data(morsecodes)
morsecodes.raw |
36 x 36 raw data of confusion rates |
morsecodes.dist |
36 x 36 dissimilarity matrix |
morsecodes.pos |
36 x 10 initial configuration |
morsecodes.colors |
36 point colors |
morsecodes.glyphs |
36 point glyphs |
morsecodes.lines |
33 lines |
morsecodes.linecolors |
33 line colors |
morsecodes.row |
36 x 2 matrix of (letter, morsecode) |
mc.raw <- as.matrix(read.table("....xgobi/data_xgvis/morsecodes.raw"))
dimnames(mc.raw) <- NULL; storage.mode(mc.raw) <- "integer"
morsecodes.raw <- mc.raw
. The *.dist
matrix is produced from the raw data by
mc.sim <- (mc.raw + t(mc.raw))/2 ; ds <- diag(mc.sim)
morsecodes.dist <- rep(ds,36) + rep(ds,rep(36,36)) - 2*mc.sim
,
i.e., $d[i,j] := s[i,i] +
s[j,j] - 2 * s[i,j]$.
data(morsecodes)
ls.str(pat="^morsecode")
morsecodes.row # remember what you learned in...
## real row names
(mc.row <- paste(morsecodes.row[,1], morsecodes.row[,2]))[1:8]
image(1:36, 1:36, morsecodes.raw, main="`morsecodes' raw confusion rates")
text(1:36,1:36, morsecodes.row[,1])
##--> help(xgvis) for running multidimensional scaling (MDS) and XGobi on these
Run the code above in your browser using DataLab