Background

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 :-)

My textbook (Cohen, Cohen, West, and Aiken 2003) recommends plotting each predictor against the residuals to make sure that:

1. The residuals don't systematically covary with the predictor
2. The residuals are homoscedastic with respect to each predictor in the model

On point (2), my textbook has this to say:

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)

How can I modify loess lines?

I know how to generate a scatterplot with a "0-line,":

    # First, I'll make a simple linear model and get its diagnostic stats
    library(ggplot2)
    data(cars)
    mod <- 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

But does anyone know how I can use ggplot2 and qplot 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?