Description

visDmatCluster is supposed to visualise clusters/bases partitioned from a supra-hexagonal grid

Usage

visDmatCluster(sMap, sBase, height = 7, margin = rep(0.1, 4), area.size = 1, gp = grid::gpar(cex = 0.8, 
  font = 2, col = "black"), border.color = "transparent", fill.color = NULL, lty = 1, 
      lwd = 1, lineend = "round", linejoin = "round", colormap = c("rainbow", "jet", 
          "bwr", "gbr", "wyr", "br", "yr", "wb"), clip = c("on", "inherit", "off"), 
      newpage = TRUE)

Arguments

sMap

an object of class "sMap"

sBase

an object of class "sBase"

height

a numeric value specifying the height of device

margin

margins as units of length 4 or 1

area.size

an inteter or a vector specifying the area size of 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)

border.color

the border color for each hexagon

fill.color

the filled color for each hexagon

lty

the line type for each hexagon. 0 for 'blank', 1 for 'solid', 2 for 'dashed', 3 for 'dotted', 4 for 'dotdash', 5 for 'longdash', 6 for 'twodash'

lwd

the line width for each hexagon

lineend

the line end style for each hexagon. It can be one of 'round', 'butt' and 'square'

linejoin

the line join style for each hexagon. It can be one of 'round', 'mitre' and 'bevel'

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

clip

either "on" for clipping to the extent of this viewport, "inherit" for inheriting the clipping region from the parent viewport, or "off" to turn clipping off altogether

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 an iid normal random matrix of 100x10 
data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10)

# 2) get trained using by default setup
sMap <- sPipeline(data=data)

Start at 2018-01-18 16:56:15

First, define topology of a map grid (2018-01-18 16:56:15)...
Second, initialise the codebook matrix (61 X 10) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:15)...
Third, get training at the rough stage (2018-01-18 16:56:15)...
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Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:15)...
Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:15)...

Below are the summaries of the training results:
   dimension of input data: 100x10
   xy-dimension of map grid: xdim=9, ydim=9, r=5
   grid lattice: hexa
   grid shape: suprahex
   dimension of grid coord: 61x2
   initialisation method: linear
   dimension of codebook matrix: 61x10
   mean quantization error: 4.74358615221645

Below are the details of trainology:
   training algorithm: batch
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 7 at rough stage; 25 at finetune stage
   radius (at rough stage): from 3 to 1
   radius (at finetune stage): from 1 to 1

End at 2018-01-18 16:56:16
Runtime in total is: 1 secs


# 3) partition the grid map into clusters using region-growing algorithm
sBase <- sDmatCluster(sMap=sMap, which_neigh=1,
distMeasure="median", clusterLinkage="average")

# 4) visualise clusters/bases partitioned from the sMap
visDmatCluster(sMap,sBase)
# 4a) also, the area size is proportional to the hits
visDmatCluster(sMap,sBase, area.size=log2(sMap$hits+1))
# 4b) also, the area size is inversely proportional to the map distance
dMat <- sDmat(sMap)


visDmatCluster(sMap,sBase, area.size=-1*log2(dMat))

# 5) customise the fill color and line type
my_color <-
visColormap(colormap="PapayaWhip-pink-Tomato")(length(sBase$seeds))[sBase$bases]


my_lty <- (sBase$bases %% 2)
visDmatCluster(sMap,sBase, fill.color=my_color, lty=my_lty,
border.color="black", lwd=2, area.size=0.9)
# also, the area size is inversely proportional to the map distance
visDmatCluster(sMap,sBase, fill.color=my_color, lty=my_lty,
border.color="black", lwd=2, area.size=-1*log2(dMat))

Source code

Source man

See also