Iest: Estimate the I-function

Description

Estimates the summary function $I(r)$ for a multitype point pattern.

Usage

Iest(X, ..., eps=NULL, r=NULL, breaks=NULL, correction=NULL)

Arguments

The observed point pattern, from which an estimate of $I(r)$ will be computed. An object of class "ppp", or data in any format acceptable to as.ppp().

...

Ignored.

eps

the resolution of the discrete approximation to Euclidean distance (see below). There is a sensible default.

Optional. Numeric vector of values for the argument $r$ at which $I(r)$ should be evaluated. There is a sensible default. First-time users are strongly advised not to specify this argument. See below for important conditions on r

breaks

An alternative to the argument r. Not normally invoked by the user. See Details section.

correction

Optional. Vector of character strings specifying the edge correction(s) to be used by Jest.

Value

An object of class "fv", see fv.object, which can be plotted directly using plot.fv.
Essentially a data frame containing
rthe vector of values of the argument $r$ at which the function $I$ has been estimated
rsthe ``reduced sample'' or ``border correction'' estimator of $I(r)$ computed from the border-corrected estimates of $J$ functions
kmthe spatial Kaplan-Meier estimator of $I(r)$ computed from the Kaplan-Meier estimates of $J$ functions
hanthe Hanisch-style estimator of $I(r)$ computed from the Hanisch-style estimates of $J$ functions
unthe uncorrected estimate of $I(r)$ computed from the uncorrected estimates of $J$
theothe theoretical value of $I(r)$ for a stationary Poisson process: identically equal to $0$

Details

The $I$ function summarises the dependence between types in a multitype point process (Van Lieshout and Baddeley, 1999) It is based on the concept of the $J$ function for an unmarked point process (Van Lieshout and Baddeley, 1996). See Jest for information about the $J$ function. The $I$ function is defined as $$I(r) = \sum_{i=1}^m p_i J_{ii}(r) - J_{\bullet\bullet}(r)$$ where $J_{\bullet\bullet}$ is the $J$ function for the entire point process ignoring the marks, while $J_{ii}$ is the $J$ function for the process consisting of points of type $i$ only, and $p_i$ is the proportion of points which are of type $i$.

The $I$ function is designed to measure dependence between points of different types, even if the points are not Poisson. Let $X$ be a stationary multitype point process, and write $X_i$ for the process of points of type $i$. If the processes $X_i$ are independent of each other, then the $I$-function is identically equal to $0$. Deviations $I(r) < 1$ or $I(r) > 1$ typically indicate negative and positive association, respectively, between types. See Van Lieshout and Baddeley (1999) for further information.

An estimate of $I$ derived from a multitype spatial point pattern dataset can be used in exploratory data analysis and formal inference about the pattern. The estimate of $I(r)$ is compared against the constant function $0$. Deviations $I(r) < 1$ or $I(r) > 1$ may suggest negative and positive association, respectively.

This algorithm estimates the $I$-function from the multitype point pattern X. It assumes that X can be treated as a realisation of a stationary (spatially homogeneous) random spatial marked point process in the plane, observed through a bounded window.

The argument X is interpreted as a point pattern object (of class "ppp", see ppp.object) and can be supplied in any of the formats recognised by as.ppp(). It must be a multitype point pattern (it must have a marks vector which is a factor).

The function Jest is called to compute estimates of the $J$ functions in the formula above. In fact three different estimates are computed using different edge corrections. See Jest for information.

References

Van Lieshout, M.N.M. and Baddeley, A.J. (1996) A nonparametric measure of spatial interaction in point patterns. Statistica Neerlandica 50, 344--361.

Van Lieshout, M.N.M. and Baddeley, A.J. (1999) Indices of dependence between types in multivariate point patterns. Scandinavian Journal of Statistics 26, 511--532.

Examples

Run this code

data(amacrine)
   Ic <- Iest(amacrine)
   plot(Ic, main="Amacrine Cells data")
   # values are below I= 0, suggesting negative association
   # between 'on' and 'off' cells.

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