quadratcount(X, nx=5, ny=nx, xbreaks, ybreaks)"ppp").xbreaks and ybreaks.nx.ny.  The table is also an object of the special class "quadratcount"
  and there is a plot method for this class.
X is divided into
  an nx * ny grid of rectangular tiles or `quadrats'.
  The number of points of X falling in each quadrat is
  counted. These numbers are returned as a contingency table.  If xbreaks is given, it should be a numeric vector
  giving the $x$ coordinates of the quadrat boundaries.
  If it is not given, it defaults to a
  sequence of nx+1 values equally spaced
  over the range of $x$ coordinates in the window X$window.
  Similarly if ybreaks is given, it should be a numeric
  vector giving the $y$ coordinates of the quadrat boundaries.
  It defaults to a vector of ny+1 values
  equally spaced over the range of $y$ coordinates in the window.
  The lengths of xbreaks and ybreaks may be different.
  The algorithm counts the number of points of X
  falling in each quadrat, and returns these counts as a
  contingency table. The [i,j] entry in the contingency table
  is the point count for the quadrat with coordinates
  (xbreaks[i],xbreaks[i+1]) by (ybreaks[i], ybreaks[i+1]).
  The return value is a table which can be printed neatly.
  The return value is also a member of the special class
  "quadratcount". Plotting the object will display the
  quadrats, annotated by their counts. See the examples.
  
  To perform a chi-squared test based on the quadrat counts,
  use quadrat.test.
Stoyan, D. and Stoyan, H. (1994) Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.
quadrat.testX <- runifpoint(50)
 quadratcount(X)
 quadratcount(X, 4, 5)
 quadratcount(X, xbreaks=c(0, 0.3, 1), ybreaks=c(0, 0.4, 0.8, 1))
 qX <-  quadratcount(X, 4, 5)
 # plotting:
 plot(X, pch="+")
 plot(qX, add=TRUE, col="red", cex=1.5, lty=2)Run the code above in your browser using DataLab