R: Classify Points in a Point Pattern on a Network

cut.lpp {spatstat.linnet} | R Documentation

## Classify Points in a Point Pattern on a Network

### Description

For a point pattern on a linear network, classify the points into distinct types according to the numerical marks in the pattern, or according to another variable.

### Usage

      ## S3 method for class 'lpp'
    cut(x, z=marks(x), ...)

### Arguments

`x` | A point pattern on a linear network (object of class `"lpp"`).
`z` | Data determining the classification. A numeric vector, a factor, a pixel image on a linear network (class `"linim"`), a function on a linear network (class `"linfun"`), a tessellation on a linear network (class `"lintess"`), a string giving the name of a column of marks, or one of the coordinate names `"x"`, `"y"`, `"seg"` or `"tp"`.
`...` | Arguments passed to `cut.default`. They determine the breakpoints for the mapping from numerical values in `z` to factor values in the output. See `cut.default`.

### Details

This function has the effect of classifying each point in the point pattern `x` into one of several possible types. The classification is based on the dataset `z`, which may be either

- a factor (of length equal to the number of points in `z`) determining the classification of each point in `x`. Levels of the factor determine the classification.

- a numeric vector (of length equal to the number of points in `z`). The range of values of `z` will be divided into bands (the number of bands is determined by `...`) and `z` will be converted to a factor using `cut.default`.

- a pixel image on a network (object of class `"linim"`). The value of `z` at each point of `x` will be used as the classifying variable.

- a function on a network (object of class `"linfun"`, see `linfun`). The value of `z` at each point of `x` will be used as the classifying variable.

- a tessellation on a network (object of class `"lintess"`, see `lintess`). Each point of `x` will be classified according to the tile of the tessellation into which it falls.

- a character string, giving the name of one of the columns of `marks(x)`, if this is a data frame.

- a character string identifying one of the coordinates: the spatial coordinates `"x"`, `"y"` or the segment identifier `"seg"` or the fractional coordinate along the segment, `"tp"`.

The default is to take `z` to be the vector of marks in `x` (or the first column in the data frame of marks of `x`, if it is a data frame). If the marks are numeric, then the range of values of the numerical marks is divided into several intervals, and each interval is associated with a level of a factor. The result is a marked point pattern, on the same linear network, with the same point locations as `x`, but with the numeric mark of each point discretised by replacing it by the factor level. This is a convenient way to transform a marked point pattern which has numeric marks into a multitype point pattern, for example to plot it or analyse it. See the examples.

To select some points from `x`, use the subset operators `[.lpp` or `subset.lpp` instead.

### Value

A multitype point pattern on the same linear network, that is, a point pattern object (of class `"lpp"`) with a `marks` vector that is a factor.

### Author(s)

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

### See Also

`cut`, `lpp`, `lintess`, `linfun`, `linim`

### Examples

      X <- runiflpp(20, simplenet)
      f <- linfun(function(x,y,seg,tp) { x }, simplenet)
      plot(cut(X, f, breaks=4))
      plot(cut(X, "x", breaks=4))
      plot(cut(X, "seg"))

[Package spatstat.linnet version 2.0-0 Index]