FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_hierarchicalclustering.wasp
Title produced by softwareHierarchical Clustering
Date of computationTue, 11 Nov 2008 14:40:31 -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/11/t1226439988fu0gbj0xyez3sjh.htm/, Retrieved Mon, 20 May 2024 11:45:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23998, Retrieved Mon, 20 May 2024 11:45:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

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] [Various EDA topic...] [2008-11-11 21:40:31] [382e90e66f02be5ed86892bdc1574692] [Current]
Feedback Forum
2008-11-22 17:37:17 [Inge Meelberghs] [reply] 
Om tijdreeksen in clusters op te delen wordt gebruik gemaakt van een dendogram, ook boomstructuur genoemd.
De bedoeling hiervan is om inzicht te krijgen in welke observaties van de tijdreeks gelijkaardig zijn. De puntjes in het dendrogram zijn observaties. Hier worden clusters van gemaakt en zo verder onderverdeeld. Deze methode wordt meestal gebruikt voor niet- tijdreeksen maar eerder in de marketing sector om bijvoorbeeld te zien welke producten samen horen.
2008-11-23 12:35:49 [Pieter Broos] [reply] 
We gaan hier na welke observaties gelijkaardig kunnen gezien worden. We zien 2 grote takken waaronder de eerste helft van de observaties groter is dan de 2de. De 2de tak is hier duidelijk kleiner.
2008-11-24 21:17:31 [Yara Van Overstraeten] [reply] 
In het Dendrogram gaat men inderdaad zien welke observaties als gemeenschappelijk kunnen gezien worden in een bepaalde periode. Men deelt de gegevens op in 2 groepen met telkens 2 categorien. Men blijft onderverdelingen maken tot men op 1 observatie uitkomt. Deze techniek wordt hoofdzakelijk gebruikt om gegevens te verkennen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,9554	24,67	692,4	935,9
0,9922	25,59	877,3	937,1
0,9778	26,09	536,8	885,1
0,9808	28,37	705,9	892,4
0,9811	27,34	951	987,3
1,0014	24,46	755,7	946,3
1,0183	27,46	695,5	799,6
1,0622	30,23	744,8	875,4
1,0773	32,33	672,1	846,2
1,0807	29,87	666,6	880,6
1,0848	24,87	760,8	885,7
1,1582	25,48	756	868,9
1,1663	27,28	604,4	882,5
1,1372	28,24	883,9	789,6
1,1139	29,58	527,9	773,3
1,1222	26,95	756,2	804,3
1,1692	29,08	812,9	817,8
1,1702	28,76	655,6	836,7
1,2286	29,59	707,6	721,8
1,2613	30,7	612,6	760,8
1,2646	30,52	659,2	841,4
1,2262	32,67	833,4	1045,6
1,1985	33,19	727,8	949,2
1,2007	37,13	797,2	850,1
1,2138	35,54	753	957,4
1,2266	37,75	762	851,8
1,2176	41,84	613,7	913,9
1,2218	42,94	759,2	888
1,249	49,14	816,4	973,8
1,2991	44,61	736,8	927,6
1,3408	40,22	680,1	833
1,3119	44,23	736,5	879,5
1,3014	45,85	637,2	797,3
1,3201	53,38	801,9	834,5
1,2938	53,26	772,3	735,1
1,2694	51,8	897,3	835
1,2165	55,3	792,1	892,8
1,2037	57,81	826,8	697,2
1,2292	63,96	666,8	821,1
1,2256	63,77	906,6	732,7
1,2015	59,15	871,4	797,6
1,1786	56,12	891	866,3
1,1856	57,42	739,2	826,3
1,2103	63,52	833,6	778,6
1,1938	61,71	715,6	779,2
1,202	63,01	871,6	951
1,2271	68,18	751,6	692,3
1,277	72,03	1005,5	841,4
1,265	69,75	681,2	857,3
1,2684	74,41	837,3	760,7
1,2811	74,33	674,7	841,2
1,2727	64,24	806,3	810,3
1,2611	60,03	860,2	1007,4
1,2881	59,44	689,8	931,3
1,3213	62,5	691,6	931,2
1,2999	55,04	682,6	855,8
1,3074	58,34	800,1	858,4
1,3242	61,92	1023,7	925,9
1,3516	67,65	733,5	930,7
1,3511	67,68	875,3	1035,6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23998&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23998&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23998&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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132






Summary of Dendrogram
LabelHeight
13.55171820954313
26.17709570591225
313.7935389222636
414.8524515824829
515.9157630593070
617.3367105371810
718.2864340154115
821.2766415491261
922.9603023693069
1023.4807230776652
1123.9574538215146
1224.0045540826685
1325.9627080484148
1430.0276753325311
1530.7832487194193
1632.0165090265730
1732.2187933454994
1832.890433259536
1934.2882229707228
2035.8391717494978
2136.5519672828974
2239.458846675999
2340.3236954660656
2441.0608083214395
2541.5701628558574
2644.6459774251836
2746.550339504777
2847.0502774624601
2947.6839422844619
3049.8291164771161
3157.1560355122889
3259.1914385034771
3362.1049710606072
3467.585094195181
3573.8606206536125
3677.3688948868007
3785.100152938341
3887.0270321672525
3988.9028936480832
4091.3446041737952
4191.6832849378526
4291.8087119513412
43101.294456752149
44116.953571840468
45117.380751476042
46130.837304667685
47153.776557144992
48178.934442089598
49197.193712755989
50222.386740868386
51248.853392374073
52250.403232666647
53337.357806169321
54405.499956474029
55415.674685492056
56672.162794595362
571033.24025008977
581295.31024441925
591982.58651477763

\begin{tabular}{lllllllll}
\hline
Summary of Dendrogram \tabularnewline
Label & Height \tabularnewline
1 & 3.55171820954313 \tabularnewline
2 & 6.17709570591225 \tabularnewline
3 & 13.7935389222636 \tabularnewline
4 & 14.8524515824829 \tabularnewline
5 & 15.9157630593070 \tabularnewline
6 & 17.3367105371810 \tabularnewline
7 & 18.2864340154115 \tabularnewline
8 & 21.2766415491261 \tabularnewline
9 & 22.9603023693069 \tabularnewline
10 & 23.4807230776652 \tabularnewline
11 & 23.9574538215146 \tabularnewline
12 & 24.0045540826685 \tabularnewline
13 & 25.9627080484148 \tabularnewline
14 & 30.0276753325311 \tabularnewline
15 & 30.7832487194193 \tabularnewline
16 & 32.0165090265730 \tabularnewline
17 & 32.2187933454994 \tabularnewline
18 & 32.890433259536 \tabularnewline
19 & 34.2882229707228 \tabularnewline
20 & 35.8391717494978 \tabularnewline
21 & 36.5519672828974 \tabularnewline
22 & 39.458846675999 \tabularnewline
23 & 40.3236954660656 \tabularnewline
24 & 41.0608083214395 \tabularnewline
25 & 41.5701628558574 \tabularnewline
26 & 44.6459774251836 \tabularnewline
27 & 46.550339504777 \tabularnewline
28 & 47.0502774624601 \tabularnewline
29 & 47.6839422844619 \tabularnewline
30 & 49.8291164771161 \tabularnewline
31 & 57.1560355122889 \tabularnewline
32 & 59.1914385034771 \tabularnewline
33 & 62.1049710606072 \tabularnewline
34 & 67.585094195181 \tabularnewline
35 & 73.8606206536125 \tabularnewline
36 & 77.3688948868007 \tabularnewline
37 & 85.100152938341 \tabularnewline
38 & 87.0270321672525 \tabularnewline
39 & 88.9028936480832 \tabularnewline
40 & 91.3446041737952 \tabularnewline
41 & 91.6832849378526 \tabularnewline
42 & 91.8087119513412 \tabularnewline
43 & 101.294456752149 \tabularnewline
44 & 116.953571840468 \tabularnewline
45 & 117.380751476042 \tabularnewline
46 & 130.837304667685 \tabularnewline
47 & 153.776557144992 \tabularnewline
48 & 178.934442089598 \tabularnewline
49 & 197.193712755989 \tabularnewline
50 & 222.386740868386 \tabularnewline
51 & 248.853392374073 \tabularnewline
52 & 250.403232666647 \tabularnewline
53 & 337.357806169321 \tabularnewline
54 & 405.499956474029 \tabularnewline
55 & 415.674685492056 \tabularnewline
56 & 672.162794595362 \tabularnewline
57 & 1033.24025008977 \tabularnewline
58 & 1295.31024441925 \tabularnewline
59 & 1982.58651477763 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23998&T=1

[TABLE]
[ROW][C]Summary of Dendrogram[/C][/ROW]
[ROW][C]Label[/C][C]Height[/C][/ROW]
[ROW][C]1[/C][C]3.55171820954313[/C][/ROW]
[ROW][C]2[/C][C]6.17709570591225[/C][/ROW]
[ROW][C]3[/C][C]13.7935389222636[/C][/ROW]
[ROW][C]4[/C][C]14.8524515824829[/C][/ROW]
[ROW][C]5[/C][C]15.9157630593070[/C][/ROW]
[ROW][C]6[/C][C]17.3367105371810[/C][/ROW]
[ROW][C]7[/C][C]18.2864340154115[/C][/ROW]
[ROW][C]8[/C][C]21.2766415491261[/C][/ROW]
[ROW][C]9[/C][C]22.9603023693069[/C][/ROW]
[ROW][C]10[/C][C]23.4807230776652[/C][/ROW]
[ROW][C]11[/C][C]23.9574538215146[/C][/ROW]
[ROW][C]12[/C][C]24.0045540826685[/C][/ROW]
[ROW][C]13[/C][C]25.9627080484148[/C][/ROW]
[ROW][C]14[/C][C]30.0276753325311[/C][/ROW]
[ROW][C]15[/C][C]30.7832487194193[/C][/ROW]
[ROW][C]16[/C][C]32.0165090265730[/C][/ROW]
[ROW][C]17[/C][C]32.2187933454994[/C][/ROW]
[ROW][C]18[/C][C]32.890433259536[/C][/ROW]
[ROW][C]19[/C][C]34.2882229707228[/C][/ROW]
[ROW][C]20[/C][C]35.8391717494978[/C][/ROW]
[ROW][C]21[/C][C]36.5519672828974[/C][/ROW]
[ROW][C]22[/C][C]39.458846675999[/C][/ROW]
[ROW][C]23[/C][C]40.3236954660656[/C][/ROW]
[ROW][C]24[/C][C]41.0608083214395[/C][/ROW]
[ROW][C]25[/C][C]41.5701628558574[/C][/ROW]
[ROW][C]26[/C][C]44.6459774251836[/C][/ROW]
[ROW][C]27[/C][C]46.550339504777[/C][/ROW]
[ROW][C]28[/C][C]47.0502774624601[/C][/ROW]
[ROW][C]29[/C][C]47.6839422844619[/C][/ROW]
[ROW][C]30[/C][C]49.8291164771161[/C][/ROW]
[ROW][C]31[/C][C]57.1560355122889[/C][/ROW]
[ROW][C]32[/C][C]59.1914385034771[/C][/ROW]
[ROW][C]33[/C][C]62.1049710606072[/C][/ROW]
[ROW][C]34[/C][C]67.585094195181[/C][/ROW]
[ROW][C]35[/C][C]73.8606206536125[/C][/ROW]
[ROW][C]36[/C][C]77.3688948868007[/C][/ROW]
[ROW][C]37[/C][C]85.100152938341[/C][/ROW]
[ROW][C]38[/C][C]87.0270321672525[/C][/ROW]
[ROW][C]39[/C][C]88.9028936480832[/C][/ROW]
[ROW][C]40[/C][C]91.3446041737952[/C][/ROW]
[ROW][C]41[/C][C]91.6832849378526[/C][/ROW]
[ROW][C]42[/C][C]91.8087119513412[/C][/ROW]
[ROW][C]43[/C][C]101.294456752149[/C][/ROW]
[ROW][C]44[/C][C]116.953571840468[/C][/ROW]
[ROW][C]45[/C][C]117.380751476042[/C][/ROW]
[ROW][C]46[/C][C]130.837304667685[/C][/ROW]
[ROW][C]47[/C][C]153.776557144992[/C][/ROW]
[ROW][C]48[/C][C]178.934442089598[/C][/ROW]
[ROW][C]49[/C][C]197.193712755989[/C][/ROW]
[ROW][C]50[/C][C]222.386740868386[/C][/ROW]
[ROW][C]51[/C][C]248.853392374073[/C][/ROW]
[ROW][C]52[/C][C]250.403232666647[/C][/ROW]
[ROW][C]53[/C][C]337.357806169321[/C][/ROW]
[ROW][C]54[/C][C]405.499956474029[/C][/ROW]
[ROW][C]55[/C][C]415.674685492056[/C][/ROW]
[ROW][C]56[/C][C]672.162794595362[/C][/ROW]
[ROW][C]57[/C][C]1033.24025008977[/C][/ROW]
[ROW][C]58[/C][C]1295.31024441925[/C][/ROW]
[ROW][C]59[/C][C]1982.58651477763[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23998&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23998&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
13.55171820954313
26.17709570591225
313.7935389222636
414.8524515824829
515.9157630593070
617.3367105371810
718.2864340154115
821.2766415491261
922.9603023693069
1023.4807230776652
1123.9574538215146
1224.0045540826685
1325.9627080484148
1430.0276753325311
1530.7832487194193
1632.0165090265730
1732.2187933454994
1832.890433259536
1934.2882229707228
2035.8391717494978
2136.5519672828974
2239.458846675999
2340.3236954660656
2441.0608083214395
2541.5701628558574
2644.6459774251836
2746.550339504777
2847.0502774624601
2947.6839422844619
3049.8291164771161
3157.1560355122889
3259.1914385034771
3362.1049710606072
3467.585094195181
3573.8606206536125
3677.3688948868007
3785.100152938341
3887.0270321672525
3988.9028936480832
4091.3446041737952
4191.6832849378526
4291.8087119513412
43101.294456752149
44116.953571840468
45117.380751476042
46130.837304667685
47153.776557144992
48178.934442089598
49197.193712755989
50222.386740868386
51248.853392374073
52250.403232666647
53337.357806169321
54405.499956474029
55415.674685492056
56672.162794595362
571033.24025008977
581295.31024441925
591982.58651477763


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')
}