ahull {alphahull} | R Documentation |

This function calculates the `\alpha`

-convex hull of a given sample of points in the plane for `\alpha>0`

.

```
ahull(x, y = NULL, alpha)
```

`x` , `y` |
The |

`alpha` |
Value of |

An attempt is made to interpret the arguments x and y in a way suitable for computing the `\alpha`

-convex hull. Any reasonable way of defining the coordinates is acceptable, see `xy.coords`

.

The `\alpha`

-convex hull is defined for any finite number of points. However, since the algorithm is based on the Delaunay triangulation, at least three non-collinear points are required.

If `y`

is NULL and `x`

is an object of class `"delvor"`

, then the `\alpha`

-convex hull is computed with no need to invoke again the function `delvor`

(it reduces the computational cost).

The complement of the `\alpha`

-convex hull can be written as the union of `O(n)`

open balls and halfplanes, see `complement`

.
The boundary of the `\alpha`

-convex hull is formed by arcs of open balls of radius `\alpha`

(besides possible isolated sample points). The arcs are determined by the intersections of some of the balls that define the complement of the `\alpha`

-convex hull. The extremes of an arc are given by `c+rA_\theta v`

and `c+rA_{-\theta}v`

where `c`

and `r`

represent the center and radius of the arc, repectively, and `A_\theta v`

represents the clockwise rotation of angle `\theta`

of the unitary vector `v`

. Joining the end points of adjacent arcs we can define polygons that help us to determine the area of the estimator , see `areaahull`

.

A list with the following components:

`arcs` |
For each arc in the boundary of the |

`xahull` |
A 2-column matrix with the coordinates of the original set of points besides possible new end points of the arcs in the boundary of the |

`length` |
Length of the boundary of the |

`complement` |
Output matrix from |

`alpha` |
Value of |

`ashape.obj` |
Object of class |

Edelsbrunner, H., Kirkpatrick, D.G. and Seidel, R. (1983). On the shape of a set of points in the plane. *IEEE Transactions on Information Theory*, 29(4), pp.551-559.

Rodriguez-Casal, R. (2007). Set estimation under convexity type assumptions. *Annales de l'I.H.P.- Probabilites & Statistiques*, 43, pp.763-774.

Pateiro-Lopez, B. (2008). *Set estimation under convexity type restrictions*. Phd. Thesis. Universidad de Santiago de Compostela. ISBN 978-84-9887-084-8.

```
## Not run:
# Random sample in the unit square
x <- matrix(runif(100), nc = 2)
# Value of alpha
alpha <- 0.2
# Alpha-convex hull
ahull.obj <- ahull(x, alpha = alpha)
plot(ahull.obj)
# Uniform sample of size n=300 in the annulus B(c,0.5)\B(c,0.25),
# with c=(0.5,0.5).
n <- 300
theta<-runif(n,0,2*pi)
r<-sqrt(runif(n,0.25^2,0.5^2))
x<-cbind(0.5+r*cos(theta),0.5+r*sin(theta))
# Value of alpha
alpha <- 0.1
# Alpha-convex hull
ahull.obj <- ahull(x, alpha = alpha)
# The arcs defining the boundary of the alpha-convex hull are ordered
plot(x)
for (i in 1:dim(ahull.obj$arcs)[1]){
arc(ahull.obj$arcs[i,1:2],ahull.obj$arcs[i,3],ahull.obj$arcs[i,4:5],
ahull.obj$arcs[i,6],col=2)
Sys.sleep(0.5)
}
# Random sample from a uniform distribution on a Koch snowflake
# with initial side length 1 and 3 iterations
x <- rkoch(2000, side = 1, niter = 3)
# Value of alpha
alpha <- 0.05
# Alpha-convex hull
ahull.obj <- ahull(x, alpha = alpha)
plot(ahull.obj)
## End(Not run)
```

[Package *alphahull* version 2.5 Index]