Description

visCompReorder is supposed to visualise multiple component planes reorded within a sheet-shape rectangle grid

Usage

visCompReorder(sMap, sReorder, margin = rep(0.1, 4), height = 7, title.rotate = 0, 
  title.xy = c(0.45, 1), colormap = c("bwr", "jet", "gbr", "wyr", "br", "yr", "rainbow", 
          "wb"), ncolors = 40, zlim = NULL, border.color = "transparent", gp = grid::gpar(), 
      newpage = TRUE)

Arguments

sMap

an object of class "sMap"

sReorder

an object of class "sReorder"

margin

margins as units of length 4 or 1

height

a numeric value specifying the height of device

title.rotate

the rotation of the title

title.xy

the coordinates of the title

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

zlim

the minimum and maximum z 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

border.color

the border color for each hexagon

gp

an object of class "gpar". It is the output from a call to the function "gpar" (i.e., a list of graphical parameter settings)

newpage

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

Value

invisible

Note

none

Examples

# 1) generate data with an iid matrix of 1000 x 9
data <- cbind(matrix(rnorm(1000*3,mean=0,sd=1), nrow=1000, ncol=3),
matrix(rnorm(1000*3,mean=0.5,sd=1), nrow=1000, ncol=3),
matrix(rnorm(1000*3,mean=-0.5,sd=1), nrow=1000, ncol=3))
colnames(data) <- c("S1","S1","S1","S2","S2","S2","S3","S3","S3")

# 2) sMap resulted from using by default setup
sMap <- sPipeline(data=data, shape=c("suprahex","trefoil")[2])

Start at 2018-01-18 16:56:14

First, define topology of a map grid (2018-01-18 16:56:14)...
Second, initialise the codebook matrix (163 X 9) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:14)...
Third, get training at the rough stage (2018-01-18 16:56:14)...
	1 out of 2 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
	2 out of 2 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
Fourth, get training at the finetune stage (2018-01-18 16:56:14)...
	1 out of 7 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
	2 out of 7 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
	3 out of 7 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
	4 out of 7 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
	5 out of 7 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
	6 out of 7 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
	7 out of 7 (2018-01-18 16:56:14)
	updated (2018-01-18 16:56:14)
Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:14)...
Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:14)...

Below are the summaries of the training results:
   dimension of input data: 1000x9
   xy-dimension of map grid: xdim=19, ydim=19, r=10
   grid lattice: hexa
   grid shape: trefoil
   dimension of grid coord: 163x2
   initialisation method: linear
   dimension of codebook matrix: 163x9
   mean quantization error: 4.26156886077175

Below are the details of trainology:
   training algorithm: batch
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 2 at rough stage; 7 at finetune stage
   radius (at rough stage): from 5 to 1.25
   radius (at finetune stage): from 2 to 1

End at 2018-01-18 16:56:14
Runtime in total is: 0 secs


# 3) reorder component planes
sReorder <- sCompReorder(sMap=sMap, amplifier=2, metric="none")

Start at 2018-01-18 16:56:14

First, define topology of a map grid (2018-01-18 16:56:14)...
Second, initialise the codebook matrix (18 X 163) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:14)...
Third, get training at the rough stage (2018-01-18 16:56:14)...
	1 out of 180 (2018-01-18 16:56:14)
	18 out of 180 (2018-01-18 16:56:14)
	36 out of 180 (2018-01-18 16:56:14)
	54 out of 180 (2018-01-18 16:56:14)
	72 out of 180 (2018-01-18 16:56:14)
	90 out of 180 (2018-01-18 16:56:14)
	108 out of 180 (2018-01-18 16:56:14)
	126 out of 180 (2018-01-18 16:56:14)
	144 out of 180 (2018-01-18 16:56:14)
	162 out of 180 (2018-01-18 16:56:14)
	180 out of 180 (2018-01-18 16:56:14)
Fourth, get training at the finetune stage (2018-01-18 16:56:14)...
	1 out of 720 (2018-01-18 16:56:14)
	72 out of 720 (2018-01-18 16:56:14)
	144 out of 720 (2018-01-18 16:56:14)
	216 out of 720 (2018-01-18 16:56:14)
	288 out of 720 (2018-01-18 16:56:14)
	360 out of 720 (2018-01-18 16:56:14)
	432 out of 720 (2018-01-18 16:56:14)
	504 out of 720 (2018-01-18 16:56:14)
	576 out of 720 (2018-01-18 16:56:14)
	648 out of 720 (2018-01-18 16:56:14)
	720 out of 720 (2018-01-18 16:56:14)
Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:14)...
Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:14)...

Below are the summaries of the training results:
   dimension of input data: 9x163
   xy-dimension of map grid: xdim=6, ydim=3, r=3
   grid lattice: rect
   grid shape: sheet
   dimension of grid coord: 18x2
   initialisation method: linear
   dimension of codebook matrix: 18x163
   mean quantization error: 32.521897311002

Below are the details of trainology:
   training algorithm: sequential
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 20 at rough stage; 80 at finetune stage
   radius (at rough stage): from 1 to 1
   radius (at finetune stage): from 1 to 1

End at 2018-01-18 16:56:14
Runtime in total is: 0 secs


# 4) visualise multiple component planes reorded within a sheet-shape rectangle grid
visCompReorder(sMap=sMap, sReorder=sReorder, margin=rep(0.1,4),
height=7,
title.rotate=0, title.xy=c(0.45, 1), colormap="gbr", ncolors=10,
zlim=c(-1,1),
border.color="transparent")

Source code

Source man

See also