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R Software Module: rwasp_hierarchicalclustering.wasp (opens new window with default values)
Title produced by software: Hierarchical Clustering
Date of computation: Mon, 05 Nov 2007 15:38:41 -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/05/ho6l94mmmipkqxy1194302172.htm/, Retrieved Mon, 05 Nov 2007 23:36:14 +0100
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
106.7 97.3 93.5 104.8 124.9 110.2 101 94.7 105.6 132 125.9 113.2 112.9 118.3 151.4 100.1 101 99.2 89.9 108.9 106.4 105.7 105.6 90.2 121.3 114.8 113.9 113 107 123.4 81.3 86.4 83.1 64.5 90.3 87 96.5 81.1 92.6 79.3 104.2 103.3 96.9 95.8 117.2 108 114.9 104.3 94.3 116.9 105 105.8 97.7 91.2 120.8 94.5 94.2 102.6 86.3 96.1 92 98.4 89.9 77.6 100.8 95.9 99.4 96 82.5 105.3 108.8 108.8 112.7 97.7 116.1 103.4 112.6 107.1 83.3 112.8 102.1 104.4 106.2 84.2 114.5 110.1 112.2 121 92.8 117.2 83.2 81.1 101.2 77.4 77.1 82.7 97.1 83.2 72.5 80.1 106.8 112.6 105.1 88.8 120.3 113.7 113.8 113.3 93.4 133.4 102.5 107.8 99.1 92.6 109.4 96.6 103.2 100.3 90.7 93.2 92.1 103.3 93.5 81.6 91.2 95.6 101.2 98.8 84.1 99.2 102.3 107.7 106.2 88.1 108.2 98.6 110.4 98.3 85.3 101.5 98.2 101.9 102.1 82.9 106.9 104.5 115.9 117.1 84.8 104.4 84 89.9 101.5 71.2 77.9 73.8 88.6 80.5 68.9 60 103.9 117.2 105.9 94.3 99.5 106 123.9 109.5 97.6 95 97.2 100 97.2 85.6 105.6 102.6 103.6 114.5 91.9 102.5 89 94.1 93.5 75.8 93.3 93.8 98.7 100.9 79.8 97.3 116.7 119.5 121.1 99 127 106.8 112.7 116.5 88.5 111.7 98.5 104.4 109.3 86.7 96.4 118.7 124.7 118.1 97.9 133 90 89.1 108.3 94.3 72.2 91.9 97 105.4 72.9 95.8 113.3 121.6 116.2 91.8 124.1 113.1 118.8 111.2 93.2 127.6 104.1 114 105.8 86.5 110.7 108.7 111.5 122.7 98.9 104.6 96.7 97.2 99.5 77.2 112.7 101 102.5 107.9 79.4 115.3 116.9 113.4 124.6 90.4 139.4 105.8 109.8 115 81.4 119 99 104.9 110.3 85.8 97.4 129.4 126.1 132.7 103.6 154 83 80 99.7 73.6 81.5 88.9 96.8 96.5 75.7 88.8 115.9 117.2 118.7 99.2 127.7 104.2 112.3 112.9 88.7 105.1 113.4 117.3 130.5 94.6 114.9 112.2 111.1 137.9 98.7 106.4 100.8 102.2 115 84.2 104.5 107.3 104.3 116.8 87.7 121.6 126.6 122.9 140.9 103.3 141.4 102.9 107.6 120.7 88.2 99 117.9 121.3 134.2 93.4 126.7 128.8 131.5 147.3 106.3 134.1 87.5 89 112.4 73.1 81.3 93.8 104.4 107.1 78.6 88.6 122.7 128.9 128.4 101.6 132.7 126.2 135.9 137.7 101.4 132.9 124.6 133.3 135 98.5 134.4 116.7 121.3 151 99 103.7 115.2 120.5 137.4 89.5 119.7 111.1 120.4 132.4 83.5 115 129.9 137.9 161.3 97.4 132.9 113.3 126.1 139.8 87.8 108.5 118.5 133.2 146 90.4 113.9 133.5 146.6 154.6 97.1 142.9 102.1 103.4 142.1 79.4 95.2 102.4 117.2 120.5 85 93
 
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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Summary of Dendrogram
LabelHeight
11.81934053986603
23.4957116585897
33.63180395946697
44.33474336033865
55.22206855565876
65.60267793113257
76
86.04648658313239
96.107372593841
106.45368112010501
116.81102048154313
126.8622153857191
137.01284535691469
147.17425954367417
157.71686464828818
168.29095893126965
178.38033412221731
188.85607136375944
198.89381807774367
208.95153618101385
219.06587006304414
229.07634287585039
239.66436754267965
249.70721381241806
2510.5976412469945
2610.9986362791030
2711.1830228471554
2811.5021737076085
2911.5909447414782
3011.7324336776306
3111.9050409491106
3212.0660681251185
3312.1581248554207
3412.2596900450215
3512.3211200789539
3612.5880896088326
3712.9359962894243
3813.4925905592662
3913.6842975705734
4013.9989285304269
4114.2151327816521
4214.2597335178467
4314.6860478005487
4415.189470036838
4515.2849599279815
4615.5238526146057
4716.2120325684351
4816.867127793433
4916.8875693928996
5017.4149361181717
5118.2436290249500
5218.5512802792691
5319.1515012466386
5419.8547223601842
5520.2519134898409
5620.6211056929545
5721.9752133095449
5823.1885747729350
5924.1458070894307
6024.8131013781027
6125.4161366064947
6225.8994208429455
6326.8467875173176
6429.0523665129022
6529.4492784291908
6629.6536675640636
6731.7064662174768
6834.2226533161881
6937.9618229277784
7039.0627699990669
7139.7268171390561
7241.082234603293
7342.6697785323524
7448.6013374301572
7558.9333521870256
7661.2185429424778
7767.1763351188497
7893.2870837790527
79141.727026357008
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/05/ho6l94mmmipkqxy1194302172/1f53e1194302319.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/05/ho6l94mmmipkqxy1194302172/1f53e1194302319.ps (open in new window)


 
Parameters:
 
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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