markvario | R Documentation |
Estimate the mark variogram of a marked point pattern.
markvario(X, correction = c("isotropic", "Ripley", "translate"), r = NULL, method = "density", ..., normalise=FALSE)
X |
The observed point pattern.
An object of class |
correction |
A character vector containing any selection of the
options |
r |
numeric vector. The values of the argument r at which the mark variogram gamma(r) should be evaluated. There is a sensible default. |
method |
A character vector indicating the user's choice of
density estimation technique to be used. Options are
|
... |
Other arguments passed to |
normalise |
If |
The mark variogram gamma(r) of a marked point process X is a measure of the dependence between the marks of two points of the process a distance r apart. It is informally defined as
gamma(r) = E[(1/2) * (M1 - M2)^2 ]
where E[ ] denotes expectation and M1,M2 are the marks attached to two points of the process a distance r apart.
The mark variogram of a marked point process is analogous, but not equivalent, to the variogram of a random field in geostatistics. See Waelder and Stoyan (1996).
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 variogram gamma(r) has been estimated |
theo |
the theoretical value of gamma(r) when the marks attached to different points are independent; equal to the sample variance of the marks |
together with a column or columns named
"iso"
and/or "trans"
,
according to the selected edge corrections. These columns contain
estimates of the function gamma(r)
obtained by the edge corrections named.
and \rolf
Cressie, N.A.C. (1991) Statistics for spatial data. John Wiley and Sons, 1991.
Mase, S. (1996) The threshold method for estimating annual rainfall. Annals of the Institute of Statistical Mathematics 48 (1996) 201-213.
Waelder, O. and Stoyan, D. (1996) On variograms in point process statistics. Biometrical Journal 38 (1996) 895-905.
Mark correlation function markcorr
for numeric marks.
Mark connection function markconnect
and
multitype K-functions Kcross
, Kdot
for factor-valued marks.
# Longleaf Pine data # marks represent tree diameter data(longleaf) # Subset of this large pattern swcorner <- owin(c(0,100),c(0,100)) sub <- longleaf[ , swcorner] # mark correlation function mv <- markvario(sub) plot(mv)
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