lattice (version 0.2-3)

densityplot: Kernel Probability Density Plots

Description

Draw Kernel Density plots

Usage

densityplot(formula, n = 50, ...)

Arguments

formula

A formula of the form ~ x | g1 * g2 * ... indicating that density plots of x should be produced conditional on the levels of the variables g1,g2,.... x must be numeric, and each of

...

arguments to be passed down to the panel function, in turn to be passed to density (if the default panel function is used).

Value

• An object of class ``trellis'', plotted by default by print.trellis.

synopsis

densityplot(formula, aspect = "fill", as.table = FALSE, between = list(x = 0, y = 0), data, groups = NULL, key = NULL, layout, main = NULL, page = NULL, panel = panel.densityplot, par.strip.text, prepanel, scales, skip = FALSE, strip = strip.default, sub = NULL, subscripts = !missing(groups), subset, xlab, xlim, ylab, ylim, n = 50, ...)

Details

See the documentation for trellis.args for description of other valid arguments. The density estimate is actually calculated using the function density, and all arguments accepted by it can be passed (as ...) in the call to densityplot to control the output. See documentation of density for details. (Note: The default value of the argument n of density is changed to 50.)

See Also

trellis.args, density, panel.densityplot, panel.mathdensity,Lattice

Examples

data(singer)
densityplot( ~ height | voice.part, data = singer, layout = c(2, 4),  
            xlab = "Height (inches)", bw = 5)
## Using a predefined panel function to fit a normal distribution
densityplot( ~ height | voice.part, data = singer, layout = c(2, 4),  
            xlab = "Height (inches)",
            ylab = "Kernel Density/ Normal Fit",
            main = list("Estimated Density", cex = 2, col = "DarkOliveGreen"),
            panel = function(x, ...) {
                panel.xyplot(x = jitter(x),
                             y = rep(0, length(x)))
                panel.densityplot(x, ...)
                panel.mathdensity(dmath = dnorm,
                                  args = list(mean=mean(x),sd=sd(x)))
            } )