compileK  R Documentation 
Lowlevel functions which
calculate the estimated K
function
and estimated pair correlation function
(or any similar functions)
from a matrix of pairwise distances and optional weights.
compileK(D, r, weights = NULL, denom = 1,
check = TRUE, ratio = FALSE, fname = "K",
samplesize=denom)
compilepcf(D, r, weights = NULL, denom = 1,
check = TRUE, endcorrect = TRUE, ratio=FALSE,
..., fname = "g", samplesize=denom)
D 
A square matrix giving the distances between all pairs of points. 
r 
An equally spaced, finely spaced sequence of distance values. 
weights 
Optional numerical weights for the pairwise distances.
A numeric matrix with the same dimensions as 
denom 
Denominator for the estimator.
A single number, or a numeric vector with the same length
as 
check 
Logical value specifying whether to check that 
ratio 
Logical value indicating whether to store ratio information. See Details. 
... 
Optional arguments passed to 
endcorrect 
Logical value indicating whether to apply End Correction of
the pair correlation estimate at 
fname 
Character string giving the name of the function being estimated. 
samplesize 
The sample size that should be used as the denominator when

These lowlevel functions construct estimates of
the K
function or pair correlation function,
or any similar functions, given only the matrix of pairwise
distances and optional weights associated with these distances.
These functions are useful for code development and for teaching,
because they perform a common task, and do the housekeeping required to
make an object of class "fv"
that represents the estimated
function. However, they are not very efficient.
compileK
calculates the weighted estimate
of the K
function,
\hat K(r) = (1/v(r)) \sum_i \sum_j 1\{ d_{ij} \le r\} w_{ij}
and compilepcf
calculates the weighted estimate of the
pair correlation function,
\hat g(r) = (1/v(r)) \sum_i \sum_j \kappa( d_{ij}  r ) w_{ij}
where d_{ij}
is the distance between spatial points
i
and j
, with corresponding weight w_{ij}
,
and v(r)
is a specified denominator. Here \kappa
is a fixedbandwidth smoothing kernel.
For a point pattern in two dimensions, the usual denominator v(r)
is constant for the K
function, and proportional to r
for the pair correlation function. See the Examples.
The result is an object of class "fv"
representing the
estimated function. This object has only one column of function
values. Additional columns (such as a column giving the theoretical
value) must be added by the user, with the aid of
bind.fv
.
If ratio=TRUE
, the result also belongs to class "rat"
and has attributes containing the numerator and denominator
of the function estimate.
(If samplesize
is given, the numerator and denominator are
rescaled by a common factor so that the denominator is
equal to samplesize
.)
This allows function estimates from
several datasets to be pooled using pool
.
An object of class "fv"
representing the estimated function.
Kest
,
pcf
for definitions of the K
function
and pair correlation function.
bind.fv
to add more columns.
compileCDF
for the corresponding lowlevel utility
for estimating a cumulative distribution function.
## Equivalent to Kest(japanesepines) and pcf(japanesepines)
X < japanesepines
D < pairdist(X)
Wt < edge.Ripley(X, D)
lambda < intensity(X)
a < (npoints(X)1) * lambda
r < seq(0, 0.25, by=0.01)
K < compileK(D=D, r=r, weights=Wt, denom=a)
g < compilepcf(D=D, r=r, weights=Wt, denom= a * 2 * pi * r)
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