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cdplot: Conditional Density Plots

cdplot

R Documentation

Conditional Density Plots

Description

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

Usage

cdplot(x, ...)

## Default S3 method:
cdplot(x, y,
  plot = TRUE, tol.ylab = 0.05, ylevels = NULL,
  bw = "nrd0", n = 512, from = NULL, to = NULL,
  col = NULL, border = 1, main = "", xlab = NULL, ylab = NULL,
  yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), ...)

## S3 method for class 'formula'
cdplot(formula, data = list(),
  plot = TRUE, tol.ylab = 0.05, ylevels = NULL,
  bw = "nrd0", n = 512, from = NULL, to = NULL,
  col = NULL, border = 1, main = "", xlab = NULL, ylab = NULL,
  yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), ...,
  subset = NULL)

Arguments

`x`	an object, the default method expects a single numerical variable (or an object coercible to this).
`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?
`tol.ylab`	convenience tolerance parameter for y-axis annotation. If the distance between two labels drops under this threshold, they are plotted equidistantly.
`ylevels`	a character or numeric vector specifying in which order the levels of the dependent variable should be plotted.
`bw, n, from, to, ...`	arguments passed to `density`
`col`	a vector of fill colors of the same length as `levels(y)`. The default is to call `gray.colors`.
`border`	border color of shaded polygons.
`main, xlab, ylab`	character strings for annotation
`yaxlabels`	character vector for annotation of y axis, defaults to `levels(y)`.
`xlim, ylim`	the range of x and y values with sensible defaults.
`subset`	an optional vector specifying a subset of observations to be used for plotting.

Details

cdplot 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 spineplot) and plots P(y | x) against x. The conditional probabilities are not derived by discretization (as in the spinogram), but using a smoothing approach via density.

Note, that the estimates of the conditional densities are more reliable for high-density regions of x. Conversely, the are less reliable in regions with only few x observations.

Value

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

Author(s)

Achim Zeileis Achim.Zeileis@R-project.org

References

Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.

Examples

## NASA space shuttle o-ring failures
fail <- factor(c(2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1,
                 1, 2, 1, 1, 1, 1, 1),
               levels = 1:2, labels = c("no", "yes"))
temperature <- c(53, 57, 58, 63, 66, 67, 67, 67, 68, 69, 70, 70,
                 70, 70, 72, 73, 75, 75, 76, 76, 78, 79, 81)

## CD plot
cdplot(fail ~ temperature)
cdplot(fail ~ temperature, bw = 2)
cdplot(fail ~ temperature, bw = "SJ")

## compare with spinogram
(spineplot(fail ~ temperature, breaks = 3))

## highlighting for failures
cdplot(fail ~ temperature, ylevels = 2:1)

## scatter plot with conditional density
cdens <- cdplot(fail ~ temperature, plot = FALSE)
plot(I(as.numeric(fail) - 1) ~ jitter(temperature, factor = 2),
     xlab = "Temperature", ylab = "Conditional failure probability")
lines(53:81, 1 - cdens[[1]](53:81), col = 2)