Function Arrays for Spatial Patterns
A class "fasp" to represent a “matrix”
of functions, amenable to plotting as a matrix of plot panels.
An object of this class is a convenient way of storing (and later plotting, editing, etc) a set of functions f[i,j](r) of a real argument r, defined for each possible pair (i,j) of indices 1 <= i,j <= n. We may think of this as a matrix or array of functions f[i,j].
Function arrays are particularly useful in the analysis of a multitype point pattern (a point pattern in which the points are identified as belonging to separate types). We may want to compute a summary function for the points of type i only, for each of the possible types i. This produces a 1 * m array of functions. Alternatively we may compute a summary function for each possible pair of types (i,j). This produces an m * m array of functions.
An object of class "fasp" is a list containing at least the
following components:
A list of data frames, each representing one of the functions.
A matrix representing the spatial arrangement of the
functions. If which[i,j] = k
then the function represented by fns[[k]] should be plotted
in the panel at position (i,j). If which[i,j] = NA
then nothing is plotted in that position.
A list of character strings, providing suitable plotting titles for the functions.
A list of default formulae for plotting each of the functions.
A character string, giving a default title for the array when it is plotted.
There are methods for plot, print and "["
for this class.
The plot method displays the entire array of functions.
The method [.fasp selects a sub-array using the natural
indices i,j.
The command eval.fasp can be used to apply
a transformation to each function in the array,
and to combine two arrays.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
and Rolf Turner r.turner@auckland.ac.nz
GG <- alltypes(amacrine, 'G') plot(GG) # select the row corresponding to cells of type "on" Gon <- GG["on", ] plot(Gon) # extract the G function for i = "on", j = "off" Gonoff <- GG["on", "off", drop=TRUE] # Fisher variance stabilising transformation GGfish <- eval.fasp(asin(sqrt(GG))) plot(GGfish)
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