Test Whether A Point Process Model is Marked
Tests whether a fitted point process model involves ``marks'' attached to the points.
## S3 method for class 'ppm': is.marked(X, \dots)
- Fitted point process model (object of class
"ppm") usually obtained from
``Marks'' are observations attached to each point of a point pattern.
For example the
longleaf dataset contains the locations
of trees, each tree being marked by its diameter;
amacrine dataset gives the locations of cells
of two types (on/off) and the type of cell may be regarded as a mark attached
to the location of the cell.
X is a fitted point process model
(an object of class
"ppm") typically obtained
by fitting a model to point pattern data using
This function returns
TRUE if the original data
(to which the model
X was fitted) were a marked point pattern.
Note that this is not the same as testing whether the model involves terms that depend on the marks (i.e. whether the fitted model ignores the marks in the data). Currently we have not implemented a test for this.
If this function returns
TRUE, the implications are
(for example) that
any simulation of this model will require simulation of random marks
as well as random point locations.
- Logical value, equal to
Xis a model that was fitted to a marked point pattern dataset.
data(lansing) # Multitype point pattern --- trees marked by species <testonly># Smaller dataset data(betacells) lansing <- betacells[seq(2, 135, by=3), ]</testonly> fit1 <- ppm(lansing, ~ marks, Poisson()) is.marked(fit1) # TRUE fit2 <- ppm(lansing, ~ 1, Poisson()) is.marked(fit2) # TRUE data(cells) # Unmarked point pattern fit3 <- ppm(cells, ~ 1, Poisson()) is.marked(fit3) # FALSE