# cdplot

0th

Percentile

##### 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 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
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.

spineplot, density

##### Aliases
• cdplot
• cdplot.default
• cdplot.formula
##### Examples
library(graphics) ## 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 package graphics, version 3.3.2, License: Part of R 3.3.2

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