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Various EDA topics hierarchical clustering

R Software Module: rwasp_hierarchicalclustering.wasp (opens new window with default values)
Title produced by software: Hierarchical Clustering
Date of computation: Sat, 03 Nov 2007 03:04:34 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/03/xao14brzm7vunkr1194084212.htm/, Retrieved Sat, 03 Nov 2007 11:03:32 +0100
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
91,19 1,79 90,49 91,25 91,53 1,95 90,65 91,5 91,88 2,26 90,95 91,68 92,06 2,04 91,19 91,81 92,32 2,16 91,07 91,84 92,67 2,75 91,15 91,93 92,85 2,79 91,81 92,08 92,82 2,88 91,95 92,11 93,46 3,36 91,62 92,26 93,23 2,97 91,27 92,28 93,54 3,1 91,4 92,39 93,29 2,49 91,76 92,46 93,2 2,2 91,99 92,82 93,6 2,25 92,34 93,16 93,81 2,09 92,3 93,33 94,62 2,79 92,85 93,51 95,22 3,14 92,94 93,56 95,38 2,93 93,26 93,67 95,31 2,65 94,21 93,76 95,3 2,67 94,08 93,88 95,57 2,26 93,98 94,01 95,42 2,35 94,23 94,21 95,53 2,13 94,93 94,31 95,33 2,18 95,09 94,4 95,9 2,9 95,37 94,9 96,06 2,63 96,23 95,31 96,31 2,67 96,2 95,52 96,34 1,81 95,43 95,68 96,49 1,33 95,63 95,91 96,22 0,88 95,96 95,97 96,53 1,28 96,51 96,15 96,5 1,26 96,65 96,34 96,77 1,26 96,21 96,42 96,66 1,29 95,54 96,54 96,58 1,1 95,96 96,72 96,63 1,37 96,41 96,81 97,06 1,21 96,32 97,19 97,73 1,74 96,94 97,5 98,01 1,76 96,97 97,71 97,76 1,48 97,63 97,86 97,49 1,04 97,33 98,04 97,77 1,62 97,66 98,2 97,96 1,49 98,18 98,25 98,23 1,79 98,22 98,41 98,51 1,8 97,91 98,56 98,19 1,58 97,93 98,62 98,37 1,86 98,4 98,75 98,31 1,74 98,78 98,71 98,6 1,59 98,73 99,05 98,97 1,26 99,4 99,52 99,11 1,13 99,04 99,71 99,64 1,92 99,68 99,8 100,03 2,61 99,62 100,01 99,98 2,26 99,8 99,99 100,32 2,41 100,65 100,12 100,44 2,26 100,59 100,15 100,51 2,03 100,46 100,27 101 2,86 100,57 100,42 100,88 2,55 100,75 100,43 100,55 2,27 100,7 100,5 100,83 2,26 101,44 100,95 101,51 2,57 101,77 101,26 102,16 3,07 101,79 101,42 102,39 2,76 101,52 101,68 102,54 2,51 101,83 101,75 102,85 2,87 102,23 101,89 103,47 3,14 103,04 102,07 103,57 3,11 102,81 102,22 103,69 3,16 102,48 102,45 103,5 2,47 102,81 102,62 103,47 2,57 103,21 102,67 103,45 2,89 103,21 102,86 103,48 2,63 102,92 104,78 103,93 2,38 103,48 104,87 103,89 1,69 103,18 105,06 104,4 1,96 103,39 105,14 104,79 2,19 103,5 105,32 104,77 1,87 103,73 105,54 105,13 1,6 104,42 105,68 105,26 1,63 104,53 105,77 104,96 1,22 104,09 106,07 104,75 1,21 104,23 106,03 105,01 1,49 104,23 106,13 105,15 1,64 104,54 106,28 105,2 1,66 104,65 106,61 105,77 1,77 105,48 106,74 105,78 1,82 105,61 107,01 106,26 1,78 105,74 107,1 106,13 1,28 105,86 107,28 106,12 1,29 105,81 107,4 106,57 1,37 106,49 107,59 106,44 1,12 106,43 107,69 106,54 1,51 105,73 107,78
 
Text written by user:
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Summary of Dendrogram
LabelHeight
10.130766968306223
20.171755640373177
30.178325545001261
40.194935886896180
50.203469899493762
60.238746727726265
70.255734237050882
80.276043474836846
90.293768616431372
100.303973683071421
110.304959013639532
120.311929479209639
130.316543835826895
140.330302891298271
150.351852241715181
160.351994318135959
170.364828726939084
180.369052841744922
190.372692903071683
200.373229152130431
210.378153408023786
220.387405069332897
230.393446311458127
240.397240481320821
250.404969134626328
260.411946598480926
270.429185274677503
280.431277173056959
290.436004587131837
300.449666543118341
310.451774279923069
320.462817458616243
330.478852795752521
340.487783318313371
350.508559886434628
360.51180233932589
370.517637330584877
380.524118307255142
390.56091018857521
400.573166176263019
410.588047617119556
420.614766282981192
430.647604054609399
440.65849585917573
450.677551375494143
460.717601896939927
470.765963445602991
480.769510210727266
490.786826540521206
500.8018896622614
510.808073761768816
520.830651362061792
530.873784870548815
540.917962496264222
550.952723367665576
560.961652972325255
570.962849556545336
580.97286865387
590.992305849553476
601.18760587778932
611.25763507628243
621.31707788529777
631.34443918446295
641.36923593597606
651.41470282977280
661.42941643411828
671.52937803354863
681.58440913836598
691.60538874416135
701.68784639877682
711.85468098568693
722.09496903749675
732.19045739986997
742.39695347159423
752.45638699762216
762.85945517230632
772.87132856814416
783.74449652002009
794.60316542400409
805.11048099530347
816.14028005209879
826.18512306326494
837.54924490825187
848.6510723223087
858.79775785636548
8620.6690647872988
8724.7131488139645
8828.6642567954587
8959.7307523786856
9071.8583966588585
91157.16024566812
92411.100644802032
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/03/xao14brzm7vunkr1194084212/1ul2c1194084266.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/03/xao14brzm7vunkr1194084212/1ul2c1194084266.ps (open in new window)


 
Parameters:
par1 = ward ; par2 = ALL ; par3 = FALSE ; par4 = FALSE ;
 
R code (references can be found in the software module):
par3 <- as.logical(par3)
par4 <- as.logical(par4)
if (par3 == 'TRUE'){
dum = xlab
xlab = ylab
ylab = dum
}
x <- t(y)
hc <- hclust(dist(x),method=par1)
d <- as.dendrogram(hc)
str(d)
mysub <- paste('Method: ',par1)
bitmap(file='test1.png')
if (par4 == 'TRUE'){
plot(d,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8),type='t',center=T, sub=mysub)
} else {
plot(d,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8), sub=mysub)
}
dev.off()
if (par2 != 'ALL'){
if (par3 == 'TRUE'){
ylab = 'cluster'
} else {
xlab = 'cluster'
}
par2 <- as.numeric(par2)
memb <- cutree(hc, k = par2)
cent <- NULL
for(k in 1:par2){
cent <- rbind(cent, colMeans(x[memb == k, , drop = FALSE]))
}
hc1 <- hclust(dist(cent),method=par1, members = table(memb))
de <- as.dendrogram(hc1)
bitmap(file='test2.png')
if (par4 == 'TRUE'){
plot(de,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8),type='t',center=T, sub=mysub)
} else {
plot(de,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8), sub=mysub)
}
dev.off()
str(de)
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Dendrogram',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Label',header=TRUE)
a<-table.element(a,'Height',header=TRUE)
a<-table.row.end(a)
num <- length(x[,1])-1
for (i in 1:num)
{
a<-table.row.start(a)
a<-table.element(a,hc$labels[i])
a<-table.element(a,hc$height[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
if (par2 != 'ALL'){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Cut Dendrogram',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Label',header=TRUE)
a<-table.element(a,'Height',header=TRUE)
a<-table.row.end(a)
num <- par2-1
for (i in 1:num)
{
a<-table.row.start(a)
a<-table.element(a,i)
a<-table.element(a,hc1$height[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
 





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