| cdplot {graphics} | R Documentation |
Computes and plots conditional densities describing how the
conditional distribution of a categorical variable y changes over a
numerical variable x.
cdplot(x, ...) ## Default S3 method: cdplot(x, y, plot = TRUE, tol.ylab = 0.05, 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, 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)
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? |
tol.ylab |
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 |
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. |
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 descretization (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.
Achim Zeileis Achim.Zeileis@R-project.org
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))
## 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)