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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 computationSun, 09 Nov 2008 10:18:53 -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/09/t1226251194b11kc7h3fw4xwcp.htm/, Retrieved Sun, 19 May 2024 11:18:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22783, Retrieved Sun, 19 May 2024 11:18:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161

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] [Q2 ] [2008-11-09 17:18:53] [54ae75b68e6a45c6d55fa4235827d5b3] [Current]
-    D    [Hierarchical Clustering] [Q2] [2008-11-24 20:17:22] [491a70d26f8c977398d8a0c1c87d3dd4]
Feedback Forum
2008-11-15 11:04:01 [Tom Ardies] [reply] 
Clustering wordt gebruikt om te zien of data in zelfde groepen behoort. Hiermee kan je dus ook zien of er trends ontstaan, zoals de data van een bepaalde zomermaand dat telkens in een zelfde cluster terecht komt.

Clustering is eigenlijk niet bedoeld voor tijdreeksen.
2008-11-22 10:08:35 [Astrid Sniekers] [reply] 
Ik ga akkoord met de uitleg van mijn collega hierboven.
2008-11-24 19:17:27 [Liese Tormans] [reply] 
De student heeft hier de juiste software gebruikt namelijk hierachical clustering. Deze berekeningsmethode gaat gelijkaardige observaties groeperen en zo clusters vormen. En dit telkens opnieuw (De clusters worden steeds verder onderverdeeld).Daarnaast is het bij deze methode ook mogelijk om bepaalde trends te zien.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	15,1	467	98,6
14,9	14,8	460	98
16,8	16,1	448	106,8
14,3	14,3	443	96,7
15,5	15,2	436	100,2
15,6	14,9	431	107,7
14,6	13,1	484	92
12,5	12,6	510	98,4
14,8	13,6	513	107,4
15,9	14,4	503	117,7
14,8	14	471	105,7
12,9	12,9	471	97,5
14,3	13,4	476	99,9
14,2	13,5	475	98,2
15,9	14,8	470	104,5
15,3	14,3	461	100,8
15,5	14,3	455	101,5
15,1	14	456	103,9
15	13,2	517	99,6
12,1	12,2	525	98,4
15,8	14,3	523	112,7
16,9	15,7	519	118,4
15,1	14,2	509	108,1
13,7	14,6	512	105,4
14,8	14,5	519	114,6
14,7	14,3	517	106,9
16	15,3	510	115,9
15,4	14,4	509	109,8
15	13,7	501	101,8
15,5	14,2	507	114,2
15,1	13,5	569	110,8
11,7	11,9	580	108,4
16,3	14,6	578	127,5
16,7	15,6	565	128,6
15	14,1	547	116,6
14,9	14,9	555	127,4
14,6	14,2	562	105
15,3	14,6	561	108,3
17,9	17,2	555	125
16,4	15,4	544	111,6
15,4	14,3	537	106,5
17,9	17,5	543	130,3
15,9	14,5	594	115
13,9	14,4	611	116,1
17,8	16,6	613	134
17,9	16,7	611	126,5
17,4	16,6	594	125,8
16,7	16,9	595	136,4
16	15,7	591	114,9
16,6	16,4	589	110,9
19,1	18,4	584	125,5
17,8	16,9	573	116,8
17,2	16,5	567	116,8
18,6	18,3	569	125,5
16,3	15,1	621	104,2
15,1	15,7	629	115,1
19,2	18,1	628	132,8
17,7	16,8	612	123,3
19,1	18,9	595	124,8
18	19	597	122
17,5	18,1	593	117,4
17,8	17,8	590	117,9
21,1	21,5	580	137,4
17,2	17,1	574	114,6
19,4	18,7	573	124,7
19,8	19	573	129,6
17,6	16,4	620	109,4
16,2	16,9	626	120,9
19,5	18,6	620	134,9
19,9	19,3	588	136,3
20	19,4	566	133,2
17,3	17,6	557	127,2

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=22783&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=22783&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22783&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
11.73781471969828
21.97737199332852
32.0712315177208
42.49799919935936
52.64764045897475
62.68514431641951
73.04138126514911
83.05941170815567
93.07083050655682
103.19061122670876
113.23419232575925
123.36005952328229
133.54118624192515
143.65376518128902
154.17612260356422
164.50444225182209
174.72134806872578
184.97031164946095
195.14392845984467
205.38242459293929
215.60535458289661
225.82096510129622
236.13595958265698
246.31966340767748
256.4831838042978
266.7297845433565
276.89129886160802
287.1941340607887
297.30821455623738
307.52994023880668
317.55314504031268
327.89176786277954
337.96868872525461
348.0628778981205
358.11482427699588
369.7767571115565
379.78887484361375
389.89417827264283
3910.2055830747398
4012.1626160512122
4112.8728459880171
4213.1425049937764
4313.2571198706375
4413.2930450512512
4513.8327848334833
4614.0846573386397
4714.2511624427832
4815.7339030230484
4917.6380899900536
5017.8068681733093
5118.2756993884405
5221.3991872428216
5321.4884615789213
5421.6631237113305
5525.1751611561088
5626.6299793378801
5727.4704230634099
5831.0932947418645
5931.1835991374482
6043.3794249683866
6146.2150132799487
6249.0997342838128
6351.042406278264
6451.6968654703477
6598.1717416627388
66111.984171034368
67125.760165861199
68202.127946445232
69435.417085556479
70585.19502938307
712319.68169433435

\begin{tabular}{lllllllll}
\hline
Summary of Dendrogram \tabularnewline
Label & Height \tabularnewline
1 & 1.73781471969828 \tabularnewline
2 & 1.97737199332852 \tabularnewline
3 & 2.0712315177208 \tabularnewline
4 & 2.49799919935936 \tabularnewline
5 & 2.64764045897475 \tabularnewline
6 & 2.68514431641951 \tabularnewline
7 & 3.04138126514911 \tabularnewline
8 & 3.05941170815567 \tabularnewline
9 & 3.07083050655682 \tabularnewline
10 & 3.19061122670876 \tabularnewline
11 & 3.23419232575925 \tabularnewline
12 & 3.36005952328229 \tabularnewline
13 & 3.54118624192515 \tabularnewline
14 & 3.65376518128902 \tabularnewline
15 & 4.17612260356422 \tabularnewline
16 & 4.50444225182209 \tabularnewline
17 & 4.72134806872578 \tabularnewline
18 & 4.97031164946095 \tabularnewline
19 & 5.14392845984467 \tabularnewline
20 & 5.38242459293929 \tabularnewline
21 & 5.60535458289661 \tabularnewline
22 & 5.82096510129622 \tabularnewline
23 & 6.13595958265698 \tabularnewline
24 & 6.31966340767748 \tabularnewline
25 & 6.4831838042978 \tabularnewline
26 & 6.7297845433565 \tabularnewline
27 & 6.89129886160802 \tabularnewline
28 & 7.1941340607887 \tabularnewline
29 & 7.30821455623738 \tabularnewline
30 & 7.52994023880668 \tabularnewline
31 & 7.55314504031268 \tabularnewline
32 & 7.89176786277954 \tabularnewline
33 & 7.96868872525461 \tabularnewline
34 & 8.0628778981205 \tabularnewline
35 & 8.11482427699588 \tabularnewline
36 & 9.7767571115565 \tabularnewline
37 & 9.78887484361375 \tabularnewline
38 & 9.89417827264283 \tabularnewline
39 & 10.2055830747398 \tabularnewline
40 & 12.1626160512122 \tabularnewline
41 & 12.8728459880171 \tabularnewline
42 & 13.1425049937764 \tabularnewline
43 & 13.2571198706375 \tabularnewline
44 & 13.2930450512512 \tabularnewline
45 & 13.8327848334833 \tabularnewline
46 & 14.0846573386397 \tabularnewline
47 & 14.2511624427832 \tabularnewline
48 & 15.7339030230484 \tabularnewline
49 & 17.6380899900536 \tabularnewline
50 & 17.8068681733093 \tabularnewline
51 & 18.2756993884405 \tabularnewline
52 & 21.3991872428216 \tabularnewline
53 & 21.4884615789213 \tabularnewline
54 & 21.6631237113305 \tabularnewline
55 & 25.1751611561088 \tabularnewline
56 & 26.6299793378801 \tabularnewline
57 & 27.4704230634099 \tabularnewline
58 & 31.0932947418645 \tabularnewline
59 & 31.1835991374482 \tabularnewline
60 & 43.3794249683866 \tabularnewline
61 & 46.2150132799487 \tabularnewline
62 & 49.0997342838128 \tabularnewline
63 & 51.042406278264 \tabularnewline
64 & 51.6968654703477 \tabularnewline
65 & 98.1717416627388 \tabularnewline
66 & 111.984171034368 \tabularnewline
67 & 125.760165861199 \tabularnewline
68 & 202.127946445232 \tabularnewline
69 & 435.417085556479 \tabularnewline
70 & 585.19502938307 \tabularnewline
71 & 2319.68169433435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22783&T=1

[TABLE]
[ROW][C]Summary of Dendrogram[/C][/ROW]
[ROW][C]Label[/C][C]Height[/C][/ROW]
[ROW][C]1[/C][C]1.73781471969828[/C][/ROW]
[ROW][C]2[/C][C]1.97737199332852[/C][/ROW]
[ROW][C]3[/C][C]2.0712315177208[/C][/ROW]
[ROW][C]4[/C][C]2.49799919935936[/C][/ROW]
[ROW][C]5[/C][C]2.64764045897475[/C][/ROW]
[ROW][C]6[/C][C]2.68514431641951[/C][/ROW]
[ROW][C]7[/C][C]3.04138126514911[/C][/ROW]
[ROW][C]8[/C][C]3.05941170815567[/C][/ROW]
[ROW][C]9[/C][C]3.07083050655682[/C][/ROW]
[ROW][C]10[/C][C]3.19061122670876[/C][/ROW]
[ROW][C]11[/C][C]3.23419232575925[/C][/ROW]
[ROW][C]12[/C][C]3.36005952328229[/C][/ROW]
[ROW][C]13[/C][C]3.54118624192515[/C][/ROW]
[ROW][C]14[/C][C]3.65376518128902[/C][/ROW]
[ROW][C]15[/C][C]4.17612260356422[/C][/ROW]
[ROW][C]16[/C][C]4.50444225182209[/C][/ROW]
[ROW][C]17[/C][C]4.72134806872578[/C][/ROW]
[ROW][C]18[/C][C]4.97031164946095[/C][/ROW]
[ROW][C]19[/C][C]5.14392845984467[/C][/ROW]
[ROW][C]20[/C][C]5.38242459293929[/C][/ROW]
[ROW][C]21[/C][C]5.60535458289661[/C][/ROW]
[ROW][C]22[/C][C]5.82096510129622[/C][/ROW]
[ROW][C]23[/C][C]6.13595958265698[/C][/ROW]
[ROW][C]24[/C][C]6.31966340767748[/C][/ROW]
[ROW][C]25[/C][C]6.4831838042978[/C][/ROW]
[ROW][C]26[/C][C]6.7297845433565[/C][/ROW]
[ROW][C]27[/C][C]6.89129886160802[/C][/ROW]
[ROW][C]28[/C][C]7.1941340607887[/C][/ROW]
[ROW][C]29[/C][C]7.30821455623738[/C][/ROW]
[ROW][C]30[/C][C]7.52994023880668[/C][/ROW]
[ROW][C]31[/C][C]7.55314504031268[/C][/ROW]
[ROW][C]32[/C][C]7.89176786277954[/C][/ROW]
[ROW][C]33[/C][C]7.96868872525461[/C][/ROW]
[ROW][C]34[/C][C]8.0628778981205[/C][/ROW]
[ROW][C]35[/C][C]8.11482427699588[/C][/ROW]
[ROW][C]36[/C][C]9.7767571115565[/C][/ROW]
[ROW][C]37[/C][C]9.78887484361375[/C][/ROW]
[ROW][C]38[/C][C]9.89417827264283[/C][/ROW]
[ROW][C]39[/C][C]10.2055830747398[/C][/ROW]
[ROW][C]40[/C][C]12.1626160512122[/C][/ROW]
[ROW][C]41[/C][C]12.8728459880171[/C][/ROW]
[ROW][C]42[/C][C]13.1425049937764[/C][/ROW]
[ROW][C]43[/C][C]13.2571198706375[/C][/ROW]
[ROW][C]44[/C][C]13.2930450512512[/C][/ROW]
[ROW][C]45[/C][C]13.8327848334833[/C][/ROW]
[ROW][C]46[/C][C]14.0846573386397[/C][/ROW]
[ROW][C]47[/C][C]14.2511624427832[/C][/ROW]
[ROW][C]48[/C][C]15.7339030230484[/C][/ROW]
[ROW][C]49[/C][C]17.6380899900536[/C][/ROW]
[ROW][C]50[/C][C]17.8068681733093[/C][/ROW]
[ROW][C]51[/C][C]18.2756993884405[/C][/ROW]
[ROW][C]52[/C][C]21.3991872428216[/C][/ROW]
[ROW][C]53[/C][C]21.4884615789213[/C][/ROW]
[ROW][C]54[/C][C]21.6631237113305[/C][/ROW]
[ROW][C]55[/C][C]25.1751611561088[/C][/ROW]
[ROW][C]56[/C][C]26.6299793378801[/C][/ROW]
[ROW][C]57[/C][C]27.4704230634099[/C][/ROW]
[ROW][C]58[/C][C]31.0932947418645[/C][/ROW]
[ROW][C]59[/C][C]31.1835991374482[/C][/ROW]
[ROW][C]60[/C][C]43.3794249683866[/C][/ROW]
[ROW][C]61[/C][C]46.2150132799487[/C][/ROW]
[ROW][C]62[/C][C]49.0997342838128[/C][/ROW]
[ROW][C]63[/C][C]51.042406278264[/C][/ROW]
[ROW][C]64[/C][C]51.6968654703477[/C][/ROW]
[ROW][C]65[/C][C]98.1717416627388[/C][/ROW]
[ROW][C]66[/C][C]111.984171034368[/C][/ROW]
[ROW][C]67[/C][C]125.760165861199[/C][/ROW]
[ROW][C]68[/C][C]202.127946445232[/C][/ROW]
[ROW][C]69[/C][C]435.417085556479[/C][/ROW]
[ROW][C]70[/C][C]585.19502938307[/C][/ROW]
[ROW][C]71[/C][C]2319.68169433435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22783&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22783&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
11.73781471969828
21.97737199332852
32.0712315177208
42.49799919935936
52.64764045897475
62.68514431641951
73.04138126514911
83.05941170815567
93.07083050655682
103.19061122670876
113.23419232575925
123.36005952328229
133.54118624192515
143.65376518128902
154.17612260356422
164.50444225182209
174.72134806872578
184.97031164946095
195.14392845984467
205.38242459293929
215.60535458289661
225.82096510129622
236.13595958265698
246.31966340767748
256.4831838042978
266.7297845433565
276.89129886160802
287.1941340607887
297.30821455623738
307.52994023880668
317.55314504031268
327.89176786277954
337.96868872525461
348.0628778981205
358.11482427699588
369.7767571115565
379.78887484361375
389.89417827264283
3910.2055830747398
4012.1626160512122
4112.8728459880171
4213.1425049937764
4313.2571198706375
4413.2930450512512
4513.8327848334833
4614.0846573386397
4714.2511624427832
4815.7339030230484
4917.6380899900536
5017.8068681733093
5118.2756993884405
5221.3991872428216
5321.4884615789213
5421.6631237113305
5525.1751611561088
5626.6299793378801
5727.4704230634099
5831.0932947418645
5931.1835991374482
6043.3794249683866
6146.2150132799487
6249.0997342838128
6351.042406278264
6451.6968654703477
6598.1717416627388
66111.984171034368
67125.760165861199
68202.127946445232
69435.417085556479
70585.19502938307
712319.68169433435


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')
}