redwood.
 
  Strauss (1975) divided the sampling region into two subregions I and II
  demarcated by a diagonal line.  The spatial pattern
  appears to be slightly regular in region I and strongly clustered in 
  region II.   Strauss (1975) writes: 
  The dataset redwoodfull contains the full point pattern
  of 195 trees. 
  The window has been rescaled to the unit square.
  Its physical size is approximately 130 feet across.
  The auxiliary information about the subregions is contained in 
  redwoodfull.extra, which is a list with entries
  rdiag	The coordinates of the diagonal boundary
	between regions I and II 
regionI 	Region I as a window object 
regionII 	Region II as a window object 
regionR 	Ripley's subrectangle (approximate) 
plotit    	Function to plot the full data and auxiliary markings
  }
  Ripley (1977) extracted a subset of these data, containing 62 points,
  lying within a square subregion which overlaps regions I and II.
  He rescaled that subset to the unit square. 
  This subset has been re-analysed many times,
  and is the dataset usually known as
  ``the redwood data'' in the spatial statistics literature.
  The exact dataset used by Ripley is supplied in the redwood.
  The approximate position of the square chosen by Ripley
  within the redwoodfull pattern
  is indicated by the window redwoodfull.extra$regionR.
  There are some minor inconsistencies with
  redwood since it originates from a different digitisation.
data(redwoodfull)redwoodfull is an object of class "ppp"
  representing the point pattern of tree locations.
  See ppp.object for details of the format of a
  point pattern object.
  The window has been rescaled to the unit square.
  Its physical size is approximately 128 feet across.  The dataset redwoodfull.extra is a list with entries
  rdiag	coordinates of endpoints of a line,
	in format list(x=numeric(2),y=numeric(2)) 
regionI 	a window object 
regionII 	a window object 
regionR 	a window object 
plotit    	Function with no arguments
  }
Ripley, B.D. (1977) Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society, Series B 39, 172--212.
Strauss, D.J. (1975) A model for clustering. Biometrika 63, 467--475.
redwooddata(redwoodfull)
       plot(redwoodfull)
       redwoodfull.extra$plotit()
       # extract the pattern in region II 
       redwoodII <- redwoodfull[, redwoodfull.extra$regionII]Run the code above in your browser using DataLab