contourPlot {EdSurvey}  R Documentation 
Diagnostic plots for regressions can become too dense to interpret. This function helps by adding a contour plot over the points to allow the density of points to be seen, even when an area is entirely covered in points.
contourPlot(
x,
y,
m = 30L,
xrange,
yrange,
xkernel,
ykernel,
nlevels = 9L,
densityColors = heat.colors(nlevels),
pointColors = "gray",
...
)
x 
numeric vector of the 
y 
numeric vector of the 
m 
integer value of the number of 
xrange 
numeric vector of length two indicating 
yrange 
numeric vector of length two indicating 
xkernel 
numeric indicating the standard deviation of Normal

ykernel 
numeric indicating the standard deviation of Normal

nlevels 
integer with the number of levels of the contour plot 
densityColors 
colors to use, specified as in 
pointColors 
color for the plot points 
... 
additional arguments to be passed to a plot call that generates the scatter plot and the contour plot 
Yuqi Liao and Paul Bailey
## Not run:
sdf < readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))
lm1 < lm.sdf(composite ~ pared * dsex + sdracem, sdf)
# plot the results
contourPlot(x=lm1$fitted.values,
y=lm1$residuals[,1], # use only the first plausible value
m=30,
xlab="fitted values",
ylab="residuals",
main="Figure 1")
# add a line indicating where the residual is zero
abline(0,0)
## End(Not run)