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:
The residuals don't systematically covary with the predictor
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?
