diagnostic stats
      library(ggplot2)
      data(cars)
      mod &lt;- fortify(lm(speed ~ dist, data = cars))
      attach(mod)
      str(mod)
  
      # Now I want to make sure the residuals are homoscedastic
      qplot (x = dist, y = .resid, data = mod) + 
      geom_smooth(se = FALSE) # "se = FALSE" Removes the standard error bands
  </code></pre>
  
  <p>But does anyone know how I can use <code>ggplot2</code> and <code>qplot</code> to generate plots where the 0-line, "mean + 1sd" AND "mean - 1sd" lines would be superimposed? Is that a weird/complex question to be asking?</p>