Description

visHexBarplot is supposed to visualise codebook matrix using barplot for all hexagons or a specific one

Usage

visHexBarplot(sObj, which.hexagon = NULL, which.hexagon.highlight = NULL, height = 7, 
  margin = rep(0.1, 4), colormap = c("customized", "bwr", "jet", "gbr", "wyr", 
          "br", "yr", "rainbow", "wb"), customized.color = "red", zeropattern.color = "gray", 
      gp = grid::gpar(cex = 0.7, font = 1, col = "black"), bar.text.cex = 0.8, bar.text.srt = 90, 
      newpage = TRUE)

Arguments

sObj

an object of class "sMap" or "sTopol" or "sInit"

which.hexagon

the integer specifying which hexagon to display. If NULL, all hexagons will be visualised

which.hexagon.highlight

an integer vector specifying which hexagons are labelled. If NULL, all hexagons will be labelled

height

a numeric value specifying the height of device

margin

margins as units of length 4 or 1

colormap

short name for the predifined colormap, and "customized" for custom input (see the next 'customized.color'). The predifined colormap 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

customized.color

the customized color for pattern visualisation

zeropattern.color

the color for zero horizental line

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)

bar.text.cex

a numerical value giving the amount by which bar text should be magnified relative to the default (i.e., 1)

bar.text.srt

a numerical value giving the angle by which bar text should be orientated

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)

Start at 2018-01-18 16:56:26

First, define topology of a map grid (2018-01-18 16:56:26)...
Second, initialise the codebook matrix (169 X 9) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:26)...
Third, get training at the rough stage (2018-01-18 16:56:26)...
	1 out of 2 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
	2 out of 2 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
Fourth, get training at the finetune stage (2018-01-18 16:56:26)...
	1 out of 7 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
	2 out of 7 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
	3 out of 7 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
	4 out of 7 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
	5 out of 7 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
	6 out of 7 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
	7 out of 7 (2018-01-18 16:56:26)
	updated (2018-01-18 16:56:26)
Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:26)...
Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:26)...

Below are the summaries of the training results:
   dimension of input data: 1000x9
   xy-dimension of map grid: xdim=15, ydim=15, r=8
   grid lattice: hexa
   grid shape: suprahex
   dimension of grid coord: 169x2
   initialisation method: linear
   dimension of codebook matrix: 169x9
   mean quantization error: 4.27816289846339

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 4 to 1
   radius (at finetune stage): from 1 to 1

End at 2018-01-18 16:56:26
Runtime in total is: 0 secs


# 3) plot codebook patterns using different types
# 3a) for all hexagons
visHexBarplot(sMap)
# 3b) only for the first hexagon
visHexBarplot(sMap, which.hexagon=1)

Source code

Source man

See also