dval {ads} | R Documentation |

Computes local density estimates of a spatial point pattern, i.e. the number of points per unit area,
within sample circles of regularly increasing radii *r*, centred at the nodes of
a grid covering a simple (rectangular or circular) or complex sampling window (see Details).

dval(p, upto, by, nx, ny)

`p ` |
a |

`upto ` |
maximum radius of the sample circles (see Details). |

`by ` |
interval length between successive sample circles radii (see Details). |

`nx,ny ` |
number of sample circles regularly spaced out in |

The local density is estimated for a regular sequence of sample circles radii given by `seq(by,upto,by)`

(see `seq`

).
The sample circles are centred at the nodes of a regular grid with size *nx* by *ny*. Ripley's edge effect correction is applied when
the sample circles overlap boundary of the sampling window (see Ripley (1977) or Goreaud & P?Pelissier (1999) for an extension to circular and complex
sampling windows). Due to edge effect correction, `upto`

, the maximum radius of the sample circles, is half the longer side for a rectangle sampling
window (i.e. *0.5*max((xmax-xmin),(ymax-ymin))*) and the radius *r0* for a circular sampling window (see `swin`

).

A list of class `c("vads","dval")`

with essentially the following components:

`r ` |
a vector of regularly spaced out distances ( |

`xy ` |
a data frame of |

`cval ` |
a matrix of size |

`dval ` |
a matrix of size |

In its current version, function `dval`

ignores the marks of multivariate and marked point patterns (they are all considered to be univariate patterns).

There are printing, summary and plotting methods for `"vads"`

objects.

Goreaud, F. and P?Pelissier, R. 1999. On explicit formula of edge effect correction for Ripley's *K*-function. *Journal of Vegetation Science*, 10:433-438.

P?Pelissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. *Journal of Vegetation Science*, 12:99-108.

Ripley, B.D. 1977. Modelling spatial patterns. *Journal of the Royal Statistical Society B*, 39:172-212.

data(BPoirier) BP <- BPoirier ## Not run: spatial point pattern in a rectangle sampling window of size [0,110] x [0,90] swr <- spp(BP$trees, win=BP$rect) dswr <- dval(swr,25,1,11,9) summary(dswr) plot(dswr) ## Not run: spatial point pattern in a circle with radius 50 centred on (55,45) swc <- spp(BP$trees, win=c(55,45,45)) dswc <- dval(swc,25,1,9,9) summary(dswc) plot(dswc) ## Not run: spatial point pattern in a complex sampling window swrt <- spp(BP$trees, win=BP$rect, tri=BP$tri1) dswrt <- dval(swrt,25,1,11,9) summary(dswrt) plot(dswrt)

[Package *ads* version 1.5-5 Index]