EdSurvey (version 2.2.3)

contourPlot: Overlaid Scatter and Contour Plots

Description

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, ...)

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

…

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

Examples

# 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)
# }