# 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```cdplot(x, ...) ## Default S3 method: 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 class '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.

## Author(s)

Achim Zeileis Achim.Zeileis@R-project.org

## References

Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.

`spineplot`, `density`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```## 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) ```