cdplot
Conditional Density Plots
Computes and plots conditional densities describing how the
conditional distribution of a categorical variable y changes over a
numerical variable x.
- Keywords
- hplot
Usage
cdplot(x, …)# S3 method for 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 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 typey ~ xwith 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 callgray.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.
References
Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.
See Also
Examples
library(graphics)
# NOT RUN {
## 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)
# }