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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 03:00:59 -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/jf9f0bwcjbbdfn61194170363.htm/, Retrieved Sun, 04 Nov 2007 10:59:23 +0100
 
User-defined keywords:
Q2 McQuitty method
 
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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Summary of Dendrogram
LabelHeight
11.81934053986603
23.4957116585897
33.63180395946697
44.33474336033865
55.22206855565876
65.60267793113257
76
86.04648658313239
96.107372593841
106.45368112010501
116.65307171518826
126.81102048154313
136.8622153857191
147.0128453569147
157.71686464828818
167.79224979197423
178.29095893126965
188.53536491058652
198.54934733822503
208.85607136375944
219.06587006304414
229.0763428758504
239.16152542915247
249.24820586402077
259.66436754267965
269.78407423951499
2710.2768202186218
2810.3352393878776
2910.3614670775909
3010.5240033722000
3110.8023243259491
3210.8552853577552
3310.9134200964784
3410.9737386581495
3511.2120807487957
3611.4635844628244
3711.9050409491106
3812.0660681251185
3912.0878864468663
4012.2131065595243
4112.3211200789539
4212.3482617442458
4312.7960872940759
4413.4925905592662
4513.7138757967634
4614.0088727562624
4714.3136470391841
4814.4466335544222
4914.7498621014623
5015.2849599279815
5115.6665695411887
5215.7076414524906
5315.9541016416365
5416.4820643158693
5516.5669240339239
5616.6374809776430
5716.867127793433
5818.0826863603394
5918.7760575346471
6019.6252587847289
6120.9466226852032
6221.1855965364901
6321.7918310026535
6422.0297523151817
6524.1147935165353
6624.2179479152118
6726.5379765883438
6826.9292025445292
6927.8398302496324
7030.8140712187647
7131.0364847018588
7231.1609745493824
7331.3004124817918
7435.9382059392606
7538.974190104474
7640.8934083389059
7749.8993588665726
7855.5461443528591
7980.4379909125861
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/jf9f0bwcjbbdfn61194170363/124c41194170452.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/jf9f0bwcjbbdfn61194170363/124c41194170452.ps (open in new window)


 
Parameters:
par1 = mcquitty ; 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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