smacpod: spdensity – R documentation

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spdensity

Kernel smoothed spatial density of point pattern

Description

spdensity computes a kernel smoothed spatial density function from a point pattern. This function is basically a wrapper for density.ppp. The density.ppp function computes the spatial intensity of a point pattern; the spdensity function scales the intensity to produce a true spatial density.

Usage

spdensity(
  x,
  sigma = NULL,
  ...,
  weights = NULL,
  edge = TRUE,
  varcov = NULL,
  at = "pixels",
  leaveoneout = TRUE,
  adjust = 1,
  diggle = FALSE,
  kernel = "gaussian",
  scalekernel = is.character(kernel),
  positive = FALSE,
  verbose = TRUE
)

Arguments

`x`	Point pattern (object of class `"ppp"`).
`sigma`	Standard deviation of isotropic smoothing kernel. Either a numerical value, or a function that computes an appropriate value of `sigma`.
`...`	Additional arguments passed to `pixellate.ppp` and `as.mask` to determine the pixel resolution, or passed to `sigma` if it is a function.
`weights`	Optional weights to be attached to the points. A numeric vector, numeric matrix, an `expression`, or a pixel image.
`edge`	Logical value indicating whether to apply edge correction.
`varcov`	Variance-covariance matrix of anisotropic smoothing kernel. Incompatible with `sigma`.
`at`	String specifying whether to compute the intensity values at a grid of pixel locations (`at="pixels"`) or only at the points of `x` (`at="points"`).
`leaveoneout`	Logical value indicating whether to compute a leave-one-out estimator. Applicable only when `at="points"`.
`adjust`	Optional. Adjustment factor for the smoothing parameter.
`diggle`	Logical. If `TRUE`, use the Jones-Diggle improved edge correction, which is more accurate but slower to compute than the default correction.
`kernel`	The smoothing kernel. A character string specifying the smoothing kernel (current options are `"gaussian"`, `"epanechnikov"`, `"quartic"` or `"disc"`), or a pixel image (object of class `"im"`) containing values of the kernel, or a `function(x,y)` which yields values of the kernel.
`scalekernel`	Logical value. If `scalekernel=TRUE`, then the kernel will be rescaled to the bandwidth determined by `sigma` and `varcov`: this is the default behaviour when `kernel` is a character string. If `scalekernel=FALSE`, then `sigma` and `varcov` will be ignored: this is the default behaviour when `kernel` is a function or a pixel image.
`positive`	Logical value indicating whether to force all density values to be positive numbers. Default is `FALSE`.
`verbose`	Logical value indicating whether to issue warnings about numerical problems and conditions.

Value

This function produces the spatial density of x as an object of class im from the spatstat.core package.

Author(s)

Joshua French

References

Waller, L.A. and Gotway, C.A. (2005). Applied Spatial Statistics for Public Health Data. Hoboken, NJ: Wiley.

Examples

data(grave)
contour(spdensity(grave))

smacpod

Statistical Methods for the Analysis of Case-Control Point Data

v2.1.1

GPL (>= 2)

Authors

Joshua French [aut, cre] (<https://orcid.org/0000-0002-9708-3353>)

Initial release