# markcrosscorr

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##### Mark Cross-Correlation Function

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

Keywords
spatial, nonparametric
##### Usage
markcrosscorr(X, r = NULL,
correction = c("isotropic", "Ripley", "translate"),
method = "density", …, normalise = TRUE, Xname = NULL)
##### Arguments
X

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

r

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.

##### 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 $$k_{mm}(r) = \frac{E_{0u}[M_i(0) M_j(u)]}{E[M_i,M_j]}$$ where $E_{0u}$ 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 $k_{mm}(r)$ 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 $k_{mm}(r) \equiv 1$.

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.

##### Value

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

markcorr

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

Documentation reproduced from package spatstat, version 1.61-0, License: GPL (>= 2)

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