View source: R/linearpcfmulti.R

linearpcfcross.inhom | R Documentation |

For a multitype point pattern on a linear network,
estimate the inhomogeneous multitype pair correlation function
from points of type *i* to points of type *j*.

linearpcfcross.inhom(X, i, j, lambdaI, lambdaJ, r=NULL, ..., correction="Ang", normalise=TRUE)

`X` |
The observed point pattern,
from which an estimate of the |

`i` |
Number or character string identifying the type (mark value)
of the points in |

`j` |
Number or character string identifying the type (mark value)
of the points in |

`lambdaI` |
Intensity values for the points of type |

`lambdaJ` |
Intensity values for the points of type |

`r` |
numeric vector. The values of the argument |

`correction` |
Geometry correction.
Either |

`...` |
Arguments passed to |

`normalise` |
Logical. If |

This is a counterpart of the function `pcfcross.inhom`

for a point pattern on a linear network (object of class `"lpp"`

).

The argument `i`

will be interpreted as
levels of the factor `marks(X)`

.
If `i`

is missing, it defaults to the first
level of the marks factor.

The argument `r`

is the vector of values for the
distance *r* at which *g[ij](r)*
should be evaluated.
The values of *r* must be increasing nonnegative numbers
and the maximum *r* value must not exceed the radius of the
largest disc contained in the window.

If `lambdaI`

or `lambdaJ`

is a fitted point process model,
the default behaviour is to update the model by re-fitting it to
the data, before computing the fitted intensity.
This can be disabled by setting `update=FALSE`

.

An object of class `"fv"`

(see `fv.object`

).

The argument `i`

is interpreted as a
level of the factor `marks(X)`

. Beware of the usual
trap with factors: numerical values are not
interpreted in the same way as character values.

Baddeley, A, Jammalamadaka, A. and Nair, G. (to appear)
Multitype point process analysis of spines on the
dendrite network of a neuron.
*Applied Statistics* (Journal of the Royal Statistical
Society, Series C), **63**, 673–694.

`linearpcfdot`

,
`linearpcf`

,
`pcfcross.inhom`

.

lam <- table(marks(chicago))/(summary(chicago)$totlength) lamI <- function(x,y,const=lam[["assault"]]){ rep(const, length(x)) } lamJ <- function(x,y,const=lam[["robbery"]]){ rep(const, length(x)) } g <- linearpcfcross.inhom(chicago, "assault", "robbery", lamI, lamJ) # using fitted models for intensity # fit <- lppm(chicago ~marks + x) # linearpcfcross.inhom(chicago, "assault", "robbery", fit, fit)

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