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, ...)
X |
Fitted point process model (object of class |
... |
Ignored. |
“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;
the 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.
The argument X is a fitted point process model
(an object of class "ppm") typically obtained
by fitting a model to point pattern data using ppm.
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). See the Examples for a trick to do 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 TRUE if
X is a model that was fitted to a marked point pattern dataset.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Rolf Turner r.turner@auckland.ac.nz.
X <- lansing # Multitype point pattern --- trees marked by species fit1 <- ppm(X, ~ marks, Poisson()) is.marked(fit1) fit2 <- ppm(X, ~ 1, Poisson()) is.marked(fit2) ## test whether the model formula involves marks "marks" %in% spatstat.utils::variablesinformula(formula(fit2)) # Unmarked point pattern fit3 <- ppm(cells, ~ 1, Poisson()) is.marked(fit3) # FALSE
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