markcorrint {spatstat} | R Documentation |
Estimates the mark correlation integral of a marked point pattern.
markcorrint(X, f = NULL, r = NULL, correction = c("isotropic", "Ripley", "translate"), ..., f1 = NULL, normalise = TRUE, returnL = FALSE, fargs = NULL)
X |
The observed point pattern.
An object of class |
f |
Optional. Test function f used in the definition of the mark correlation function. An R function with at least two arguments. There is a sensible default. |
r |
Optional. Numeric vector. The values of the argument r at which the mark correlation function k[f](r) should be evaluated. There is a sensible default. |
correction |
A character vector containing any selection of the
options |
... |
Ignored. |
f1 |
An alternative to |
normalise |
If |
returnL |
Compute the analogue of the K-function if |
fargs |
Optional. A list of extra arguments to be passed to the function
|
Given a marked point pattern X
,
this command estimates the weighted indefinite integral
K[f](r) = 2 * pi * integral[0,r] (s * k[f](s)) ds
of the mark correlation function k[f](r).
See markcorr
for a definition of the
mark correlation function.
The use of the weighted indefinite integral was advocated by Penttinen et al (1992). The relationship between K[f] and k[f] is analogous to the relationship between the classical K-function K(r) and the pair correlation function g(r).
If returnL=FALSE
then the function K[f](r) is returned;
otherwise the function
L[f](r) = sqrt(K[f](r)/pi)
is returned.
An object of class "fv"
(see fv.object
).
Essentially a data frame containing numeric columns
r |
the values of the argument r at which the mark correlation integral K[f](r) has been estimated |
theo |
the theoretical value of K[f](r) when the marks attached to different points are independent, namely pi * r^2 |
together with a column or columns named
"iso"
and/or "trans"
,
according to the selected edge corrections. These columns contain
estimates of the mark correlation integral K[f](r)
obtained by the edge corrections named (if returnL=FALSE
).
Adrian Baddeley Adrian.Baddeley@csiro.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner r.turner@auckland.ac.nz
Penttinen, A., Stoyan, D. and Henttonen, H. M. (1992) Marked point processes in forest statistics. Forest Science 38 (1992) 806-824.
Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008) Statistical analysis and modelling of spatial point patterns. Chichester: John Wiley.
markcorr
to estimate the mark correlation function.
# CONTINUOUS-VALUED MARKS: # (1) Spruces # marks represent tree diameter data(spruces) # mark correlation function ms <- markcorrint(spruces) plot(ms) # (2) simulated data with independent marks X <- rpoispp(100) X <- X %mark% runif(X$n) Xc <- markcorrint(X) plot(Xc) # MULTITYPE DATA: # Hughes' amacrine data # Cells marked as 'on'/'off' data(amacrine) M <- markcorrint(amacrine, function(m1,m2) {m1==m2}, correction="translate") plot(M)