plot.fads {ads}R Documentation

Plot second-order neigbourhood functions

Description

Plot second-order neigbourhood function estimates returned by functions kfun, k12fun, kmfun, kijfun or ki.fun.

Usage

## S3 method for class 'fads':
plot(x,opt,cols,lty,main,sub,legend,csize,...)

Arguments

x an object of class "fads" (see Details).
opt one of c("all","L","K","n","g") to dislay either all or one of the functions in a single window. By default opt = "all" for fads objects of subclass "kfun", "k12fun", or "kmfun"; by default opt = "L" for fads objects of subclass "kij", or "ki.".
cols (optional) coulours used for plotting functions.
lty (optional) line types used for plotting functions.
main by default, the value of argument x, otherwise a text to be displayed as a title of the plot. main=NULL displays no title.
sub by default, the name of the function displayed, otherwise a text to be displayed as function subtitle. sub=NULL displays no subtitle.
legend If legend = TRUE (the default) a legend for the plotting functions is displayed.
csize scaling factor for font size so that actual font size is par("cex")*csize. By default csize = 1.
... extra arguments that will be passed to the plotting functions plot.swin, plot.default, symbols and/or points.

Details

Function plot.fads displays second-order neighbourhood function estimates as a function of interpoint distance, with expected values as well as confidence interval limits when computed. Argument x can be any fads object returned by functions kfun, k12fun, kmfun, kijfun or ki.fun.

Value

none.

Author(s)

Raphael.Pelissier@ird.fr

See Also

kfun, k12fun, kmfun, kijfun, ki.fun.

Examples

  data(BPoirier)
  BP<-BPoirier
  # Ripley's function 
  swr<-spp(BP$trees,win=BP$rect)
  k.swr<-kfun(swr,25,1,500)
  plot(k.swr)
  
  # Intertype function
  swrm<-spp(BP$trees,win=BP$rect,marks=BP$species)
  k12.swrm<-k12fun(swrm,25,1,500,marks=c("beech","oak"))
  plot(k12.swrm,opt="L",cols=1)
  
  # Mark correlation function
  swrm<-spp(BP$trees,win=BP$rect,marks=BP$dbh)
  km.swrm<-kmfun(swrm,25,1,500)
  plot(km.swrm,main="Example 1",sub=NULL,legend=FALSE)


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