`visCompReorder`

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

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)

- 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

invisible

none

# 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 16:56:14First, 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: 4.26156886077175Below 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 1End at 2018-01-18 16:56:14Runtime in total is: 0 secs# 3) reorder component planes sReorder <- sCompReorder(sMap=sMap, amplifier=2, metric="none")Start at 2018-01-18 16:56:14First, 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: 32.521897311002Below 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 1End at 2018-01-18 16:56:14Runtime 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")

`visCompReorder.r`

`visCompReorder.Rd`

`visCompReorder.pdf`

`visVp`

, `visHexComp`

,
`visColorbar`

, `sCompReorder`