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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_hierarchicalclustering.wasp
Title produced by softwareHierarchical Clustering
Date of computationThu, 13 Nov 2008 02:26:19 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/13/t1226568466j4i3k71g7lcqnp6.htm/, Retrieved Sun, 19 May 2024 12:40:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24515, Retrieved Sun, 19 May 2024 12:40:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Hierarchical Clustering] [workshop 3 Q2] [2008-11-13 09:26:19] [1a15026c70cce1c14dcfcc267c5d8133] [Current]
Feedback Forum
2008-11-17 08:22:31 [006ad2c49b6a7c2ad6ab685cfc1dae56] [reply] 
Het dendrogram wordt gebruikt om gegevens op te delen in verschillende groepen (clusters). Het begint bij een verdeling in twee, en gaat zo verder waarna je een uitspraak kan doen over de soorten gegevens die in de verschillende groepen zitten.
2008-11-20 08:42:16 [Angelique Van de Vijver] [reply] 
Het dendrogram verdeelt de gegevens in verschillende clusters. Zo zie je eerst een deling in twee waarna deze clusters verder worden onderverdeeld.(hiërarchische clustering). Het dendrogram is louter exploratief en er kan dus niet veel uit worden afgeleid. Er zijn verschillende groepen van maanden die hetzelfde patroon vertonen. De linker -en rechtercluster verschillen wel van waarde. De rechtercluster heeft een hogere waarde bij de eerste onderverdeling.
2008-11-24 12:11:07 [Anouk Greeve] [reply] 
Het nut van een dendrogram is dat we kunnen zien welke gegevens in 1 groep zitten en welke we dus anders moeten behandelen. We kunnen vaststellen dat de waarden van de periodes die indezelfde kluster liggen, rond dezelfde hoogte liggen en dus dezelfde gegevens bevatten

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118.4	111.4	104.0
121.4	114.1	107.9
128.8	121.8	113.8
131.7	127.6	113.8
141.7	129.9	123.1
142.9	128.0	125.1
139.4	123.5	137.6
134.7	124.0	134.0
125.0	127.4	140.3
113.6	127.6	152.1
111.5	128.4	150.6
108.5	131.4	167.3
112.3	135.1	153.2
116.6	134.0	142.0
115.5	144.5	154.4
120.1	147.3	158.5
132.9	150.9	180.9
128.1	148.7	181.3
129.3	141.4	172.4
132.5	138.9	192.0
131.0	139.8	199.3
124.9	145.6	215.4
120.8	147.9	214.3
122.0	148.5	201.5
122.1	151.1	190.5
127.4	157.5	196.0
135.2	167.5	215.7
137.3	172.3	209.4
135.0	173.5	214.1
136.0	187.5	237.8
138.4	205.5	239.0
134.7	195.1	237.8
138.4	204.5	251.5
133.9	204.5	248.8
133.6	201.7	215.4
141.2	207.0	201.2
151.8	206.6	203.1
155.4	210.6	214.2
156.6	211.1	188.9
161.6	215.0	203.0
160.7	223.9	213.3
156.0	238.2	228.5
159.5	238.9	228.2
168.7	229.6	240.9
169.9	232.2	258.8
169.9	222.1	248.5
185.9	221.6	269.2
190.8	227.3	289.6
195.8	221.0	323.4
211.9	213.6	317.2
227.1	243.4	322.8
251.3	253.8	340.9
256.7	265.3	368.2
251.9	268.2	388.5
251.2	268.5	441.2
270.3	266.9	474.3
267.2	268.4	483.9
243.0	250.8	417.9
229.9	231.2	365.9
187.2	192.0	263.0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24515&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24515&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24515&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Summary of Dendrogram
LabelHeight
12.70185121722126
23.00832179129827
33.58189893771447
44.82804308182934
55.24785670536076
65.29528091794951
75.36842621258781
85.61248608016091
95.94138031100519
106.48459713474938
116.76830850360708
127.50666370633454
137.71038261048049
147.81505126050554
159.04744039022349
169.9649385346825
1710.1990195607225
1810.7447661677674
1910.7763630228385
2010.8171160666788
2112.1136950423352
2213.5366170072142
2313.8307057573613
2414.1461919502270
2514.7811573727437
2618.1205944926213
2718.7725863961256
2820.2620857442502
2921.0603893601234
3021.4486659451799
3121.7407451574228
3223.8922998140176
3327.0000309951652
3428.9973209513718
3529.4459414890777
3630.1345929319542
3730.3878265099694
3830.8932180672498
3931.9595056282165
4034.7089340862108
4137.3905185672453
4240.9356278280491
4342.4975910841938
4445.8816748295424
4551.2480127753399
4661.7799378407632
4772.1102270942971
4878.1576516117181
4988.3493835987703
5090.211546390219
51106.294157082492
52162.183751092097
53175.207190828103
54200.434432473968
55289.414341350622
56530.671184524882
57623.343273649682
581370.16296695868
592863.13190212375

\begin{tabular}{lllllllll}
\hline
Summary of Dendrogram \tabularnewline
Label & Height \tabularnewline
1 & 2.70185121722126 \tabularnewline
2 & 3.00832179129827 \tabularnewline
3 & 3.58189893771447 \tabularnewline
4 & 4.82804308182934 \tabularnewline
5 & 5.24785670536076 \tabularnewline
6 & 5.29528091794951 \tabularnewline
7 & 5.36842621258781 \tabularnewline
8 & 5.61248608016091 \tabularnewline
9 & 5.94138031100519 \tabularnewline
10 & 6.48459713474938 \tabularnewline
11 & 6.76830850360708 \tabularnewline
12 & 7.50666370633454 \tabularnewline
13 & 7.71038261048049 \tabularnewline
14 & 7.81505126050554 \tabularnewline
15 & 9.04744039022349 \tabularnewline
16 & 9.9649385346825 \tabularnewline
17 & 10.1990195607225 \tabularnewline
18 & 10.7447661677674 \tabularnewline
19 & 10.7763630228385 \tabularnewline
20 & 10.8171160666788 \tabularnewline
21 & 12.1136950423352 \tabularnewline
22 & 13.5366170072142 \tabularnewline
23 & 13.8307057573613 \tabularnewline
24 & 14.1461919502270 \tabularnewline
25 & 14.7811573727437 \tabularnewline
26 & 18.1205944926213 \tabularnewline
27 & 18.7725863961256 \tabularnewline
28 & 20.2620857442502 \tabularnewline
29 & 21.0603893601234 \tabularnewline
30 & 21.4486659451799 \tabularnewline
31 & 21.7407451574228 \tabularnewline
32 & 23.8922998140176 \tabularnewline
33 & 27.0000309951652 \tabularnewline
34 & 28.9973209513718 \tabularnewline
35 & 29.4459414890777 \tabularnewline
36 & 30.1345929319542 \tabularnewline
37 & 30.3878265099694 \tabularnewline
38 & 30.8932180672498 \tabularnewline
39 & 31.9595056282165 \tabularnewline
40 & 34.7089340862108 \tabularnewline
41 & 37.3905185672453 \tabularnewline
42 & 40.9356278280491 \tabularnewline
43 & 42.4975910841938 \tabularnewline
44 & 45.8816748295424 \tabularnewline
45 & 51.2480127753399 \tabularnewline
46 & 61.7799378407632 \tabularnewline
47 & 72.1102270942971 \tabularnewline
48 & 78.1576516117181 \tabularnewline
49 & 88.3493835987703 \tabularnewline
50 & 90.211546390219 \tabularnewline
51 & 106.294157082492 \tabularnewline
52 & 162.183751092097 \tabularnewline
53 & 175.207190828103 \tabularnewline
54 & 200.434432473968 \tabularnewline
55 & 289.414341350622 \tabularnewline
56 & 530.671184524882 \tabularnewline
57 & 623.343273649682 \tabularnewline
58 & 1370.16296695868 \tabularnewline
59 & 2863.13190212375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24515&T=1

[TABLE]
[ROW][C]Summary of Dendrogram[/C][/ROW]
[ROW][C]Label[/C][C]Height[/C][/ROW]
[ROW][C]1[/C][C]2.70185121722126[/C][/ROW]
[ROW][C]2[/C][C]3.00832179129827[/C][/ROW]
[ROW][C]3[/C][C]3.58189893771447[/C][/ROW]
[ROW][C]4[/C][C]4.82804308182934[/C][/ROW]
[ROW][C]5[/C][C]5.24785670536076[/C][/ROW]
[ROW][C]6[/C][C]5.29528091794951[/C][/ROW]
[ROW][C]7[/C][C]5.36842621258781[/C][/ROW]
[ROW][C]8[/C][C]5.61248608016091[/C][/ROW]
[ROW][C]9[/C][C]5.94138031100519[/C][/ROW]
[ROW][C]10[/C][C]6.48459713474938[/C][/ROW]
[ROW][C]11[/C][C]6.76830850360708[/C][/ROW]
[ROW][C]12[/C][C]7.50666370633454[/C][/ROW]
[ROW][C]13[/C][C]7.71038261048049[/C][/ROW]
[ROW][C]14[/C][C]7.81505126050554[/C][/ROW]
[ROW][C]15[/C][C]9.04744039022349[/C][/ROW]
[ROW][C]16[/C][C]9.9649385346825[/C][/ROW]
[ROW][C]17[/C][C]10.1990195607225[/C][/ROW]
[ROW][C]18[/C][C]10.7447661677674[/C][/ROW]
[ROW][C]19[/C][C]10.7763630228385[/C][/ROW]
[ROW][C]20[/C][C]10.8171160666788[/C][/ROW]
[ROW][C]21[/C][C]12.1136950423352[/C][/ROW]
[ROW][C]22[/C][C]13.5366170072142[/C][/ROW]
[ROW][C]23[/C][C]13.8307057573613[/C][/ROW]
[ROW][C]24[/C][C]14.1461919502270[/C][/ROW]
[ROW][C]25[/C][C]14.7811573727437[/C][/ROW]
[ROW][C]26[/C][C]18.1205944926213[/C][/ROW]
[ROW][C]27[/C][C]18.7725863961256[/C][/ROW]
[ROW][C]28[/C][C]20.2620857442502[/C][/ROW]
[ROW][C]29[/C][C]21.0603893601234[/C][/ROW]
[ROW][C]30[/C][C]21.4486659451799[/C][/ROW]
[ROW][C]31[/C][C]21.7407451574228[/C][/ROW]
[ROW][C]32[/C][C]23.8922998140176[/C][/ROW]
[ROW][C]33[/C][C]27.0000309951652[/C][/ROW]
[ROW][C]34[/C][C]28.9973209513718[/C][/ROW]
[ROW][C]35[/C][C]29.4459414890777[/C][/ROW]
[ROW][C]36[/C][C]30.1345929319542[/C][/ROW]
[ROW][C]37[/C][C]30.3878265099694[/C][/ROW]
[ROW][C]38[/C][C]30.8932180672498[/C][/ROW]
[ROW][C]39[/C][C]31.9595056282165[/C][/ROW]
[ROW][C]40[/C][C]34.7089340862108[/C][/ROW]
[ROW][C]41[/C][C]37.3905185672453[/C][/ROW]
[ROW][C]42[/C][C]40.9356278280491[/C][/ROW]
[ROW][C]43[/C][C]42.4975910841938[/C][/ROW]
[ROW][C]44[/C][C]45.8816748295424[/C][/ROW]
[ROW][C]45[/C][C]51.2480127753399[/C][/ROW]
[ROW][C]46[/C][C]61.7799378407632[/C][/ROW]
[ROW][C]47[/C][C]72.1102270942971[/C][/ROW]
[ROW][C]48[/C][C]78.1576516117181[/C][/ROW]
[ROW][C]49[/C][C]88.3493835987703[/C][/ROW]
[ROW][C]50[/C][C]90.211546390219[/C][/ROW]
[ROW][C]51[/C][C]106.294157082492[/C][/ROW]
[ROW][C]52[/C][C]162.183751092097[/C][/ROW]
[ROW][C]53[/C][C]175.207190828103[/C][/ROW]
[ROW][C]54[/C][C]200.434432473968[/C][/ROW]
[ROW][C]55[/C][C]289.414341350622[/C][/ROW]
[ROW][C]56[/C][C]530.671184524882[/C][/ROW]
[ROW][C]57[/C][C]623.343273649682[/C][/ROW]
[ROW][C]58[/C][C]1370.16296695868[/C][/ROW]
[ROW][C]59[/C][C]2863.13190212375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24515&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24515&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of Dendrogram
LabelHeight
12.70185121722126
23.00832179129827
33.58189893771447
44.82804308182934
55.24785670536076
65.29528091794951
75.36842621258781
85.61248608016091
95.94138031100519
106.48459713474938
116.76830850360708
127.50666370633454
137.71038261048049
147.81505126050554
159.04744039022349
169.9649385346825
1710.1990195607225
1810.7447661677674
1910.7763630228385
2010.8171160666788
2112.1136950423352
2213.5366170072142
2313.8307057573613
2414.1461919502270
2514.7811573727437
2618.1205944926213
2718.7725863961256
2820.2620857442502
2921.0603893601234
3021.4486659451799
3121.7407451574228
3223.8922998140176
3327.0000309951652
3428.9973209513718
3529.4459414890777
3630.1345929319542
3730.3878265099694
3830.8932180672498
3931.9595056282165
4034.7089340862108
4137.3905185672453
4240.9356278280491
4342.4975910841938
4445.8816748295424
4551.2480127753399
4661.7799378407632
4772.1102270942971
4878.1576516117181
4988.3493835987703
5090.211546390219
51106.294157082492
52162.183751092097
53175.207190828103
54200.434432473968
55289.414341350622
56530.671184524882
57623.343273649682
581370.16296695868
592863.13190212375


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = ward ; par2 = ALL ; par3 = FALSE ; par4 = FALSE ;
Parameters (R input):
par1 = ward ; 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')
}