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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 02:58:35 -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/t9lusx4lpxqmovm1194170254.htm/, Retrieved Sun, 04 Nov 2007 10:57:36 +0100
 
User-defined keywords:
Q2 Average 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 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
116.65307171518826
126.81102048154313
136.8622153857191
147.01284535691469
157.71686464828818
167.79224979197423
178.29095893126965
188.53536491058652
198.54934733822503
208.85607136375944
219.06587006304414
229.07634287585039
239.16152542915247
249.24820586402077
259.66436754267965
269.78407423951499
2710.2768202186218
2810.3614670775909
2910.4934767274156
3010.5240033722000
3110.8023243259491
3210.8207028470378
3310.9134200964784
3411.0035207123062
3511.028341938512
3611.6805505127767
3711.8214497038615
3811.9050409491106
3912.0660681251185
4012.1013798091961
4112.3211200789539
4212.7960872940759
4313.4925905592662
4413.521855814108
4513.5552422654116
4613.6471718125107
4713.8713618260873
4814.3893216315851
4914.5860555258083
5014.6364953040235
5115.2849599279815
5215.4116114581087
5315.7076414524906
5415.9276708365762
5516.0003889381927
5616.6905870560931
5716.867127793433
5817.1777009237376
5918.0812672930959
6018.0826863603394
6118.2427119390037
6219.6328005933232
6321.8746900591820
6422.058905609594
6523.1040882766312
6623.5561968002172
6724.3932028969486
6826.5379765883438
6926.6863336551662
7028.2790986002908
7129.4936610150478
7230.1725727574364
7333.2012570156019
7436.5277669948092
7536.7888819154980
7642.3089801689107
7744.6293643873411
7846.1523773536167
7954.7487303955624
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/t9lusx4lpxqmovm1194170254/1xbw21194170312.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/04/t9lusx4lpxqmovm1194170254/1xbw21194170312.ps (open in new window)


 
Parameters:
par1 = average ; 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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