Reorganize matrix according to clustering-output
Reorganize input matrix as sorted by cluster numbers (and geometric mean) according to vector with cluster names, and index for sorting per cluster and per geometric mean.
In case mat
is an array, the 3rd dimension will be considered as 'column' with arguments useColumn
( and cluNo
, if it designs a 'column' of mat).
reorgByCluNo( mat, cluNo, useColumn = NULL, meanCol = NULL, retList = FALSE, silent = FALSE, callFrom = NULL )
mat |
(matrix or data.frame) main input |
cluNo |
(positive integer, length to match nrow(dat) initial cluster numbers for each line of 'mat' (obtained by separate clustering or other segmentation) or may desinn column of |
useColumn |
(character or integer) the columns to use from mat as main data (default will use all, exept |
meanCol |
(character or integer) alternative summarizing data for intra-cluster sorting (instead of geometric mean) |
retList |
(logical) |
silent |
(logical) suppress messages |
callFrom |
(character) allow easier tracking of messages produced |
list or array (as 2- or 3 dim) with possible number of occurances for each of the 3 elements in nMax. Read results vertical : out[[1]] or out[,,1] .. (multiplicative) table for 1st element of nMax; out[,,2] .. for 2nd
dat1 <- matrix(round(runif(24),2), ncol=3, dimnames=list(NULL,letters[1:3])) clu <- stats::kmeans(dat1, 5)$cluster reorgByCluNo(dat1, clu) dat2 <- cbind(dat1, clu=clu) reorgByCluNo(dat2, "clu")
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