Diagonal Density Panels

Plots univariate density estimates estimates to be used in a lattice::splom() call with the diag.panel argument.

diag.panel.splom.density(x, bw = "nrd0", adjust = 1, kernel = "gaussian",
  weights = NULL, n = 512, ...)

Arguments

x	data vector corresponding to that row / column (which will be the same for diagonal 'panels').
bw	the smoothing bandwidth to be used. The kernels are scaled such that this is the standard deviation of the smoothing kernel. (Note this differs from the reference books cited below, and from S-PLUS.) bw can also be a character string giving a rule to choose the bandwidth. See bw.nrd. The default, "nrd0", has remained the default for historical and compatibility reasons, rather than as a general recommendation, where e.g., "SJ" would rather fit, see also Venables and Ripley (2002). The specified (or computed) value of bw is multiplied by adjust.
adjust	the bandwidth used is actually adjust*bw. This makes it easy to specify values like ‘half the default’ bandwidth.
kernel	a character string giving the smoothing kernel to be used. This must partially match one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine", with default "gaussian", and may be abbreviated to a unique prefix (single letter). "cosine" is smoother than "optcosine", which is the usual ‘cosine’ kernel in the literature and almost MSE-efficient. However, "cosine" is the version used by S.
weights	numeric vector of non-negative observation weights, hence of same length as x. The default NULL is equivalent to weights = rep(1/nx, nx) where nx is the length of (the finite entries of) x[].
n	the number of equally spaced points at which the density is to be estimated. When n > 512, it is rounded up to a power of 2 during the calculations (as fft is used) and the final result is interpolated by approx. So it almost always makes sense to specify n as a power of two.
...	Further arguments passed on to lattice::diag.panel.splom() and lattice::panel.lines().

See also

lattice::diag.panel.splom(), lattice::splom(), stats::density().

Examples

splom(~ iris[1:4],
      data = iris,
      diag.panel = diag.panel.splom.density,
      pscales = 0)