Description

visNetReorder is supposed to visualise the multiple graph colorings reorded within a sheet-shape rectangle grid

Usage

visNetReorder(g, data, sReorder, height = 7, margin = rep(0.1, 4), border.color = "#EEEEEE", 
  colormap = c("bwr", "jet", "gbr", "wyr", "br", "yr", "rainbow", "wb"), ncolors = 40, 
      zlim = NULL, colorbar = T, colorbar.fraction = 0.5, newpage = T, glayout = layout.fruchterman.reingold, 
      mtext.side = 3, mtext.adj = 0, mtext.cex = 1, mtext.font = 2, mtext.col = "black", 
      ...)

Arguments

g

an object of class "igraph" or "graphNEL"

data

an input data matrix used to color-code vertices/nodes. One column corresponds to one graph node coloring. The input matrix must have row names, and these names should include all node names of input graph, i.e. V(g)$name, since there is a mapping operation. After mapping, the length of the pattern vector should be the same as the number of nodes of input graph. The way of how to color-code is to map values in the pattern onto the whole colormap (see the next arguments: colormap, ncolors, zlim and colorbar)

sReorder

an object of class "sReorder"

height

a numeric value specifying the height of device

margin

margins as units of length 4 or 1

border.color

the border color of each figure

colormap

short name for the colormap. It can be one of "jet" (jet colormap), "bwr" (blue-white-red colormap), "gbr" (green-black-red colormap), "wyr" (white-yellow-red colormap), "br" (black-red colormap), "yr" (yellow-red colormap), "wb" (white-black colormap), and "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta). Alternatively, any hyphen-separated HTML color names, e.g. "blue-black-yellow", "royalblue-white-sandybrown", "darkgreen-white-darkviolet". A list of standard color names can be found in http://html-color-codes.info/color-names

ncolors

the number of colors specified over the colormap

zlim

the minimum and maximum z/patttern values for which colors should be plotted, defaulting to the range of the finite values of z. Each of the given colors will be used to color an equispaced interval of this range. The midpoints of the intervals cover the range, so that values just outside the range will be plotted

colorbar

logical to indicate whether to append a colorbar. If pattern is null, it always sets to false

colorbar.fraction

the relative fraction of colorbar block against the figure block

newpage

logical to indicate whether to open a new page. By default, it sets to true for opening a new page

glayout

either a function or a numeric matrix configuring how the vertices will be placed on the plot. If layout is a function, this function will be called with the graph as the single parameter to determine the actual coordinates. This function can be one of "layout.auto", "layout.random", "layout.circle", "layout.sphere", "layout.fruchterman.reingold", "layout.kamada.kawai", "layout.spring", "layout.reingold.tilford", "layout.fruchterman.reingold.grid", "layout.lgl", "layout.graphopt", "layout.svd" and "layout.norm". A full explanation of these layouts can be found in http://igraph.org/r/doc/layout_nicely.html

mtext.side

on which side of the mtext plot (1=bottom, 2=left, 3=top, 4=right)

mtext.adj

the adjustment for mtext alignment (0 for left or bottom alignment, 1 for right or top alignment)

mtext.cex

the font size of mtext labels

mtext.font

the font weight of mtext labels

mtext.col

the color of mtext labels

...

additional graphic parameters. See http://igraph.org/r/doc/plot.common.html for the complete list.

Value

invisible

Note

none

Examples

# 1) generate a random graph according to the ER model
g <- erdos.renyi.game(100, 1/100)

# 2) produce the induced subgraph only based on the nodes in query
subg <- dNetInduce(g, V(g), knn=0)

# 3) reorder the module with vertices being color-coded by input data
nnodes <- vcount(subg)
nsamples <- 10
data <- matrix(runif(nnodes*nsamples), nrow=nnodes, ncol=nsamples)
rownames(data) <- V(subg)$name
sReorder <- dNetReorder(g=subg, data, feature="node",
node.normalise="none")

Start at 2018-01-19 12:39:19

First, define topology of a map grid (2018-01-19 12:39:19)...
Second, initialise the codebook matrix (36 X 10) using 'linear' initialisation, given a topology and input data (2018-01-19 12:39:19)...
Third, get training at the rough stage (2018-01-19 12:39:19)...
	1 out of 360 (2018-01-19 12:39:19)
	36 out of 360 (2018-01-19 12:39:19)
	72 out of 360 (2018-01-19 12:39:19)
	108 out of 360 (2018-01-19 12:39:19)
	144 out of 360 (2018-01-19 12:39:19)
	180 out of 360 (2018-01-19 12:39:19)
	216 out of 360 (2018-01-19 12:39:19)
	252 out of 360 (2018-01-19 12:39:19)
	288 out of 360 (2018-01-19 12:39:19)
	324 out of 360 (2018-01-19 12:39:19)
	360 out of 360 (2018-01-19 12:39:19)
Fourth, get training at the finetune stage (2018-01-19 12:39:19)...
	1 out of 1440 (2018-01-19 12:39:19)
	144 out of 1440 (2018-01-19 12:39:19)
	288 out of 1440 (2018-01-19 12:39:20)
	432 out of 1440 (2018-01-19 12:39:20)
	576 out of 1440 (2018-01-19 12:39:20)
	720 out of 1440 (2018-01-19 12:39:20)
	864 out of 1440 (2018-01-19 12:39:20)
	1008 out of 1440 (2018-01-19 12:39:20)
	1152 out of 1440 (2018-01-19 12:39:20)
	1296 out of 1440 (2018-01-19 12:39:20)
	1440 out of 1440 (2018-01-19 12:39:20)
Next, identify the best-matching hexagon/rectangle for the input data (2018-01-19 12:39:20)...
Finally, append the response data (hits and mqe) into the sMap object (2018-01-19 12:39:20)...

Below are the summaries of the training results:
   dimension of input data: 10x10
   xy-dimension of map grid: xdim=6, ydim=6, r=3
   grid lattice: rect
   grid shape: sheet
   dimension of grid coord: 36x2
   initialisation method: linear
   dimension of codebook matrix: 36x10
   mean quantization error: 0.232787363343837

Below are the details of trainology:
   training algorithm: sequential
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 36 at rough stage; 144 at finetune stage
   radius (at rough stage): from 1 to 1
   radius (at finetune stage): from 1 to 1

End at 2018-01-19 12:39:20
Runtime in total is: 1 secs


# 4) visualise the module with vertices being color-coded by input data
visNetReorder(g=subg, colormap="bwr", data=data, sReorder)

Source code

Source man

See also