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ripras(x, y=NULL, shape="convex", f)x coordinates of observed points,
or a 2-column matrix giving x,y coordinates,
or a list with components x,y giving coordinates
(such as a point pattern object of class "ppp".)y coordinates of observed points,
if x is a vector."convex" or "rectangle"."owin").x and y, this function computes
an estimate due to Ripley and Rasson (1977) of the
spatial domain from which the points came. The points are assumed to have been generated independently and uniformly distributed inside an unknown domain $D$.
If shape="convex" (the default), the domain $D$ is assumed
to be a convex set. The maximum
likelihood estimate of $D$ is the convex hull of the
points (computed by convexhull.xy).
Analogously to the problems of estimating the endpoint
of a uniform distribution, the MLE is not optimal.
Ripley and Rasson's estimator is a rescaled copy of the convex hull,
centred at the centroid of the convex hull.
The scaling factor is
$1/sqrt(1 - m/n)$
where $n$ is the number of data points and
$m$ the number of vertices of the convex hull.
The scaling factor may be overridden using the argument f.
If shape="rectangle", the domain $D$ is assumed
to be a rectangle with sides parallel to the coordinate axes. The maximum
likelihood estimate of $D$ is the bounding box of the points
(computed by bounding.box.xy). The Ripley-Rasson
estimator is a rescaled copy of the bounding box,
with scaling factor $1/sqrt(1 - 4/n)$
where $n$ is the number of data points,
centred at the centroid of the bounding box.
The scaling factor may be overridden using the argument f.
owin,
as.owin,
bounding.box.xy,
convexhull.xyx <- runif(30)
y <- runif(30)
w <- ripras(x,y)
plot(owin(), main="ripras(x,y)")
plot(w, add=TRUE)
points(x,y)
X <- rpoispp(15)
plot(X, main="ripras(X)")
plot(ripras(X), add=TRUE)
# two points insufficient
ripras(c(0,1),c(0,0))
# triangle
ripras(c(0,1,0.5), c(0,0,1))
# three collinear points
ripras(c(0,0,0), c(0,1,2))Run the code above in your browser using DataLab