# 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 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.

##### 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)
# }
```

*Documentation reproduced from package graphics, version 3.4.3, License: Part of R 3.4.3*