Converts data specifying an observation window
in any of several formats, into an object of class "owin".
as.owin(W, …, fatal=TRUE) # S3 method for owin
as.owin(W, …, fatal=TRUE)
# S3 method for ppp
as.owin(W, …, fatal=TRUE)
# S3 method for ppm
as.owin(W, …, from=c("points", "covariates"), fatal=TRUE)
# S3 method for kppm
as.owin(W, …, from=c("points", "covariates"), fatal=TRUE)
# S3 method for dppm
as.owin(W, …, from=c("points", "covariates"), fatal=TRUE)
# S3 method for lpp
as.owin(W, …, fatal=TRUE)
# S3 method for lppm
as.owin(W, …, fatal=TRUE)
# S3 method for msr
as.owin(W, …, fatal=TRUE)
# S3 method for psp
as.owin(W, …, fatal=TRUE)
# S3 method for quad
as.owin(W, …, fatal=TRUE)
# S3 method for quadratcount
as.owin(W, …, fatal=TRUE)
# S3 method for quadrattest
as.owin(W, …, fatal=TRUE)
# S3 method for tess
as.owin(W, …, fatal=TRUE)
# S3 method for im
as.owin(W, …, fatal=TRUE)
# S3 method for layered
as.owin(W, …, fatal=TRUE)
# S3 method for data.frame
as.owin(W, …, step, fatal=TRUE)
# S3 method for distfun
as.owin(W, …, fatal=TRUE)
# S3 method for nnfun
as.owin(W, …, fatal=TRUE)
# S3 method for funxy
as.owin(W, …, fatal=TRUE)
# S3 method for boxx
as.owin(W, …, fatal=TRUE)
# S3 method for rmhmodel
as.owin(W, …, fatal=FALSE)
# S3 method for leverage.ppm
as.owin(W, …, fatal=TRUE)
# S3 method for influence.ppm
as.owin(W, …, fatal=TRUE)
# S3 method for default
as.owin(W, …, fatal=TRUE)
Data specifying an observation window, in any of several formats described under Details below.
Logical flag determining what to do if the data cannot be converted to an observation window. See Details.
Ignored.
Character string. See Details.
Optional. A single number, or numeric vector of length 2, giving the grid step lengths in the \(x\) and \(y\) directions.
An object of class "owin" (see owin.object)
specifying an observation window.
The class "owin" is a way of specifying the observation window
for a point pattern. See owin.object for an overview.
This function converts data in any of several formats
into an object of class "owin" for use by the spatstat
package. The function as.owin is generic, with methods
for different classes of objects, and a default method.
The argument W may be
an object of class "owin"
a structure with entries xrange, yrange specifying the
\(x\) and \(y\) dimensions of a rectangle
a four-element vector
(interpreted as (xmin, xmax, ymin, ymax))
specifying the \(x\) and \(y\) dimensions of a rectangle
a structure with entries xl, xu, yl, yu
specifying the \(x\) and \(y\) dimensions of a rectangle
as (xmin, xmax) = (xl, xu) and
(ymin, ymax) = (yl, yu). This will accept objects of
class spp used in the Venables and Ripley spatial
library.
an object of class "ppp" representing a point pattern.
In this case, the object's window structure will be
extracted.
an object of class "psp" representing a line segment pattern.
In this case, the object's window structure will be
extracted.
an object of class "tess" representing a tessellation.
In this case, the object's window structure will be
extracted.
an object of class "quad" representing a quadrature scheme.
In this case, the window of the data component will be
extracted.
an object of class "im" representing a pixel image.
In this case, a window of type "mask" will be returned,
with the same pixel raster coordinates as the image.
An image pixel value of NA, signifying that the pixel
lies outside the window, is transformed into the logical value
FALSE, which is the corresponding convention for window masks.
an object of class "ppm", "kppm" or "dppm"
representing a fitted point process
model. In this case, if from="data" (the default),
as.owin extracts the original point
pattern data to which the model was fitted, and returns the
observation window of this point pattern. If
from="covariates" then as.owin extracts the
covariate images to which the model was fitted,
and returns a binary mask window that specifies the pixel locations.
an object of class "lpp"
representing a point pattern on a linear network.
In this case, as.owin extracts the linear network
and returns a window containing this network.
an object of class "lppm"
representing a fitted point process model on a linear network.
In this case, as.owin extracts the linear network
and returns a window containing this network.
A data.frame with exactly three columns. Each row of the
data frame corresponds to one pixel. Each row contains the
\(x\) and \(y\) coordinates of a pixel, and a logical value
indicating whether the pixel lies inside the window.
A data.frame with exactly two columns. Each row of the
data frame contains the \(x\) and \(y\) coordinates of a pixel
that lies inside the window.
an object of class "distfun", "nnfun"
or "funxy" representing a function of spatial location,
defined on a spatial domain. The spatial domain of the function will be
extracted.
an object of class "rmhmodel" representing a
point process model that can be simulated using rmh.
The window (spatial domain) of the model will be extracted.
The window may be NULL in some circumstances (indicating that the
simulation window has not yet been determined). This is not treated
as an error, because the argument fatal defaults to
FALSE for this method.
an object of class "layered" representing a
list of spatial objects. See layered.
In this case, as.owin will be applied to each
of the objects in the list, and the union of these windows
will be returned.
If the argument W is not in one of these formats
and cannot be converted to a window, then an error will
be generated (if fatal=TRUE) or a value of NULL
will be returned (if fatal=FALSE).
When W is a data frame, the argument step
can be used to specify the pixel grid spacing; otherwise, the spacing
will be guessed from the data.
# NOT RUN {
w <- as.owin(c(0,1,0,1))
w <- as.owin(list(xrange=c(0,5),yrange=c(0,10)))
# point pattern
data(demopat)
w <- as.owin(demopat)
# image
Z <- as.im(function(x,y) { x + 3}, unit.square())
w <- as.owin(Z)
# Venables & Ripley 'spatial' package
require(spatial)
towns <- ppinit("towns.dat")
w <- as.owin(towns)
detach(package:spatial)
# }
Run the code above in your browser using DataLab