Overlaid Scatter and Contour Plots

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.

Usage

contourPlot(
  x,
  y,
  m = 30L,
  xrange,
  yrange,
  xkernel,
  ykernel,
  nlevels = 9L,
  densityColors = heat.colors(nlevels),
  pointColors = "gray",
  ...
)

Arguments

x

numeric vector of the x data to be plotted

y

numeric vector of the y data to be plotted

m

integer value of the number of x and y grid points

xrange

numeric vector of length two indicating x-range of plot; defaults to range(x)

yrange

numeric vector of length two indicating y-range of plot; defaults to range(y)

xkernel

numeric indicating the standard deviation of Normal x kernel to use in generating contour plot

ykernel

numeric indicating the standard deviation of Normal y kernel to use in generating contour plot

nlevels

integer with the number of levels of the contour plot

densityColors

colors to use, specified as in par. Defaults to the heat.colors with nlevels. When specified, colors overrides nlevels.

pointColors

color for the plot points

...

additional arguments to be passed to a plot call that generates the scatter plot and the contour plot

Author

Yuqi Liao and Paul Bailey

Examples

if (FALSE) { # \dontrun{
sdf <- readNAEP(path=system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))
lm1 <- lm.sdf(formula=composite ~ pared * dsex + sdracem, data=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)
} # }