cd_plot | R Documentation |

Computes and plots conditional densities describing how the
distribution of a categorical variable `y`

changes over a
numerical variable `x`

.

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, return_grob = FALSE, ...) ## 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, return_grob = FALSE, ...)

`x` |
an object, the default method expects either a single numerical variable. |

`y` |
a |

`formula` |
a |

`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 |

`main, xlab, ylab` |
character strings for annotation |

`margins` |
margins when calling |

`gp` |
a |

`name` |
name of the plotting viewport. |

`newpage` |
logical. Should |

`return_grob` |
logical. Should a snapshot of the display be returned as a grid grob? |

`pop` |
logical. Should the viewport created be popped? |

`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.

The conditional density functions (cumulative over the levels of `y`

)
are returned invisibly.

Achim Zeileis Achim.Zeileis@R-project.org

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conditional distributions*, Unpublished Manuscript.

`spine`

, `density`

## 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)

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