vcd (version 1.4-4)

cd_plot: Conditional Density Plots

Description

Computes and plots conditional densities describing how the distribution of a categorical variable y changes over a numerical variable x.

Usage

cd_plot(x, …)
# S3 method for default
cd_plot(x, y,
  plot = TRUE, ylab_tol = 0.05,
  bw = "nrd0", n = 512, from = NULL, to = NULL,
  main = "", xlab = NULL, ylab = NULL, margins = c(5.1, 4.1, 4.1, 3.1),
  gp = gpar(), name = "cd_plot", newpage = TRUE, pop = TRUE, return_grob = FALSE, …)
# S3 method for formula
cd_plot(formula, data = list(),
  plot = TRUE, ylab_tol = 0.05,
  bw = "nrd0", n = 512, from = NULL, to = NULL,
  main = "", xlab = NULL, ylab = NULL, margins = c(5.1, 4.1, 4.1, 3.1),
  gp = gpar(), name = "cd_plot", newpage = TRUE, pop = TRUE, return_grob = FALSE, …)

Arguments

x

an object, the default method expects either a single numerical variable.

y

a "factor" interpreted to be the dependent variable

formula

a "formula" of type y ~ x with a single dependent "factor" and a single numerical explanatory variable.

data

an optional data frame.

plot

logical. Should the computed conditional densities be plotted?

ylab_tol

convenience tolerance parameter for y-axis annotation. If the distance between two labels drops under this threshold, they are plotted equidistantly.

bw, n, from, to, …

arguments passed to density

main, xlab, ylab

character strings for annotation

margins

margins when calling plotViewport

gp

a "gpar" object controlling the grid graphical parameters of the rectangles. It should specify in particular a vector of fill colors of the same length as levels(y). The default is to call gray.colors.

name

name of the plotting viewport.

newpage

logical. Should grid.newpage be called before plotting?

return_grob

logical. Should a snapshot of the display be returned as a grid grob?

pop

logical. Should the viewport created be popped?

Value

The conditional density functions (cumulative over the levels of y) are returned invisibly.

Details

cd_plot computes the conditional densities of x given the levels of y weighted by the marginal distribution of y. The densities are derived cumulatively over the levels of y.

This visualization technique is similar to spinograms (see spine) but they do not discretize the explanatory variable, but rather use a smoothing approach. Furthermore, the original x axis and not a distorted x axis (as for spinograms) is used. This typically results in conditional densities that are based on very few observations in the margins: hence, the estimates are less reliable there.

References

Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.

See Also

spine, density

Examples

# NOT RUN {
## Arthritis data
data("Arthritis")
cd_plot(Improved ~ Age, data = Arthritis)
cd_plot(Improved ~ Age, data = Arthritis, bw = 3)
cd_plot(Improved ~ Age, data = Arthritis, bw = "SJ")
## compare with spinogram
spine(Improved ~ Age, data = Arthritis, breaks = 3)

## Space shuttle data
data("SpaceShuttle")
cd_plot(Fail ~ Temperature, data = SpaceShuttle, bw = 2)

## scatter plot with conditional density
cdens <- cd_plot(Fail ~ Temperature, data = SpaceShuttle, bw = 2, plot = FALSE)
plot(I(-1 * (as.numeric(Fail) - 2)) ~ jitter(Temperature, factor = 2), data = SpaceShuttle,
  xlab = "Temperature", ylab = "Failure")
lines(53:81, cdens[[1]](53:81), col = 2)
# }