R: Create a Point Matching

pppmatching {spatstat.geom} | R Documentation

## Create a Point Matching

### Description

Creates an object of class `"pppmatching"` representing a matching of two planar point patterns (objects of class `"ppp"`).

### Usage

      pppmatching(X, Y, am, type = NULL, cutoff = NULL, q = NULL,
        mdist = NULL)

### Arguments

`X,Y` | Two point patterns (objects of class `"ppp"`).
`am` | An `npoints(X)` by `npoints(Y)` matrix with entries >= 0 that specifies which points are matched and with what weight; alternatively, an object that can be coerced to this form by `as.matrix`.
`type` | A character string giving the type of the matching. One of `"spa"`, `"ace"` or `"mat"`, or `NULL` for a generic or unknown matching.
`cutoff, q` | Numerical values specifying the cutoff value > 0 for interpoint distances and the order q in [0,Inf] of the average that is applied to them. `NULL` if not applicable or unknown.
`mdist` | Numerical value for the distance to be associated with the matching.

### Details

The argument `am` is interpreted as a "generalized adjacency matrix": if the `[i,j]`-th entry is positive, then the `i`-th point of `X` and the `j`-th point of `Y` are matched and the value of the entry gives the corresponding weight of the match. For an unweighted matching all the weights should be set to 1.

The remaining arguments are optional and allow to save additional information about the matching. See the help files for `pppdist` and `matchingdist` for details on the meaning of these parameters.

### Author(s)

Dominic Schuhmacher dominic.schuhmacher@stat.unibe.ch http://dominic.schuhmacher.name/

### See Also

`pppmatching.object` `matchingdist`

### Examples

      # a random unweighted complete matching
      X <- runifrect(10)
      Y <- runifrect(10)
      am <- r2dtable(1, rep(1,10), rep(1,10))[[1]]
            # generates a random permutation matrix
      m <- pppmatching(X, Y, am)
      summary(m)
      m$matrix
      plot(m)

      # a random weighted complete matching
      X <- runifrect(7)
      Y <- runifrect(7)
      am <- r2dtable(1, rep(10,7), rep(10,7))[[1]]/10
            # generates a random doubly stochastic matrix
      m2 <- pppmatching(X, Y, am)
      summary(m2)
      m2$matrix
      plot(m2)
      m3 <- pppmatching(X, Y, am, "ace")
      m4 <- pppmatching(X, Y, am, "mat")

[Package spatstat.geom version 2.2-0 Index]