marktable(X, R, N, exclude=TRUE, collapse=FALSE)"ppp".
N.
R.
exclude=TRUE, the neighbours of a point
do not include the point itself. If exclude=FALSE,
a point belongs to its own neighbourhood.
collapse=FALSE (the default) the results for
each point are returned as separate rows of a table.
If collapse=TRUE, the results are aggregated according to the
type of point.
"table").
If collapse=FALSE, the table has one row for
each point in X, and one column for each possible mark value.
If collapse=TRUE, the table has one row and one column
for each possible mark value.
X,
inspects all the neighbouring points within a radius R of the current
point (or the N nearest neighbours of the current point),
and compiles a frequency table of the marks attached to the
neighbours. The dataset X must be a multitype point pattern, that is,
marks(X) must be a factor.
If collapse=FALSE (the default),
the result is a two-dimensional contingency table with one row for
each point in the pattern, and one column for each possible mark
value. The [i,j] entry in the table gives the number of
neighbours of point i that have mark j.
If collapse=TRUE, this contingency table is aggregated
according to the type of point, so that the result is a contingency
table with one row and one column for each possible mark value.
The [i,j] entry in the table gives the number of
neighbours of a point with mark i that have mark j.
To perform more complicated calculations on the neighbours of every
point, use markstat or applynbd.
markstat,
applynbd,
Kcross,
ppp.object,
table
head(marktable(amacrine, 0.1))
head(marktable(amacrine, 0.1, exclude=FALSE))
marktable(amacrine, N=1, collapse=TRUE)
Run the code above in your browser using DataLab