R: Compute an Adjacency Matrix for a graphBAM object
adjacencyMatrix
R Documentation
Compute an Adjacency Matrix for a graphBAM object
Description
Though unwieldy for large matrices, a full adjacency matrix
can be useful for debugging and export.
If the graph is “undirected” then recicprocal edges
are explicit in the matrix.
Usage
adjacencyMatrix(object)
Arguments
object
A graphBAM object.
Details
Thus far only implemented for graphBAM objects.
Value
adjacencyMatrix returns an n x n matrix, where n is
the number of nodes in the graph, ordered in the same manner as
seen in the nodes method. All cells in the matrix are 0
except where edges are found.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(graph)
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/graph/adjacencyMatrix.Rd_%03d_medium.png", width=480, height=480)
> ### Name: adjacencyMatrix
> ### Title: Compute an Adjacency Matrix for a graphBAM object
> ### Aliases: adjacencyMatrix adjacencyMatrix,graphBAM-method
> ### Keywords: manip
>
> ### ** Examples
>
> from <- c("a", "a", "a", "x", "x", "c")
> to <- c("b", "c", "x", "y", "c", "a")
> weight <- c(3.4, 2.6, 1.7, 5.3, 1.6, 7.9)
> df <- data.frame(from, to, weight)
> g1 <- graphBAM(df, edgemode = "directed")
> adjacencyMatrix(g1)
a b c x y
a 0 1 1 1 0
b 0 0 0 0 0
c 1 0 0 0 0
x 0 0 1 0 1
y 0 0 0 0 0
>
>
>
>
>
> dev.off()
null device
1
>