sm.density | R Documentation |

This function creates a density estimate from data in one, two or three dimensions. In two dimensions a variety of graphical displays can be selected, and in three dimensions a contour surface can be plotted. A number of other features of the construction of the estimate, and of its display, can be controlled.

If the `rpanel`

package is available, an interactive panel can be
activated to control various features of the plot.

If the `rgl`

package is also available, rotatable plots are
available for the two- and three-dimensional cases. (For
three-dimensional data, the `misc3d`

package is also required.)

sm.density(x, h, model = "none", weights = NA, group=NA, ...)

`x` |
a vector, or a matrix with two or three columns, containing the data. |

`h` |
a vector of length one, two or three, defining the smoothing parameter.
A normal kernel function is used and |

`model` |
This argument applies only with one-dimensional data. Its default value
is |

`weights` |
a vector of integers representing frequencies of individual observations.
Use of this parameter is incompatible with binning; hence |

`group` |
a vector of groups indicators (numeric or character values) or a factor. |

`...` |
other optional parameters are passed to the |

see Chapters 1, 2 and 6 of the reference below.
In the three-dimensional case, the contours of the density estimate are
constructed by the `contour3d`

function in the `misc3d`

package of Feng & Tierney.

a list containing the values of the density estimate at the evaluation points,
the smoothing parameter, the smoothing parameter weights and the kernel
weights. For one- and two-dimensional data, the standard error of the estimate
(on the square root scale, where the standard error is approximately constant)
and the upper and lower ends of a variability band are also supplied. Less
information is supplied when the smoothing parameter weights
or kernel weights are not all 1, or when `positive`

is set to `TRUE`

.

a plot is produced, unless the option `display="none"`

is set.

Bowman, A.W. and Azzalini, A. (1997).
*Applied Smoothing Techniques for Data Analysis: *
*the Kernel Approach with S-Plus Illustrations.*
Oxford University Press, Oxford.

`h.select`

, `hnorm`

, `hsj`

, `hcv`

,
`nise`

, `nmise`

, `sm`

,
`sm.sphere`

, `sm.regression`

,
`sm.options`

# A one-dimensional example y <- rnorm(50) sm.density(y, model = "Normal") # sm.density(y, panel = TRUE) # A two-dimensional example y <- cbind(rnorm(50), rnorm(50)) sm.density(y, display = "image") # sm.density(y, panel = TRUE) # A three-dimensional example # y <- cbind(rnorm(50), rnorm(50), rnorm(50)) # sm.density(y)

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