# R/colorsofdata.adagrid.R In denpro: Visualization of Multivariate Functions, Sets, and Data

```colorsofdata.adagrid <- function(dendat, pcf, lst, paletti = NULL, clusterlevel=NULL, nodes=NULL){
# links from dendat to rec to node to color
# "lst\$infopointer" gives links from nodes to recs
# this version written made by Sauli Herrala

n<-dim(dendat)[1]
d<-dim(dendat)[2]
rnum<-length(pcf\$value)

if (is.null(paletti)){
paletti<-c("red","blue","green","orange","navy","darkgreen",
"orchid","aquamarine","turquoise", "pink","violet","magenta",
"chocolate","cyan", colors()[50:657],colors()[50:657])
}
# links from node to color
if ((is.null(clusterlevel))&&(is.null(nodes))) col<-colobary(lst\$parent,paletti)
if (!is.null(clusterlevel)) col<-colobary.merge(lst\$parent,lst\$level,colothre=clusterlevel,paletti)
if (!is.null(nodes)) col<-colobary.nodes(lst\$parent,nodes,paletti)

nodefinder<-matrix(0,rnum,1)
for (i in 1:rnum) nodefinder[lst\$infopointer[i]]<-i

# find links from dendat to rec
den2pcf<-matrix(0, n, 1)
pcf2den<-matrix(0, rnum, 1)
value<-matrix(0, n, 1)
ala <- pcf\$down
yla <- pcf\$high
# a bit complex
ala <- sapply(1:ncol(ala), function(x, pcf, ala) pcf\$grid[ala[, x], x], pcf = pcf, ala = ala)
yla <- sapply(1:ncol(yla), function(x, pcf, yla) pcf\$grid[yla[, x], x], pcf = pcf, yla = yla)
ala <- t(ala)
yla <- t(yla)

prc <- proc.time()
for (i in 1:n){
bol <- (c(dendat[i, ]) < ala) | (c(dendat[i,]) >  yla)
j <- which.min(colSums(bol))
den2pcf[i] <- j
pcf2den[j] <- i
value[i] <- pcf\$value[j]
}
datcol <- matrix("white",n,1)
for (i in 1:n){
eka<-den2pcf[i]
if (eka>0) tok<-nodefinder[eka]
if ((eka>0)&&(tok>0)) datcol[i]<-col[tok]
}
or<-order(value,decreasing=FALSE)
return(list(datacolo=datcol,ord=or))
}
```

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denpro documentation built on May 2, 2019, 8:55 a.m.