Computes and plots conditional densities describing how the conditional distribution of a categorical variable
y changes over a numerical variable
cdplot(x, ...) ## S3 method for class 'default': 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))
- an object, the default method expects a single numerical variable (or an object coercible to this).
"factor"interpreted to be the dependent variable
y ~ xwith a single dependent
"factor"and a single numerical explanatory variable.
- an optional data frame.
- logical. Should the computed conditional densities be plotted?
- convenience tolerance parameter for y-axis annotation. If the distance between two labels drops under this threshold, they are plotted equidistantly.
- 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
- a vector of fill colors of the same length as
levels(y). The default is to call
- border color of shaded polygons.
- main, xlab, ylab
- character strings for annotation
- character vector for annotation of y axis, defaults to
- xlim, ylim
- the range of x and y values with sensible defaults.
- an optional vector specifying a subset of observations to be used for plotting.
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
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.
The conditional density functions (cumulative over the levels of
y) are returned invisibly.
Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.
## 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[](53:81), col = 2)
Documentation reproduced from R 2.15.3. License: GPL-2.