R: Pair Correlation Function of Point Pattern

pcf.ppp {spatstat.core} | R Documentation

## Pair Correlation Function of Point Pattern

### Description

Estimates the pair correlation function of a point pattern using kernel methods.

### Usage

      ## S3 method for class 'ppp'
    pcf(X, ..., r = NULL, kernel="epanechnikov", bw=NULL,
                        stoyan=0.15,
                        correction=c("translate", "Ripley"),
                        divisor = c("r", "d"),
                        var.approx = FALSE,
                        domain=NULL,
                        ratio=FALSE, close=NULL)

### Arguments

`X` | A point pattern (object of class `"ppp"`).
`r` | Vector of values for the argument r at which g(r) should be evaluated. There is a sensible default.
`kernel` | Choice of smoothing kernel, passed to `density.default`.
`bw` | Bandwidth for smoothing kernel, passed to `density.default`. Either a single numeric value giving the standard deviation of the kernel, or a character string specifying a bandwidth selection rule recognised by `density.default`. If `bw` is missing or `NULL`, the default value is computed using Stoyan's rule of thumb: see Details.
`...` | Other arguments passed to the kernel density estimation function `density.default`.
`stoyan` | Coefficient for Stoyan's bandwidth selection rule; see Details.
`correction` | Edge correction. A character vector specifying the choice (or choices) of edge correction. See Details.
`divisor` | Choice of divisor in the estimation formula: either `"r"` (the default) or `"d"`. See Details.
`var.approx` | Logical value indicating whether to compute an analytic approximation to the variance of the estimated pair correlation.
`domain` | Optional. Calculations will be restricted to this subset of the window. See Details.
`ratio` | Logical. If `TRUE`, the numerator and denominator of each edge-corrected estimate will also be saved, for use in analysing replicated point patterns.
`close` | Advanced use only. Precomputed data. See section on Advanced Use.

### Details

The pair correlation function g(r) is a summary of the dependence between points in a spatial point process. The best intuitive interpretation is the following: the probability p(r) of finding two points at locations x and y separated by a distance r is equal to

p(r) = lambda^2 * g(r) dx dy

where lambda is the intensity of the point process. For a completely random (uniform Poisson) process, p(r) = lambda^2 dx dy so g(r) = 1. Formally, the pair correlation function of a stationary point process is defined by

g(r) = K'(r)/ ( 2 * pi * r)

where K'(r) is the derivative of K(r), the reduced second moment function (aka “Ripley's K function”) of the point process. See `Kest` for information about K(r).

For a stationary Poisson process, the pair correlation function is identically equal to 1. Values g(r) < 1 suggest inhibition between points; values greater than 1 suggest clustering.

This routine computes an estimate of g(r) by kernel smoothing.

- If `divisor="r"` (the default), then the standard kernel estimator (Stoyan and Stoyan, 1994, pages 284–285) is used. By default, the recommendations of Stoyan and Stoyan (1994) are followed exactly.

- If `divisor="d"` then a modified estimator is used: the contribution from an interpoint distance d[ij] to the estimate of g(r) is divided by d[ij] instead of dividing by r. This usually improves the bias of the estimator when r is close to zero.

There is also a choice of spatial edge corrections (which are needed to avoid bias due to edge effects associated with the boundary of the spatial window):

- If `correction="translate"` or `correction="translation"` then the translation correction is used. For `divisor="r"` the translation-corrected estimate is given in equation (15.15), page 284 of Stoyan and Stoyan (1994).

- If `correction="Ripley"` or `correction="isotropic"` then Ripley's isotropic edge correction is used. For `divisor="r"` the isotropic-corrected estimate is given in equation (15.18), page 285 of Stoyan and Stoyan (1994).

- If `correction="none"` then no edge correction is used, that is, an uncorrected estimate is computed.

Multiple corrections can be selected. The default is `correction=c("translate", "Ripley")`.

Alternatively `correction="all"` selects all options; `correction="best"` selects the option which has the best statistical performance; `correction="good"` selects the option which is the best compromise between statistical performance and speed of computation.

The choice of smoothing kernel is controlled by the argument `kernel` which is passed to `density.default`. The default is the Epanechnikov kernel, recommended by Stoyan and Stoyan (1994, page 285).

The bandwidth of the smoothing kernel can be controlled by the argument `bw`. Bandwidth is defined as the standard deviation of the kernel; see the documentation for `density.default`. For the Epanechnikov kernel with half-width `h`, the argument `bw` is equivalent to h/sqrt(5).

Stoyan and Stoyan (1994, page 285) recommend using the Epanechnikov kernel with support [-h,h] chosen by the rule of thumn h = c/sqrt(lambda), where lambda is the (estimated) intensity of the point process, and c is a constant in the range from 0.1 to 0.2. See equation (15.16). If `bw` is missing or `NULL`, then this rule of thumb will be applied. The argument `stoyan` determines the value of c. The smoothing bandwidth that was used in the calculation is returned as an attribute of the final result.

The argument `r` is the vector of values for the distance r at which g(r) should be evaluated. There is a sensible default. If it is specified, `r` must be a vector of increasing numbers starting from `r[1] = 0`, and `max(r)` must not exceed half the diameter of the window.

If the argument `domain` is given, estimation will be restricted to this region. That is, the estimate of g(r) will be based on pairs of points in which the first point lies inside `domain` and the second point is unrestricted. The argument `domain` should be a window (object of class `"owin"`) or something acceptable to `as.owin`. It must be a subset of the window of the point pattern `X`.

To compute a confidence band for the true value of the pair correlation function, use `lohboot`.

If `var.approx = TRUE`, the variance of the estimate of the pair correlation will also be calculated using an analytic approximation (Illian et al, 2008, page 234) which is valid for stationary point processes which are not too clustered. This calculation is not yet implemented when the argument `domain` is given.

### Value

A function value table (object of class `"fv"`). Essentially a data frame containing the variables

`r` | the vector of values of the argument r at which the pair correlation function g(r) has been estimated
`theo` | vector of values equal to 1, the theoretical value of g(r) for the Poisson process
`trans` | vector of values of g(r) estimated by translation correction
`iso` | vector of values of g(r) estimated by Ripley isotropic correction
`v` | vector of approximate values of the variance of the estimate of g(r)

as required.

If `ratio=TRUE` then the return value also has two attributes called `"numerator"` and `"denominator"` which are `"fv"` objects containing the numerators and denominators of each estimate of g(r).

The return value also has an attribute `"bw"` giving the smoothing bandwidth that was used.

### Advanced Use

To perform the same computation using several different bandwidths `bw`, it is efficient to use the argument `close`. This should be the result of `closepairs(X, rmax)` for a suitably large value of `rmax`, namely `rmax >= max(r) + 3 * bw`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk and Martin Hazelton.

### References

Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008) Statistical Analysis and Modelling of Spatial Point Patterns. Wiley.

Stoyan, D. and Stoyan, H. (1994) Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.

### See Also

`Kest`, `pcf`, `density.default`, `bw.stoyan`, `bw.pcf`, `lohboot`.

### Examples

      X <- simdat

      p <- pcf(X)
      plot(p, main="pair correlation function for X")
      # indicates inhibition at distances r < 0.3

      pd <- pcf(X, divisor="d")

      # compare estimates
      plot(p, cbind(iso, theo) ~ r, col=c("blue", "red"),
             ylim.covers=0, main="", lwd=c(2,1), lty=c(1,3), legend=FALSE)
      plot(pd, iso ~ r, col="green", lwd=2, add=TRUE)
      legend("center", col=c("blue", "green"), lty=1, lwd=2,
             legend=c("divisor=r","divisor=d"))

      # calculate approximate variance and show POINTWISE confidence bands
      pv <- pcf(X, var.approx=TRUE)
      plot(pv, cbind(iso, iso+2*sqrt(v), iso-2*sqrt(v)) ~ r)

[Package spatstat.core version 2.0-0 Index]