| SGCS-funs {SGCS} | R Documentation |
Statistical measures based on edges of a geometric graph structure over a given point pattern data.
| Date: | 2009-09-08 |
| License: | GPL v2 or later |
The graph structure is in this version geometric graph, meaning points x and y are connected
if ||x-y||<R for the given range parameter R. Notice that in clustering function, the R equals to
the parameter r.
The main function is spatial.graph.cluster.Fun, but the use of shortcuts confun, cumconfun and clustfun is highly
encouraged.
In addition, the triplet intensity function T (Schladitz & Baddeley 2000) is also included for now: The Tfun.
confun(X, r=NULL, R=NULL, h=NULL, ...)
cumconfun(X, r=NULL, R=NULL, ...)
clustfun(X, r=NULL, ...)
Tfun(X, r=NULL, ...)
spatial.graph.cluster.Fun(X, r=NULL, funtype=1, funpars=0,
minusRange=NULL, toroidal=FALSE,
doDists=FALSE, doWeights=FALSE,
prepRange=0, prepGraph=NULL, dbg=FALSE)
X |
All: Point pattern object of class ppp from package spatstat. |
r |
Vector of the range parameters in which to calculate the function value. if NULL, a range from 0 to 1/3 of window length is used. |
R |
confun, cumconfun: Clustering radius of the graph. If NULL, R=1/sqrt(lambda). |
h |
confun: Smoothing parameter in kernel-estimation. Box kernel width = 2h. NULL-> h=0.15*R |
... |
Parametes for the function art1Fun: |
funtype |
(shortcuts handle) Which function to calculate. confun=1, cumconfun=2, clustfun=3, Tfun=4. |
funpars |
(shortcuts handle) Additional function parameter(s): confun fpar=c(R,h), cumconfun fpar=R. |
minusRange |
clustfun, Tfun: Minus-correction parameter. Rectangular window required, NULL means no correction. |
toroidal |
clustfun, Tfun: Toroidal correction of rectangular window. |
doDists |
Precalculate distances for faster computation. Be aware of memory consumption n*(n-1). |
doWeights |
confun,cumconfun: Precalculate translation correction weights for faster computation. Be aware of memory consumption O(n^2). |
prepRange |
Preparation distance: If >0, precalculate a graph, from which it is easier to find needed connections. Useful with more complicated neighbourhoods than just the geometric (Maybe added in the future). |
prepGraph |
An optional graph-object from spatgraphs to be used as basis for finding connections between points. Good for big datasets. |
dbg |
Print additional messages. |
Object of class fv, see spatstat for details. Has plot, envelope etc. nice methods.
Tuomas Rajala
University of Jyvaskyla, Finland
tuomas.a.rajala@jyu.fi
Rajala, Penttinen: Spatial clustering and graph feature statistics (working title). Schladitz, Baddeley: A Third order point process characteristic, SJS, vol 27, 657-671, 2000.