cd_plot {vcd} | R Documentation

## Conditional Density Plots

### Description

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

### Usage

    cd_plot(x, ...)
    ## Default S3 method:
    cd_plot(x, y,
      plot = TRUE, ylab_tol = 0.05,
      bw = "nrd0", n = 512, from = NULL, to = NULL,
      main = "", xlab = NULL, ylab = NULL, margins = c(5.1, 4.1, 4.1, 3.1),
      gp = gpar(), name = "cd_plot", newpage = TRUE, pop = TRUE,
      ...)
    ## S3 method for class 'formula':
    cd_plot(formula, data = list(),
      plot = TRUE, ylab_tol = 0.05,
      bw = "nrd0", n = 512, from = NULL, to = NULL,
      main = "", xlab = NULL, ylab = NULL, margins = c(5.1, 4.1, 4.1, 3.1),
      gp = gpar(), name = "cd_plot", newpage = TRUE, pop = TRUE,
      ...)

### 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?
`ylab_tol` | 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`
`main, xlab, ylab` | character strings for annotation
`margins` | margins when calling `plotViewport`
`gp` | a `"gpar"` object controlling the grid graphical parameters of the rectangles. It should specify in particular a vector of `fill` colors of the same length as `levels(y)`. The default is to call `gray.colors`.
`name` | name of the plotting viewport.
`newpage` | logical. Should `grid.newpage` be called before plotting?
`pop` | logical. Should the viewport created be popped?

### Details

`cd_plot` 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 `spine`) but they do not discretize the explanatory variable, but rather use a smoothing approach. Furthermore, the original x axis and not a distorted x axis (as for spinograms) is used. This typically results in conditional densities that are based on very few observations in the margins: hence, the estimates are less reliable there.

### 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

`spine`, `density`

### Examples

    ## Arthritis data
    data("Arthritis")
    cd_plot(Improved ~ Age, data = Arthritis)
    cd_plot(Improved ~ Age, data = Arthritis, bw = 3)
    cd_plot(Improved ~ Age, data = Arthritis, bw = "SJ")
    ## compare with spinogram
    spine(Improved ~ Age, data = Arthritis, breaks = 3)

    ## Space shuttle data
    data("SpaceShuttle")
    cd_plot(Fail ~ Temperature, data = SpaceShuttle, bw = 2)

    ## scatter plot with conditional density
    cdens <- cd_plot(Fail ~ Temperature, data = SpaceShuttle, bw = 2, plot = FALSE)
    plot(I(-1 * (as.numeric(Fail) - 2)) ~ jitter(Temperature, factor = 2), data = SpaceShuttle,
      xlab = "Temperature", ylab = "Failure")
    lines(53:81, cdens[[1]](53:81), col = 2)

[Package vcd version 0.9-6 Index]