cdplot {graphics} | R Documentation

## 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, ...)

    ## Default S3 method:
    cdplot(x, y,
      plot = TRUE, tol.ylab = 0.05,
      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,
      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 either a single numerical variable.
`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.
`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 descretization (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.

### See Also

`spineplot`, `density`

### Examples

    ## 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))

    ## 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)

[Package graphics version 2.3.1 Index]