"ppp" representing
a point pattern dataset in the two-dimensional plane.ppp(x,y, ..., window, marks, check=TRUE, drop=TRUE)"owin"owin to create the
window, if window is missingFALSE unless you are sure that this
check is unnecessary."ppp"
describing a point pattern in the two-dimensional plane
(see ppp.object).x and y
must lie inside the specified window, in order to
define a valid object of class "ppp".
Any points which do not lie inside the window will be
removed from the point pattern, and a warning will be issued. The rejected points are still accessible: they are stored
as an attribute of the point pattern called "rejects"
(which is an object of class "ppp" containing the rejected points
in a large window). However, rejected points in a point pattern
will be ignored by all other functions except
plot.ppp.
To remove the rejected points altogether,
use as.ppp. To include the rejected points,
you will need to find a larger window that contains them,
and use this larger window in a call to ppp.
"ppp". This function
creates such objects. The vectors x and y must be numeric vectors of
equal length. They are interpreted as the cartesian coordinates
of the points in the pattern.
A point pattern dataset is assumed to have been observed within a specific
region of the plane called the observation window.
An object of class "ppp" representing a point pattern
contains information specifying the observation window.
This window must always be specified when creating a point pattern dataset;
there is intentionally no default action of ``guessing'' the window
dimensions from the data points alone.
You can specify the observation window in several (mutually exclusive) ways:
xrange, yrangespecify a rectangle
with these dimensions;polyspecifies a polygonal boundary.
If the boundary is a single polygon thenpolymust be a list with componentsx,ygiving the coordinates of the vertices.
If the boundary consists of several disjoint polygons
thenpolymust be a list of such lists
so thatpoly[[i]]$xgives the$x$coordinates
of the vertices of the$i$th boundary polygon.maskspecifies a binary pixel image with entries
that areTRUEif the corresponding pixel is inside
the window.windowis an object of class"owin"(seeowin.object) specifying the window.xrange, yrange or poly
or mask are passed to the window creator function
owin for interpretation. See
owin for further details. The argument window, if given, must be an object of class
"owin". It is a full description of the window geometry,
and could have been obtained from owin or
as.owin, or by just extracting the observation window
of another point pattern, or by manipulating such windows.
See owin or the Examples below.
The points with coordinates x and y
must lie inside the specified window, in order to
define a valid object of this class.
Any points which do not lie inside the window will be
removed from the point pattern, and a warning will be issued.
See the section on Rejected Points.
The name of the unit of length for the x and y coordinates
can be specified in the dataset, using the argument unitname, which is
passed to owin. See the examples below, or the help file
for owin.
The optional argument marks is given if the point pattern
is marked, i.e. if each data point carries additional information.
For example, points which are classified into two or more different
types, or colours, may be regarded as having a mark which identifies
which colour they are. Data recording the locations and heights of
trees in a forest can be regarded as a marked point pattern where the
mark is the tree height.
The argument marks can be either
xandy, which is interpreted so
thatmarks[i]is the mark attached to the point(x[i],y[i]). If the mark is a real number thenmarksshould be a numeric vector, while if the mark takes only a finite
number of possible values (e.g. colours or types) thenmarksshould be afactor.ith row of the data frame is interpreted
as containing the mark values for theith point in the point
pattern. The columns of the data frame correspond to different
mark variables (e.g. tree species and tree diameter).drop=TRUE (the default), then
a data frame with only one column will be
converted to a vector, and a data frame with no columns will be
converted to NULL.
See ppp.object for a description of the
class "ppp". Users would normally invoke ppp to create a point pattern,
but the functions as.ppp and
scanpp may sometimes be convenient.
ppp.object,
as.ppp,
owin.object,
owin,
as.owin# some arbitrary coordinates in [0,1]
x <- runif(20)
y <- runif(20)
# the following are equivalent
X <- ppp(x, y, c(0,1), c(0,1))
X <- ppp(x, y)
X <- ppp(x, y, window=owin(c(0,1),c(0,1)))
# specify that the coordinates are given in metres
X <- ppp(x, y, c(0,1), c(0,1), unitname=c("metre","metres"))
plot(X)
# marks
m <- sample(1:2, 20, replace=TRUE)
m <- factor(m, levels=1:2)
X <- ppp(x, y, c(0,1), c(0,1), marks=m)
plot(X)
# polygonal window
X <- ppp(x, y, poly=list(x=c(0,10,0), y=c(0,0,10)))
plot(X)
# copy the window from another pattern
data(cells)
X <- ppp(x, y, window=cells$window)Run the code above in your browser using DataLab