1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scan.R
\name{local_scan}
\alias{local_scan}
\title{Compute local scan statistics on graphs}
\usage{
local_scan(
graph.us,
graph.them = NULL,
k = 1,
FUN = NULL,
weighted = FALSE,
mode = c("out", "in", "all"),
neighborhoods = NULL,
...
)
}
\arguments{
\item{graph.us, graph}{An igraph object, the graph for which the scan
statistics will be computed}
\item{graph.them}{An igraph object or \code{NULL}, if not \code{NULL},
then the \sQuote{them} statistics is computed, i.e. the neighborhoods
calculated from \code{graph.us} are evaluated on \code{graph.them}.}
\item{k}{An integer scalar, the size of the local neighborhood for each
vertex. Should be non-negative.}
\item{FUN}{Character, a function name, or a function object itself, for
computing the local statistic in each neighborhood. If \code{NULL}(the
default value), \code{ecount()} is used for unweighted graphs (if
\code{weighted=FALSE}) and a function that computes the sum of edge
weights is used for weighted graphs (if \code{weighted=TRUE}). This
argument is ignored if \code{k} is zero.}
\item{weighted}{Logical scalar, TRUE if the edge weights should be used
for computation of the scan statistic. If TRUE, the graph should be
weighted. Note that this argument is ignored if \code{FUN} is not
\code{NULL}, \code{"ecount"} and \code{"sumweights"}.}
\item{mode}{Character scalar, the kind of neighborhoods to use for the
calculation. One of \sQuote{\code{out}}, \sQuote{\verb{in}},
\sQuote{\code{all}} or \sQuote{\code{total}}. This argument is ignored
for undirected graphs.}
\item{neighborhoods}{A list of neighborhoods, one for each vertex, or
\code{NULL}. If it is not \code{NULL}, then the function is evaluated on
the induced subgraphs specified by these neighborhoods.
In theory this could be useful if the same \code{graph.us} graph is used
for multiple \code{graph.them} arguments. Then the neighborhoods can be
calculated on \code{graph.us} and used with multiple graphs. In
practice, this is currently slower than simply using \code{graph.them}
multiple times.}
\item{\dots}{Arguments passed to \code{FUN}, the function that computes
the local statistics.}
}
\value{
For \code{local_scan()} typically a numeric vector containing the
computed local statistics for each vertex. In general a list or vector
of objects, as returned by \code{FUN}.
}
\description{
The scan statistic is a summary of the locality statistics that is
computed from the local neighborhood of each vertex. The
\code{local_scan()} function computes the local statistics for each vertex
for a given neighborhood size and the statistic function.
}
\details{
See the given reference below for the details on the local scan
statistics.
\code{local_scan()} calculates exact local scan statistics.
If \code{graph.them} is \code{NULL}, then \code{local_scan()} computes the
\sQuote{us} variant of the scan statistics. Otherwise,
\code{graph.them} should be an igraph object and the \sQuote{them}
variant is computed using \code{graph.us} to extract the neighborhood
information, and applying \code{FUN} on these neighborhoods in
\code{graph.them}.
}
\examples{
pair <- sample_correlated_gnp_pair(n = 10^3, corr = 0.8, p = 0.1)
local_0_us <- local_scan(graph.us = pair$graph1, k = 0)
local_1_us <- local_scan(graph.us = pair$graph1, k = 1)
local_0_them <- local_scan(
graph.us = pair$graph1,
graph.them = pair$graph2, k = 0
)
local_1_them <- local_scan(
graph.us = pair$graph1,
graph.them = pair$graph2, k = 1
)
Neigh_1 <- neighborhood(pair$graph1, order = 1)
local_1_them_nhood <- local_scan(
graph.us = pair$graph1,
graph.them = pair$graph2,
neighborhoods = Neigh_1
)
}
\references{
Priebe, C. E., Conroy, J. M., Marchette, D. J., Park,
Y. (2005). Scan Statistics on Enron Graphs. \emph{Computational and
Mathematical Organization Theory}.
}
\seealso{
Other scan statistics:
\code{\link{scan_stat}()}
}
\concept{scan statistics}
|
