as.owin: Convert Data To Class owin

Description

Converts data specifying an observation window in any of several formats, into an object of class "owin".

Usage

as.owin(W, ..., fatal=TRUE)
 ## S3 method for class 'owin':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'ppp':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'ppm':
as.owin(W, \dots, from=c("points", "covariates"), fatal=TRUE)
 ## S3 method for class 'kppm':
as.owin(W, \dots, from=c("points", "covariates"), fatal=TRUE)
 ## S3 method for class 'lpp':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'lppm':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'psp':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'quad':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'tess':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'im':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'layered':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'gpc.poly':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'data.frame':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'distfun':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'nnfun':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'funxy':
as.owin(W, \dots, fatal=TRUE)
 ## S3 method for class 'rmhmodel':
as.owin(W, \dots, fatal=FALSE)
 ## S3 method for class 'default':
as.owin(W, \dots, fatal=TRUE)

Arguments

Data specifying an observation window, in any of several formats described under Details below.

fatal

Logical flag determining what to do if the data cannot be converted to an observation window. See Details.

...

Ignored.

from

Character string. See Details.

Value

An object of class "owin" (see owin.object) specifying an observation window.

Details

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 entriesxrange,yrangespecifying 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 entriesxl,xu,yl,yuspecifying the$x$and$y$dimensions of a rectangle as(xmin, xmax) = (xl, xu)and(ymin, ymax) = (yl, yu). This will accept objects of classsppused in the Venables and Ripleyspatiallibrary.
an object of class"gpc.poly"from thegpclibpackage, representing a polygonal window.
an object of class"ppp"representing a point pattern. In this case, the object'swindowstructure will be extracted.
an object of class"psp"representing a line segment pattern. In this case, the object'swindowstructure will be extracted.
an object of class"tess"representing a tessellation. In this case, the object'swindowstructure will be extracted.
an object of class"quad"representing a quadrature scheme. In this case, the window of thedatacomponent 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 ofNA, signifying that the pixel lies outside the window, is transformed into the logical valueFALSE, which is the corresponding convention for window masks.
an object of class"ppm"or"kppm"representing a fitted point process model. In this case, iffrom="data"(the default),as.owinextracts the original point pattern data to which the model was fitted, and returns the observation window of this point pattern. Iffrom="covariates"thenas.owinextracts 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.owinextracts 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.owinextracts the linear network and returns a window containing this network.
Adata.framewith 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.
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 usingrmh. The window (spatial domain) of the model will be extracted. The window may beNULLin some circumstances (indicating that the simulation window has not yet been determined). This is not treated as an error, because the argumentfataldefaults toFALSEfor this method.
an object of class"layered"representing a list of spatial objects. Seelayered. In this case,as.owinwill 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).

Examples

Run this code

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)