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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 02:51:09 -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/dwqo30as4dm0spa1194169776.htm/, Retrieved Sun, 04 Nov 2007 10:49:38 +0100
 
User-defined keywords:
Q2 Single 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 time2 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
106.13188388670235
116.45368112010501
126.54140657657052
136.81102048154313
146.8622153857191
157.01284535691469
167.0547856097829
177.14002801114953
187.20416546173115
197.71686464828818
207.96555082841106
217.96743371481684
228.10123447383174
238.11356888181766
248.2048765987064
258.29095893126965
268.49411561023278
278.75271386485357
288.78919791562347
298.85607136375944
308.88988188897918
318.92356431029665
328.95153618101385
338.97050723203544
349.06587006304414
359.07634287585039
369.25148636706556
379.31718841711383
389.66436754267965
399.86052736926377
409.88331928048467
419.88989383158384
429.95288902781498
439.99549898704412
449.99699954986495
4510.0044989879554
4610.0309520983803
4710.1024749442896
4810.3614670775909
4910.4947605975553
5010.7405772656780
5110.8282039138539
5211.1319360400606
5311.6481758228488
5411.6541837981044
5511.7766718558343
5611.9887447216129
5712.0660681251185
5812.1260051129793
5912.3211200789539
6012.3624431242372
6112.3899152539475
6213.0479883506999
6313.4925905592662
6413.5310753452931
6513.5535973084639
6613.9319776054945
6714.0960987510729
6815.2849599279815
6915.3518728499164
7015.6259399717265
7116.867127793433
7217.3594354746921
7317.4149361181717
7417.8555313558572
7520.6859855941166
7621.6857095802743
7723.835267986746
7823.9941659575823
7928.1700195243099
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/dwqo30as4dm0spa1194169776/15s5h1194169866.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/dwqo30as4dm0spa1194169776/15s5h1194169866.ps (open in new window)


 
Parameters:
par1 = single ; 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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Software written by Ed van Stee & Patrick Wessa


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