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Various EDA topics

R Software Module: rwasp_hierarchicalclustering.wasp (opens new window with default values)
Title produced by software: Hierarchical Clustering
Date of computation: Sun, 04 Nov 2007 02:56:33 -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/04/tag0qrgvevr72ca1194170106.htm/, Retrieved Sun, 04 Nov 2007 10:55:08 +0100
 
User-defined keywords:
Q2 Centroid linkage
 
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
75.74512072532152
85.80896927782118
96.04648658313239
106.107372593841
116.45368112010501
126.68318991623895
136.70856395188957
146.81102048154313
156.83260979020074
166.8622153857191
176.9570635694692
187.01284535691469
197.4083140899238
207.57343886962343
217.96271809360783
227.70067939697619
237.74391660490514
248.08577866488946
258.29095893126965
267.51037217573801
277.94376386896572
287.49156230923087
298.39521515116436
308.61961785807173
318.85607136375944
328.88675723499887
338.8336632419453
348.91976550282563
358.21826678172368
368.59385614668311
379.06587006304414
389.07634287585039
399.21848623328527
409.29070268016596
419.0290314497483
429.38657694801817
439.66436754267965
449.88155467921738
459.22074492388363
4610.3614670775909
479.0145275409285
488.92164046755097
4910.7252559029533
5011.0257465898430
5111.1190299629814
5211.3220627930350
5311.5326938832682
5411.9829738307730
5512.9772900194014
5610.7200986181489
5713.1374055311728
5813.2018424987335
5913.2292277162367
6013.5952321659580
6114.1296382069122
6215.2849599279815
6315.3049958376085
6416.5753917415342
6513.8608601027515
6616.7485198699959
6716.5514915500870
6816.867127793433
6916.7515655982834
7021.4437693692230
7120.6974630812794
7221.3183210611272
7324.0019345695498
7424.7991840079771
7522.1667598959838
7626.3343789736007
7741.7485457345893
7849.1853350459952
7956.1162166642157
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/tag0qrgvevr72ca1194170106/1k7bf1194170189.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/tag0qrgvevr72ca1194170106/1k7bf1194170189.ps (open in new window)


 
Parameters:
par1 = centroid ; 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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