Function to visualise a supra-hexagonal grid
Description
visHexGrid is supposed to visualise a supra-hexagonal grid
Usage
visHexGrid(hbin, area.size = 1, border.color = NULL, fill.color = NULL, lty = 1, lwd = 1, lineend = "round", linejoin = "round")
Arguments
- hbin
- an object of class "hexbin"
- area.size
- an inteter or a vector specifying the area size of each hexagon
- 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'
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) colnames(data) <- paste(rep('S',10), seq(1:10), sep="") # 2) sMap resulted from using by default setup sMap <- sPipeline(data=data)Start at 2018-01-18 16:56:28 First, define topology of a map grid (2018-01-18 16:56:28)... Second, initialise the codebook matrix (61 X 10) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:28)... Third, get training at the rough stage (2018-01-18 16:56:28)... 1 out of 7 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 2 out of 7 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 3 out of 7 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 4 out of 7 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 5 out of 7 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 6 out of 7 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 7 out of 7 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) Fourth, get training at the finetune stage (2018-01-18 16:56:28)... 1 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 2 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 3 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 4 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 5 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 6 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 7 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 8 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 9 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 10 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 11 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 12 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 13 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 14 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 15 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 16 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 17 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 18 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 19 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 20 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 21 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 22 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 23 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 24 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) 25 out of 25 (2018-01-18 16:56:28) updated (2018-01-18 16:56:28) Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:28)... Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:28)... 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: 4.9630432442287 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 16:56:28 Runtime in total is: 0 secs# 3) create an object of "hexbin" class from sMap dat <- data.frame(sMap$coord) xdim <- sMap$xdim ydim <- sMap$ydim hbin <- hexbin::hexbin(dat$x, dat$y, xbins=xdim-1, shape=sqrt(0.75)*ydim/xdim) # 4) visualise hbin object vp <- hexbin::hexViewport(hbin) visHexGrid(hbin)
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