`"ppp"`

representing
a point pattern dataset in the two-dimensional plane.`ppp(x,y, ..., window, marks, check=TRUE)`

x

Vector of $x$ coordinates of data points

y

Vector of $y$ coordinates of data points

window

window of observation,
an object of class

`"owin"`

...

arguments passed to

`owin`

to create the
window, if `window`

is missingmarks

(optional) vector of mark values

check

Logical flag indicating whether to check
that all the $(x,y)$ points lie inside the specified window.
Do not set this to

`FALSE`

unless you are sure that this
check is unnecessary.- An object of class
`"ppp"`

describing a point pattern in the two-dimensional plane (see`ppp.object`

).

`x`

and `y`

`"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, yrange`

specify a rectangle with these dimensions;`poly`

specifies a polygonal boundary. If the boundary is a single polygon then`poly`

must be a list with components`x,y`

giving the coordinates of the vertices. If the boundary consists of several disjoint polygons then`poly`

must be a list of such lists so that`poly[[i]]$x`

gives the$x$coordinates of the vertices of the$i$th boundary polygon.`mask`

specifies a binary pixel image with entries that are`TRUE`

if the corresponding pixel is inside the window.`window`

is an object of class`"owin"`

(see`owin.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.

In the current implementation, `marks`

must be a vector, of
the same length as `x`

and `y`

, which is interpreted so
that `marks[i]`

is the mark attached to the point
`(x[i],y[i])`

. If the mark is a real number then `marks`

should be a numeric vector, while if the mark takes only a finite
number of possible values (e.g. colours or types) then
`marks`

should be a `factor`

.
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)
```

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