Type: Package
Title: Graph Edge Computations for Spatial Point Patterns
Version: 3.4
Date: 2023-02-10
Author: Tuomas Rajala
Maintainer: Tuomas Rajala <tuomas.rajala@iki.fi>
Description: Graphs (or networks) and graph component calculations for spatial locations in 1D, 2D, 3D etc.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: Rcpp (≥ 0.11.6), Matrix, methods
Suggests: rgl
LinkingTo: Rcpp
RoxygenNote: 7.2.3
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2023-02-10 11:25:29 UTC; 03130236
Repository: CRAN
Date/Publication: 2023-02-10 12:00:05 UTC

sgadj to sg

Description

sgadj to sg

Usage

adj2sg(x)

Arguments

x

sgadj object

Class creator

Description

Class creator

Usage

as.sg(edges = list(), type = "?", pars = NULL, note = NULL)

Arguments

edges

list of neighbourhoods

type

type

pars

parameters

note

notes

Creator for sgadj-class

Description

Creator for sgadj-class

Usage

as.sgadj(edges = NULL, type = "?", pars = NULL, other = "")

Arguments

edges

edge list-of-lists

type

of the graph

pars

parameters for the graph

other

other comments

Creator for sgc

Description

Creator for sgc

Usage

as.sgc(clusters, type = "?", pars = NULL, note = NULL)

Arguments

clusters

list of clusters as point indices

type

type

pars

parameters

note

notes

cut edges

Description

cut edges

Usage

## S3 method for class 'sg'
cut(x, data, R, ...)

Arguments

x

sg graph object

data

point pattern used for computing g

R

cutting length

...

ignored

Removes edges with length > R.

Edge lengths

Description

Edge lengths

Usage

edgeLengths(g, x, ...)

Arguments

g

sg-object

x

point pattern

...

ignored

verify class sg

Description

verify class sg

Usage

is_sg(x)

Arguments

x

object to check

Plot a spatial graph

Description

Rudimentary plotting.

Usage

## S3 method for class 'sg'
plot(
  x,
  data,
  which = NULL,
  add = FALSE,
  addPoints = FALSE,
  points.pch = 1,
  points.col = 1,
  points.cex = 1,
  max.edges = 10000,
  ...
)

Arguments

x

an 'sg' graph object

data

The point pattern object, same as for computing the 'g'

which

Indices of which out-edges to plot. Default: all

add

Add to existing plot? (default: FALSE)

addPoints

Add points? Will be added if add=FALSE

points.pch

point styling

points.col

point styling

points.cex

point styling

max.edges

limit of edges to try to plot, gets very slow at high count. default 1e4

...

passed to 'lines' function

plot sgadj

Description

plot sgadj

Usage

## S3 method for class 'sgadj'
plot(x, ...)

Arguments

x

sgadj object

...

passed to plot.sg

converts to sg and plots that.

plot clusters

Description

plot clusters

Usage

## S3 method for class 'sgc'
plot(x, data, atleast = 2, add = FALSE, col, ...)

Arguments

x

spatcluster-cluster object

data

point pattern object used for computing the graph

atleast

plot only cluster with 'atleast' points in them

add

add or plot new

col

colors for clusters, chosen randomly if missing.

...

passed to points

plot spectral clustering results

Description

plot spectral clustering results

Usage

## S3 method for class 'sgspectral'
plot(x, data, ...)

Arguments

x

spectral_sg result

data

point pattern

...

ignored

Plot 3d graph

Description

Plot 3d graph

Usage

plot3_sg(x, data, which, ...)

Arguments

x

sg object

data

coordinates

which

points of which out-edges will be plotted

...

passed to segments3d

Print method for sg

Description

Print sg class.

Usage

## S3 method for class 'sg'
print(x, ...)

Arguments

x

sg object

...

ignored

Details

Print basic info.

print method for sgadj

Description

print method for sgadj

Usage

## S3 method for class 'sgadj'
print(x, ...)

Arguments

x

sgadj object

...

ignored

sgc print method

Description

sgc print method

Usage

## S3 method for class 'sgc'
print(x, ...)

Arguments

x

sgc object

...

ignored

Prune a graph

Description

Prune a graph

Usage

prune_sg(g, level = 1, verbose = FALSE)

Arguments

g

sg object

level

pruning level

verbose

verbosity

Details

Remove edges from a graph by their path connectivity.

Examples

x <- matrix(runif(50*2), ncol=2)
g <- spatgraph(x, "MST")
gp <- prune_sg(g, level = 2)
plot(g, x, lty=2)
plot(gp, x, add=TRUE, col=2)

Remove edges connected to certain nodes

Description

Remove the existence of particular nodes from the graph.

Usage

remove_nodes(g, i, fuse = FALSE, verb = FALSE)

Arguments

g

sg object

i

indices of nodes for which to remove the edges

fuse

Should the neighours of removed nodes be connected?

verb

verbose?

Details

Basically, just clear the neighbourhood of selected indices. If fuse=TRUE, connect neighbours together (excluding i's). Should work over several remove nodes along a path.

Note: g should be symmetric. use sg2sym to force symmetry, it is not checked.

Warning: In development.

Examples


x <- matrix(runif(200), ncol=2)
g <- spatgraph(x, "RST", c(1,0))
g <- sg2sym(g)
i <- sample(100, 50)
k <- setdiff(1:100, i)
gs <- remove_nodes(g, i, fuse=TRUE)
plot(g,x, add=FALSE)
points(x[k,], pch=19, col=4)
plot(gs, x, add=TRUE, lty=2, col=3)

sg to sgadj

Description

sg to sgadj

Usage

sg2adj(x)

Arguments

x

sg object

sg to dxf format

Description

sg to dxf format

Usage

sg2dxf(g, x, file)

Arguments

g

sg object

x

pattern object used for computing g

file

filename for output

sg to igraph

Description

Obsolete. Use igraph::graph_from_adj_list on the graph edges element.

Usage

sg2igraph(g, x, ...)

Arguments

g

sg object

x

possibly the location pattern

...

not used

Not implemented. You can use the 'graph_from_adj_list'-function in 'igraph'-package on the edges-element of the graph.

Examples

## Not run: 
ix <- igraph::graph_from_adj_list(x$edges)

## End(Not run)

Make a sparse adjacency matrix from sg-object

Description

Make a sparse adjacency matrix from sg-object

Usage

sg2sparse(x)

Arguments

x

sg-object

Symmetrisation of sg adjacency matrix wrapper for 1way and 2way symmetrisation

Description

Symmetrisation of sg adjacency matrix wrapper for 1way and 2way symmetrisation

Usage

sg2sym(x, way = 1)

Arguments

x

sg object

way

1: OR rule, 2: AND rule for keeping edges.

weighted sg to weighted adjacency matrix

Description

weighted sg to weighted adjacency matrix

Usage

sg2wadj(x)

Arguments

x

weighted sg object

Parse input for coordinates

Description

Extract the coordinate locations from the input object.

Usage

sg_parse_coordinates(x, verbose = FALSE)

Arguments

x

Input object containing the coordinates in some format.

verbose

Print out info of the coordinates.

Verify input parameters for the graph

Description

Mainly for internal use.

Usage

sg_verify_parameters(coord, type, par, maxR, doDists, preGraph)

Arguments

coord

Coordinates of the locations

type

Type of graph

par

Parameter(s) for the graph

maxR

Maximum range for edges, helps in large patterns.

doDists

Precompute distances? Speeds up some graphs, takes up memory.

preGraph

Precomputed graph, taken as a super-graph

shortest path on the graph

Description

Djikstra's algorithm

Usage

shortestPath(i, j, g, x = NULL, dbg = FALSE)

Arguments

i

index from

j

index to

g

sg object

x

optional point pattern from which g was computed

dbg

verbose

Make an sg-object from adjacency matrix

Description

Make an sg-object from adjacency matrix

Usage

sparse2sg(x)

Arguments

x

square matrix. non-0 elements are taken as edge presence.

Compute the connected components of a graph

Description

Compute the connected components of a graph

Usage

spatcluster(x, verbose = TRUE, sym = FALSE)

Arguments

x

sg-object

verbose

print info

sym

force symmetry of edges

Compute the edges of a spatial graph

Description

Given a spatial point pattern, we compute the edges of a graph (network) for a specified type of edge relationship.

Usage

spatgraph(
  x,
  type = "geometric",
  par = NULL,
  verbose = FALSE,
  maxR = 0,
  doDists = FALSE,
  preGraph = NULL
)

Arguments

x

Input point pattern object

type

Type of the graph

par

Parameter(s) for the graph

verbose

Print details

maxR

Maximum range for edges, helps in large patterns.

doDists

Precompute distances? Speeds up some graphs, takes up memory.

preGraph

Precomputed graph, taken as a super-graph

Details

Several edge definitions are supported:

geometric

par=numeric>0. Geometric graph, par = connection radius.

knn

par=integer>0. k-nearest neighbours graph, par = k.

mass_geometric

Connect two points if ||x-y||<m(x). par=vector giving the m(x_i)'s

markcross

Connect two points if ||x-y||<m(x)+m(y). par = vector giving the m(x_i)'s

gabriel

Gabriel graph. Additional parameter for allowing par=k instead of 0 points in the circle.

MST

Minimal spanning tree.

SIG

Spheres of Influence.

RST

Radial spanning tree, par=origin of radiation, coordinate vector

RNG

Relative neighbourhood graph

CCC

Class-Cover-Catch, par=factor vector of point types. The factor vector is converted to integers according to R's internal representation of factors, and the points with type 1 will be the target. Use relevel to change the target.

The parameter 'maxR' can be given to bring n^3 graphs closer to n^2. k-nearest neighbours will warn if maxR is too small (<k neighbours for some points), others, like RNG, don't so be careful.

Voronoi diagram aka Delaunay triangulation is not supported as other R-packages can do it, see. e.g. package 'deldir'.

Examples


# basic example
x <- matrix(runif(50*2), ncol=2)
g <- spatgraph(x, "knn", par=3)
plot(g, x)

# bigger example
xb <- matrix(runif(5000*2), ncol=2)
gb <- spatgraph(xb, "RNG", maxR=0.1)

spectral clustering

Description

spectral clustering

Usage

spectral_sg(g, m = 2, K = 3)

Arguments

g

sg object. Should be weighted (with weight_sg-function)

m

levels to consider

K

number of assumed clusters

sg summary

Description

sg summary

Usage

## S3 method for class 'sg'
summary(object, ...)

Arguments

object

sg object

...

ignored

sgc summary

Description

sgc summary

Usage

## S3 method for class 'sgc'
summary(object, ...)

Arguments

object

sgc object

...

ignored

Transpose sg object

Description

This will transpose the adjacency matrix underlying the graph. Will transform to and from sgadj-object (see 'sg2adj')

Usage

## S3 method for class 'sg'
t(x)

Arguments

x

sg-object.

Transpose sgadj object

Description

This will transpose the adjacency matrix underlying the graph.

Usage

## S3 method for class 'sgadj'
t(x)

Arguments

x

sgadj object

Set weights to edges of sg

Description

For each edge e(i,j) between points i,j, set the weight f(||x_i-x_j||)

Usage

weight_sg(g, x, f = function(x) exp(-x^2/scale), scale = 1, ...)

Arguments

g

sg object

x

point pattern used in g

f

function for the weight

scale

additional scale parameter for the default f

...

ignored

Details

Default f(x) = exp(-x^2/scale)