graphics (version 3.6.0)

# cdplot: Conditional Density Plots

## Description

Computes and plots conditional densities describing how the conditional distribution of a categorical variable `y` changes over a numerical variable `x`.

## 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.

## Value

The conditional density functions (cumulative over the levels of `y`) are returned invisibly.

## 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.

## References

Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.

`spineplot`, `density`

## Examples

Run this code
``````# 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)
# }
``````

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