fasp.object | R Documentation |
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 \le i,j \le 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 \times m
array of functions.
Alternatively we may compute a summary function
for each possible pair of types (i,j)
.
This produces an m \times m
array of functions.
For multitype point patterns the command alltypes
will compute arrays of summary functions for each possible
type or for each possible pair of types.
The function alltypes
returns an object of class "fasp"
.
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.
and \rolf
alltypes
,
plot.fasp
,
[.fasp
,
eval.fasp
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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