Kmulti.uf {ecespa} | R Documentation |
For a bivariate point pattern, calculates the combined estimator of the multitype Kij(r) and Kji(r) functions.
Kmulti.uf(X, I, J, r = NULL, corre = "isotropic")
X |
Multitype marked point pattern. An object with the ppp format of spatstat. |
I |
Subset index specifying the points of the first pattern. |
J |
Subset index specifying the points of the second pattern. |
r |
Numeric vector. The values of the argument r at which the multitype K function K*ij(r) should be evaluated. |
corre |
A character item selecting any of the options "border", "bord.modif", "isotropic", "Ripley" or
"translate", as described in Kest . It specifies the edge correction(s) to be applied. |
As a consequence of edge effects, the estimators Kij(r) and Kji(r) of the same bivariate pattern could differ. K*ij(r) is the combined estimator defined by Upton and Fingleton (1985) as
nj Kij(r)+ ni Kji(r) / (ni + nj) ,
ni and nj being respectively the number of points in I and J.
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 function K*ij(r) has been estimated |
theo |
The theoretical value of K*ij(r) for a marked Poisson process, namely pi * r^2 |
.
together with a column or columns named "border", "bord.modif", "iso" and/or "trans", according to the selected edge corrections.
These columns contain estimates of the function K*ij(r) obtained by the edge corrections named.
Kmulti.uf
is a wrapper for a convenient use of the Kmulti
function of spatstat.
Please refer to its help page for additional documentation.
Marcelino de la Cruz. marcelino.delacruz@upm.es
Upton, G. J.G. & Fingleton, B. 1985. Spatial data analysis by example, Vol. I. Point pattern and quantitative data. Wiley, NY.
## Not run: data(amacrine) plot(Kmulti.uf(amacrine, I=amacrine$marks=="on", J=amacrine$marks=="off", corre="isotropic"), sqrt(./pi)-r~r, main="") # compare with Kmulti plot(Kmulti(amacrine, I=amacrine$marks=="on", J=amacrine$marks=="off"), sqrt(iso/pi)-r~r, add=TRUE, col=3) plot(Kmulti(amacrine, J=amacrine$marks=="on", I=amacrine$marks=="off"), sqrt(iso/pi)-r~r, add=TRUE, col=4) ## End(Not run)