NNCH {adehabitat} | R Documentation |
NNCH
computes the home range of several animals using the
LoCoH family of methods.
NNCH(xy, id = NULL, k = c(10), unin = c("m", "km"), unout = c("m2", "ha", "km2"), status = FALSE, duplicates = 1, hog.limit = 500, r = NULL, a = NULL, min.k = NULL, max.k = NULL)
xy |
a data frame containing the coordinates of the relocations of the monitored animals |
id |
a factor giving the identity of the animal for each relocation |
k |
if defined, the Fixed k LoCoH algorithm (k-NNCH) is used; the number of nearest neighbors minus one out of which to create convex hulls |
r |
if defined, the Fixed r LoCoH algorithm is used; the convex hulls are created out of all points within r distance from the root points |
a |
if defined, the Adaptive LoCoH algorithm is used; create convex hulls from the maximum number of nearest neighbors such that the sum of their distances is less than or equal to a |
unin |
the units of the relocations coordinates. Either
"m" for meters or "km" for kilometers |
unout |
the units of the output areas. Either "m2" for
square meters, "km2" for square kilometers or "ha" for
hectares |
status |
if TRUE print out occasional progress messages as
we analyze the data |
duplicates |
a setting to determine how duplicated points are
handled. If a number, duplicated points, are displaces
duplicates amount in a random direction, if "delete"
all but one copy of duplicated points are deleted, if
"ignore" no special handling of duplicated points (could
create zero area hulls) |
hog.limit |
if less than the number of points, a slow but memory efficient algorithm is used |
min.k |
for use with Fixed r LoCoH and Adaptive LoCoH a "floor" for the value of k (ie. if the value of k found using the algorithm is less than min.k, set k equal to min.k) |
max.k |
for use with Fixed r LoCoH and Adaptive LoCoH a "ceiling" for the value of k (ie. if the value of k found using the algorithm is more than max.k, set k equal to max.k) |
NNCH
returns a list of class NNCH
.
These functions require the package gpclib
.
The LoCoH family of methods for locating Utilization Distributions
consists of three algorithms: Fixed k LoCoH, Fixed r LoCoH, and
Adaptive LoCoH. All the algorithms work by constructing a small convex
hull for each point, and then incrementally merging the hulls together
from smallest to largest into isopleths. The 10% isopleth contains
10% of the points and represents a higher utilization the the 100%
isopleth that contains all the points.
Fixed k LoCoH: Also known as k-NNCH, Fixed k LoCoH is described
in Getz and Willmers (2004). The convex hull for each point is
constructed from the (k-1) nearest neighbors to that point. Hulls are
merged together from smallest to largest based on the area of the
hull.
Fixed r LoCoH: In this case, hulls are created from all points
within r
distance of the root point. When merging hulls, the
hulls are primarily sorted by the value of k generated for each hull
(the number of points contained in the hull), and secondly by the area
of the hull.
Adaptive LoCoH: Here, hulls are created out of the maximum
nearest neighbors such that the sum of the distances from the nearest
neighbors is less than or equal to d. Use the same hull sorting as
Fixed r LoCoH.
Fixed r LoCoH and Adaptive LoCoH are discussed in a forthcoming paper
(Getz et al).
All of these algorithms can take a signifigant amount of time. Time
taken increases exponentially with the size of the data set.
Scott Fortmann-Roe scottfr@gmail.com
Clement Calenge clement.calenge@oncfs.gouv.fr
Getz, W.M. & Wilmers, C.C. (2004). A local nearest-neighbor convex-hull
construction of home ranges and utilization
distributions. Ecography, in press.
Getz, W.M., Fortmann-Roe, S.B, Lyons, A., Ryan, S., Cross, P. (in
preparation). LoCoH methods for the construction of home
ranges and utilization distributions. in preparation.
NNCH.select
for plotting, rasterization,
conversion to shapefiles, and management of the objects of class
NNCH
. NNCH.area
for functions computing the
homerange area.
## Not run: data(chamois) xy <- chamois$locs (nn <- NNCH(xy, k=c(6,7))) summary(nn) NNCH.select(nn, k=7) ## Graphical exploration plot(nn, k=7) ## rasterization: asc <- ascgen(chamois$locs,nrcol=100) asc <- NNCH.asciigrid(nn, k=7, asc=asc) image(asc) ## End(Not run)