markcrosscorr: Mark Cross-Correlation Function

Description

Given a spatial point pattern with several columns of marks, this function computes the mark correlation function between each pair of columns of marks.

Usage

markcrosscorr(X, r = NULL,
                correction = c("isotropic", "Ripley", "translate"),
                method = "density", …, normalise = TRUE, Xname = NULL)

Arguments

The observed point pattern. An object of class "ppp" or something acceptable to as.ppp.

Optional. Numeric vector. The values of the argument $r$ at which the mark correlation function $k_{f} (r)$ should be evaluated. There is a sensible default.

correction

A character vector containing any selection of the options "isotropic", "Ripley", "translate", "translation", "none" or "best". It specifies the edge correction(s) to be applied. Alternatively correction="all" selects all options.

method

A character vector indicating the user's choice of density estimation technique to be used. Options are "density", "loess", "sm" and "smrep".

…

Arguments passed to the density estimation routine (density, loess or sm.density) selected by method.

normalise

If normalise=FALSE, compute only the numerator of the expression for the mark correlation.

Xname

Optional character string name for the dataset X.

Value

A function array (object of class "fasp") containing the mark cross-correlation functions for each possible pair of columns of marks.

Details

First, all columns of marks are converted to numerical values. A factor with $m$ possible levels is converted to $m$ columns of dummy (indicator) values.

Next, each pair of columns is considered, and the mark cross-correlation is defined as $k_{m m} (r) = \frac{E_{0 u} [M_{i} (0) M_{j} (u)]}{E [M_{i}, M_{j}]}$ where $E_{0 u}$ denotes the conditional expectation given that there are points of the process at the locations $0$ and $u$ separated by a distance $r$ . On the numerator, $M_{i} (0)$ and $M_{j} (u)$ are the marks attached to locations $0$ and $u$ respectively in the $i$ th and $j$ th columns of marks respectively. On the denominator, $M_{i}$ and $M_{j}$ are independent random values drawn from the $i$ th and $j$ th columns of marks, respectively, and $E$ is the usual expectation.

Note that $k_{m m} (r)$ is not a ``correlation'' in the usual statistical sense. It can take any nonnegative real value. The value 1 suggests ``lack of correlation'': if the marks attached to the points of X are independent and identically distributed, then $k_{m m} (r) \equiv 1$ .

The argument X must be a point pattern (object of class "ppp") or any data that are acceptable to as.ppp. It must be a marked point pattern.

The cross-correlations are estimated in the same manner as for markcorr.

Examples

Run this code

# NOT RUN {
  # The dataset 'betacells' has two columns of marks:
  #       'type' (factor)
  #       'area' (numeric)
  if(interactive()) plot(betacells)
  plot(markcrosscorr(betacells))
# }

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