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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 computationMon, 10 Nov 2008 06:59:06 -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/10/t1226325587djtde90uk1paxov.htm/, Retrieved Mon, 27 May 2024 21:58:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23061, Retrieved Mon, 27 May 2024 21:58:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123

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] [hier. clustering] [2008-11-10 13:59:06] [4940af498c7c54f3992f17142bd40069] [Current]
Feedback Forum
2008-11-16 16:04:03 [Jan Van Riet] [reply] 
De interpretatie hiervan is de volgende:
Op het dendogram zien we een opsplitsing van de gegevens in 2 grote groepen (categorieën), die elks nog eens verder vertakt zijn in kleinere groepjes. Dit wijst erop dat er een verschil is tussen de waarnemingen in categorie 1 en in categorie 2. Dit zou kunnen wijzen op een trend. De observaties die gelijkaardig gezien worden worden dus gegroepeerd.
2008-11-21 19:47:09 [Kim Wester] [reply] 
Hierarchical Clustering is bedoeld om inzicht te krijgen in welke observaties als gelijkaardig kunnen worden gezien. De puntjes in het dendrogram zijn observaties. Hier worden clusters van gemaakt en onderverdeeld (vertakkingen). Deze methode wordt vaak toegepast op NIET tijdreeksen maar op bijvoorbeeld marketing resultaten.
2008-11-23 13:43:11 [Nathalie Boden] [reply] 
Het is een grafiek die clustering weergeeft. De groepjes zijn hiërarchisch opgebouwd in 2 groepen. Waarbij de linkertak te groeperen valt in 1 categorie en waarbij de rechtertak te groeperen valt in 1 andere categorie. Zo is het zeer duidelijk welke observaties er gelijkaardig zijn. Dit kan een verklaring hebben bv. een trend. Deze techniek is zeer populair in de marketingbranche.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
94,71	1995,37	10511	512238
93,77	1946,81	10812	519164
95,73	1765,9	10738	517009
95,99	1635,25	10171	509933
95,82	1833,42	9721	509127
95,47	1910,43	9897	500857
95,82	1959,67	9828	506971
94,71	1969,6	9924	569323
96,33	2061,41	10371	579714
96,5	2093,48	10846	577992
96,16	2120,88	10413	565464
96,33	2174,56	10709	547344
96,33	2196,72	10662	554788
95,05	2350,44	10570	562325
96,84	2440,25	10297	560854
96,92	2408,64	10635	555332
97,44	2472,81	10872	543599
97,78	2407,6	10296	536662
97,69	2454,62	10383	542722
96,67	2448,05	10431	593530
98,29	2497,84	10574	610763
98,2	2645,64	10653	612613
98,71	2756,76	10805	611324
98,54	2849,27	10872	594167
98,2	2921,44	10625	595454
96,92	2981,85	10407	590865
99,06	3080,58	10463	589379
99,65	3106,22	10556	584428
99,82	3119,31	10646	573100
99,99	3061,26	10702	567456
100,33	3097,31	11353	569028
99,31	3161,69	11346	620735
101,1	3257,16	11451	628884
101,1	3277,01	11964	628232
100,93	3295,32	12574	612117
100,85	3363,99	13031	595404
100,93	3494,17	13812	597141
99,6	3667,03	14544	593408
101,88	3813,06	14931	590072
101,81	3917,96	14886	579799
102,38	3895,51	16005	574205
102,74	3801,06	17064	572775
102,82	3570,12	15168	572942
101,72	3701,61	16050	619567
103,47	3862,27	15839	625809
102,98	3970,1	15137	619916
102,68	4138,52	14954	587625
102,9	4199,75	15648	565742
103,03	4290,89	15305	557274
101,29	4443,91	15579	560576
103,69	4502,64	16348	548854
103,68	4356,98	15928	531673
104,2	4591,27	16171	525919
104,08	4696,96	15937	511038
104,16	4621,4	15713	498662
103,05	4562,84	15594	555362
104,66	4202,52	15683	564591
104,46	4296,49	16438	541657
104,95	4435,23	17032	527070
105,85	4105,18	17696	509846
106,23	4116,68	17745	514258

Tables (Output of Computation)





Summary of computational 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

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23061&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23061&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23061&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Summary of Dendrogram
LabelHeight
1584.444552117650
2659.637584435575
3829.859640240444
4872.485521541762
5944.143727300033
61004.28130451582
71013.63626005584
81151.53669958886
91445.84613431029
101490.33022934516
111498.81254338226
121549.71508533020
131701.84744266341
141786.59942734794
151886.95706514709
161908.94652591423
171911.39195037440
181949.58659123214
191952.7472450115
202163.84686835737
212194.41793364471
222205.70888861155
232468.65608208191
242684.93955176392
253126.44037663352
263584.29538038284
273989.68988006835
284423.67837931419
294698.40877053591
305037.25682415873
315266.79343195292
325554.54397524532
335850.84742380214
346389.8807912099
356516.77260209315
366781.83712993758
376962.56314802277
387021.07438875741
397152.26377536558
407200.51438547275
417963.43289697721
4210289.4841285626
4311607.2851195226
4411805.4984737415
4511940.5446326579
4616000.1869639818
4716704.9477317962
4817542.4953564001
4918536.3572001604
5020168.0904998403
5121848.3494011471
5227832.3394847137
5340011.3661816668
5445422.6126143287
5547694.8313514359
5680667.9841637241
57195858.141475331
58201906.993643512
59506301.218772732
601113728.30418665

\begin{tabular}{lllllllll}
\hline
Summary of Dendrogram \tabularnewline
Label & Height \tabularnewline
1 & 584.444552117650 \tabularnewline
2 & 659.637584435575 \tabularnewline
3 & 829.859640240444 \tabularnewline
4 & 872.485521541762 \tabularnewline
5 & 944.143727300033 \tabularnewline
6 & 1004.28130451582 \tabularnewline
7 & 1013.63626005584 \tabularnewline
8 & 1151.53669958886 \tabularnewline
9 & 1445.84613431029 \tabularnewline
10 & 1490.33022934516 \tabularnewline
11 & 1498.81254338226 \tabularnewline
12 & 1549.71508533020 \tabularnewline
13 & 1701.84744266341 \tabularnewline
14 & 1786.59942734794 \tabularnewline
15 & 1886.95706514709 \tabularnewline
16 & 1908.94652591423 \tabularnewline
17 & 1911.39195037440 \tabularnewline
18 & 1949.58659123214 \tabularnewline
19 & 1952.7472450115 \tabularnewline
20 & 2163.84686835737 \tabularnewline
21 & 2194.41793364471 \tabularnewline
22 & 2205.70888861155 \tabularnewline
23 & 2468.65608208191 \tabularnewline
24 & 2684.93955176392 \tabularnewline
25 & 3126.44037663352 \tabularnewline
26 & 3584.29538038284 \tabularnewline
27 & 3989.68988006835 \tabularnewline
28 & 4423.67837931419 \tabularnewline
29 & 4698.40877053591 \tabularnewline
30 & 5037.25682415873 \tabularnewline
31 & 5266.79343195292 \tabularnewline
32 & 5554.54397524532 \tabularnewline
33 & 5850.84742380214 \tabularnewline
34 & 6389.8807912099 \tabularnewline
35 & 6516.77260209315 \tabularnewline
36 & 6781.83712993758 \tabularnewline
37 & 6962.56314802277 \tabularnewline
38 & 7021.07438875741 \tabularnewline
39 & 7152.26377536558 \tabularnewline
40 & 7200.51438547275 \tabularnewline
41 & 7963.43289697721 \tabularnewline
42 & 10289.4841285626 \tabularnewline
43 & 11607.2851195226 \tabularnewline
44 & 11805.4984737415 \tabularnewline
45 & 11940.5446326579 \tabularnewline
46 & 16000.1869639818 \tabularnewline
47 & 16704.9477317962 \tabularnewline
48 & 17542.4953564001 \tabularnewline
49 & 18536.3572001604 \tabularnewline
50 & 20168.0904998403 \tabularnewline
51 & 21848.3494011471 \tabularnewline
52 & 27832.3394847137 \tabularnewline
53 & 40011.3661816668 \tabularnewline
54 & 45422.6126143287 \tabularnewline
55 & 47694.8313514359 \tabularnewline
56 & 80667.9841637241 \tabularnewline
57 & 195858.141475331 \tabularnewline
58 & 201906.993643512 \tabularnewline
59 & 506301.218772732 \tabularnewline
60 & 1113728.30418665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23061&T=1

[TABLE]
[ROW][C]Summary of Dendrogram[/C][/ROW]
[ROW][C]Label[/C][C]Height[/C][/ROW]
[ROW][C]1[/C][C]584.444552117650[/C][/ROW]
[ROW][C]2[/C][C]659.637584435575[/C][/ROW]
[ROW][C]3[/C][C]829.859640240444[/C][/ROW]
[ROW][C]4[/C][C]872.485521541762[/C][/ROW]
[ROW][C]5[/C][C]944.143727300033[/C][/ROW]
[ROW][C]6[/C][C]1004.28130451582[/C][/ROW]
[ROW][C]7[/C][C]1013.63626005584[/C][/ROW]
[ROW][C]8[/C][C]1151.53669958886[/C][/ROW]
[ROW][C]9[/C][C]1445.84613431029[/C][/ROW]
[ROW][C]10[/C][C]1490.33022934516[/C][/ROW]
[ROW][C]11[/C][C]1498.81254338226[/C][/ROW]
[ROW][C]12[/C][C]1549.71508533020[/C][/ROW]
[ROW][C]13[/C][C]1701.84744266341[/C][/ROW]
[ROW][C]14[/C][C]1786.59942734794[/C][/ROW]
[ROW][C]15[/C][C]1886.95706514709[/C][/ROW]
[ROW][C]16[/C][C]1908.94652591423[/C][/ROW]
[ROW][C]17[/C][C]1911.39195037440[/C][/ROW]
[ROW][C]18[/C][C]1949.58659123214[/C][/ROW]
[ROW][C]19[/C][C]1952.7472450115[/C][/ROW]
[ROW][C]20[/C][C]2163.84686835737[/C][/ROW]
[ROW][C]21[/C][C]2194.41793364471[/C][/ROW]
[ROW][C]22[/C][C]2205.70888861155[/C][/ROW]
[ROW][C]23[/C][C]2468.65608208191[/C][/ROW]
[ROW][C]24[/C][C]2684.93955176392[/C][/ROW]
[ROW][C]25[/C][C]3126.44037663352[/C][/ROW]
[ROW][C]26[/C][C]3584.29538038284[/C][/ROW]
[ROW][C]27[/C][C]3989.68988006835[/C][/ROW]
[ROW][C]28[/C][C]4423.67837931419[/C][/ROW]
[ROW][C]29[/C][C]4698.40877053591[/C][/ROW]
[ROW][C]30[/C][C]5037.25682415873[/C][/ROW]
[ROW][C]31[/C][C]5266.79343195292[/C][/ROW]
[ROW][C]32[/C][C]5554.54397524532[/C][/ROW]
[ROW][C]33[/C][C]5850.84742380214[/C][/ROW]
[ROW][C]34[/C][C]6389.8807912099[/C][/ROW]
[ROW][C]35[/C][C]6516.77260209315[/C][/ROW]
[ROW][C]36[/C][C]6781.83712993758[/C][/ROW]
[ROW][C]37[/C][C]6962.56314802277[/C][/ROW]
[ROW][C]38[/C][C]7021.07438875741[/C][/ROW]
[ROW][C]39[/C][C]7152.26377536558[/C][/ROW]
[ROW][C]40[/C][C]7200.51438547275[/C][/ROW]
[ROW][C]41[/C][C]7963.43289697721[/C][/ROW]
[ROW][C]42[/C][C]10289.4841285626[/C][/ROW]
[ROW][C]43[/C][C]11607.2851195226[/C][/ROW]
[ROW][C]44[/C][C]11805.4984737415[/C][/ROW]
[ROW][C]45[/C][C]11940.5446326579[/C][/ROW]
[ROW][C]46[/C][C]16000.1869639818[/C][/ROW]
[ROW][C]47[/C][C]16704.9477317962[/C][/ROW]
[ROW][C]48[/C][C]17542.4953564001[/C][/ROW]
[ROW][C]49[/C][C]18536.3572001604[/C][/ROW]
[ROW][C]50[/C][C]20168.0904998403[/C][/ROW]
[ROW][C]51[/C][C]21848.3494011471[/C][/ROW]
[ROW][C]52[/C][C]27832.3394847137[/C][/ROW]
[ROW][C]53[/C][C]40011.3661816668[/C][/ROW]
[ROW][C]54[/C][C]45422.6126143287[/C][/ROW]
[ROW][C]55[/C][C]47694.8313514359[/C][/ROW]
[ROW][C]56[/C][C]80667.9841637241[/C][/ROW]
[ROW][C]57[/C][C]195858.141475331[/C][/ROW]
[ROW][C]58[/C][C]201906.993643512[/C][/ROW]
[ROW][C]59[/C][C]506301.218772732[/C][/ROW]
[ROW][C]60[/C][C]1113728.30418665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23061&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23061&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
1584.444552117650
2659.637584435575
3829.859640240444
4872.485521541762
5944.143727300033
61004.28130451582
71013.63626005584
81151.53669958886
91445.84613431029
101490.33022934516
111498.81254338226
121549.71508533020
131701.84744266341
141786.59942734794
151886.95706514709
161908.94652591423
171911.39195037440
181949.58659123214
191952.7472450115
202163.84686835737
212194.41793364471
222205.70888861155
232468.65608208191
242684.93955176392
253126.44037663352
263584.29538038284
273989.68988006835
284423.67837931419
294698.40877053591
305037.25682415873
315266.79343195292
325554.54397524532
335850.84742380214
346389.8807912099
356516.77260209315
366781.83712993758
376962.56314802277
387021.07438875741
397152.26377536558
407200.51438547275
417963.43289697721
4210289.4841285626
4311607.2851195226
4411805.4984737415
4511940.5446326579
4616000.1869639818
4716704.9477317962
4817542.4953564001
4918536.3572001604
5020168.0904998403
5121848.3494011471
5227832.3394847137
5340011.3661816668
5445422.6126143287
5547694.8313514359
5680667.9841637241
57195858.141475331
58201906.993643512
59506301.218772732
601113728.30418665


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')
}