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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 computationTue, 11 Nov 2008 16:34:50 -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/12/t1226446582g8a21jb2yd59c3n.htm/, Retrieved Sun, 19 May 2024 08:52:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24010, Retrieved Sun, 19 May 2024 08:52:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact249

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2008-11-11 14:08:00] [adb6b6905cde49db36d59ca44433140d]
F RMPD    [Hierarchical Clustering] [Hierarchical Clus...] [2008-11-11 23:34:50] [d592f629d96b926609f311957d74fcca] [Current]
F    D      [Hierarchical Clustering] [hierarchical clus...] [2008-11-13 08:23:58] [3b5d63cebdc58ed6c519cdb5b6a36d46]
F RMPD      [Box-Cox Linearity Plot] [box cox linearity...] [2008-11-13 08:29:17] [3b5d63cebdc58ed6c519cdb5b6a36d46]
- RMPD      [Box-Cox Normality Plot] [box cox normal plot] [2008-11-13 08:38:29] [3b5d63cebdc58ed6c519cdb5b6a36d46]
-    D      [Hierarchical Clustering] [Hierarchical Clus...] [2008-12-15 11:20:36] [b591abfa820a394aeb0c5ebd9cfa1091]
Feedback Forum
2008-11-19 20:06:09 [Evelien Blockx] [reply] 
Je verwoordde de kern van de zaak maar je trad niet verder in detail. Zoals je zei zijn er in het dendrogram clusters te zien. De cijfers onderaan stellen de maanden voor. Met het dendrogram kan je kijken welke observaties iets gelijkaardig hebben.

In dit dendrogram zie je duidelijk 2 grote clusters. De eerste grote cluster geeft de maanden t.e.m. 24 weer. De andere cluster de rest van de maanden.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2120.88	9682.35	10413.00	29.08
2174.56	9762.12	10709.00	28.76
2196.72	10124.63	10662.00	29.59
2350.44	10540.05	10570.00	30.70
2440.25	10601.61	10297.00	30.52
2408.64	10323.73	10635.00	32.67
2472.81	10418.40	10872.00	33.19
2407.60	10092.96	10296.00	37.13
2454.62	10364.91	10383.00	35.54
2448.05	10152.09	10431.00	37.75
2497.84	10032.80	10574.00	41.84
2645.64	10204.59	10653.00	42.94
2756.76	10001.60	10805.00	49.14
2849.27	10411.75	10872.00	44.61
2921.44	10673.38	10625.00	40.22
2981.85	10539.51	10407.00	44.23
3080.58	10723.78	10463.00	45.85
3106.22	10682.06	10556.00	53.38
3119.31	10283.19	10646.00	53.26
3061.26	10377.18	10702.00	51.80
3097.31	10486.64	11353.00	55.30
3161.69	10545.38	11346.00	57.81
3257.16	10554.27	11451.00	63.96
3277.01	10532.54	11964.00	63.77
3295.32	10324.31	12574.00	59.15
3363.99	10695.25	13031.00	56.12
3494.17	10827.81	13812.00	57.42
3667.03	10872.48	14544.00	63.52
3813.06	10971.19	14931.00	61.71
3917.96	11145.65	14886.00	63.01
3895.51	11234.68	16005.00	68.18
3801.06	11333.88	17064.00	72.03
3570.12	10997.97	15168.00	69.75
3701.61	11036.89	16050.00	74.41
3862.27	11257.35	15839.00	74.33
3970.10	11533.59	15137.00	64.24
4138.52	11963.12	14954.00	60.03
4199.75	12185.15	15648.00	59.44
4290.89	12377.62	15305.00	62.50
4443.91	12512.89	15579.00	55.04
4502.64	12631.48	16348.00	58.34
4356.98	12268.53	15928.00	61.92
4591.27	12754.80	16171.00	67.65
4696.96	13407.75	15937.00	67.68
4621.40	13480.21	15713.00	70.30
4562.84	13673.28	15594.00	75.26
4202.52	13239.71	15683.00	71.44
4296.49	13557.69	16438.00	76.36
4435.23	13901.28	17032.00	81.71
4105.18	13200.58	17696.00	92.60
4116.68	13406.97	17745.00	90.60
3844.49	12538.12	19394.00	92.23
3720.98	12419.57	20148.00	94.09
3674.40	12193.88	20108.00	102.79
3857.62	12656.63	18584.00	109.65
3801.06	12812.48	18441.00	124.05
3504.37	12056.67	18391.00	132.69
3032.60	11322.38	19178.00	135.81
3047.03	11530.75	18079.00	116.07
2962.34	11114.08	18483.00	101.42

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=24010&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=24010&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24010&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
187.4669771971113
2105.373947918829
3123.863046143714
4152.833058596627
5170.917082235802
6198.735264311215
7208.487389546705
8212.447833832213
9219.418814371056
10233.260505229668
11233.406888073167
12234.054187102047
13234.288070759056
14239.997612696460
15251.911787131219
16252.298607407969
17270.829575187704
18311.224834645309
19329.566185530684
20331.782530733612
21336.936049349785
22341.825605974742
23360.061676925846
24389.479811514272
25391.369671533476
26458.804830050968
27471.90916610222
28496.354753578527
29540.725104511714
30586.700014913925
31592.596180716684
32607.716095017763
33616.722805534414
34661.76347457323
35700.119631348815
36776.399749666492
37819.077449205668
38884.3486421227
39888.740527736141
40951.123253591491
41977.954070492173
421036.82990526438
431238.45529679234
441419.08076808223
451546.26254543794
461563.44263249763
471564.31540552602
481840.82410075718
493012.2511165587
503314.90551163345
513449.11830834474
524014.88742111584
534053.76982091904
545477.68004552776
555798.99947861306
567832.78107435285
5718363.5659534285
5828767.3356093909
59129969.051671641

\begin{tabular}{lllllllll}
\hline
Summary of Dendrogram \tabularnewline
Label & Height \tabularnewline
1 & 87.4669771971113 \tabularnewline
2 & 105.373947918829 \tabularnewline
3 & 123.863046143714 \tabularnewline
4 & 152.833058596627 \tabularnewline
5 & 170.917082235802 \tabularnewline
6 & 198.735264311215 \tabularnewline
7 & 208.487389546705 \tabularnewline
8 & 212.447833832213 \tabularnewline
9 & 219.418814371056 \tabularnewline
10 & 233.260505229668 \tabularnewline
11 & 233.406888073167 \tabularnewline
12 & 234.054187102047 \tabularnewline
13 & 234.288070759056 \tabularnewline
14 & 239.997612696460 \tabularnewline
15 & 251.911787131219 \tabularnewline
16 & 252.298607407969 \tabularnewline
17 & 270.829575187704 \tabularnewline
18 & 311.224834645309 \tabularnewline
19 & 329.566185530684 \tabularnewline
20 & 331.782530733612 \tabularnewline
21 & 336.936049349785 \tabularnewline
22 & 341.825605974742 \tabularnewline
23 & 360.061676925846 \tabularnewline
24 & 389.479811514272 \tabularnewline
25 & 391.369671533476 \tabularnewline
26 & 458.804830050968 \tabularnewline
27 & 471.90916610222 \tabularnewline
28 & 496.354753578527 \tabularnewline
29 & 540.725104511714 \tabularnewline
30 & 586.700014913925 \tabularnewline
31 & 592.596180716684 \tabularnewline
32 & 607.716095017763 \tabularnewline
33 & 616.722805534414 \tabularnewline
34 & 661.76347457323 \tabularnewline
35 & 700.119631348815 \tabularnewline
36 & 776.399749666492 \tabularnewline
37 & 819.077449205668 \tabularnewline
38 & 884.3486421227 \tabularnewline
39 & 888.740527736141 \tabularnewline
40 & 951.123253591491 \tabularnewline
41 & 977.954070492173 \tabularnewline
42 & 1036.82990526438 \tabularnewline
43 & 1238.45529679234 \tabularnewline
44 & 1419.08076808223 \tabularnewline
45 & 1546.26254543794 \tabularnewline
46 & 1563.44263249763 \tabularnewline
47 & 1564.31540552602 \tabularnewline
48 & 1840.82410075718 \tabularnewline
49 & 3012.2511165587 \tabularnewline
50 & 3314.90551163345 \tabularnewline
51 & 3449.11830834474 \tabularnewline
52 & 4014.88742111584 \tabularnewline
53 & 4053.76982091904 \tabularnewline
54 & 5477.68004552776 \tabularnewline
55 & 5798.99947861306 \tabularnewline
56 & 7832.78107435285 \tabularnewline
57 & 18363.5659534285 \tabularnewline
58 & 28767.3356093909 \tabularnewline
59 & 129969.051671641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24010&T=1

[TABLE]
[ROW][C]Summary of Dendrogram[/C][/ROW]
[ROW][C]Label[/C][C]Height[/C][/ROW]
[ROW][C]1[/C][C]87.4669771971113[/C][/ROW]
[ROW][C]2[/C][C]105.373947918829[/C][/ROW]
[ROW][C]3[/C][C]123.863046143714[/C][/ROW]
[ROW][C]4[/C][C]152.833058596627[/C][/ROW]
[ROW][C]5[/C][C]170.917082235802[/C][/ROW]
[ROW][C]6[/C][C]198.735264311215[/C][/ROW]
[ROW][C]7[/C][C]208.487389546705[/C][/ROW]
[ROW][C]8[/C][C]212.447833832213[/C][/ROW]
[ROW][C]9[/C][C]219.418814371056[/C][/ROW]
[ROW][C]10[/C][C]233.260505229668[/C][/ROW]
[ROW][C]11[/C][C]233.406888073167[/C][/ROW]
[ROW][C]12[/C][C]234.054187102047[/C][/ROW]
[ROW][C]13[/C][C]234.288070759056[/C][/ROW]
[ROW][C]14[/C][C]239.997612696460[/C][/ROW]
[ROW][C]15[/C][C]251.911787131219[/C][/ROW]
[ROW][C]16[/C][C]252.298607407969[/C][/ROW]
[ROW][C]17[/C][C]270.829575187704[/C][/ROW]
[ROW][C]18[/C][C]311.224834645309[/C][/ROW]
[ROW][C]19[/C][C]329.566185530684[/C][/ROW]
[ROW][C]20[/C][C]331.782530733612[/C][/ROW]
[ROW][C]21[/C][C]336.936049349785[/C][/ROW]
[ROW][C]22[/C][C]341.825605974742[/C][/ROW]
[ROW][C]23[/C][C]360.061676925846[/C][/ROW]
[ROW][C]24[/C][C]389.479811514272[/C][/ROW]
[ROW][C]25[/C][C]391.369671533476[/C][/ROW]
[ROW][C]26[/C][C]458.804830050968[/C][/ROW]
[ROW][C]27[/C][C]471.90916610222[/C][/ROW]
[ROW][C]28[/C][C]496.354753578527[/C][/ROW]
[ROW][C]29[/C][C]540.725104511714[/C][/ROW]
[ROW][C]30[/C][C]586.700014913925[/C][/ROW]
[ROW][C]31[/C][C]592.596180716684[/C][/ROW]
[ROW][C]32[/C][C]607.716095017763[/C][/ROW]
[ROW][C]33[/C][C]616.722805534414[/C][/ROW]
[ROW][C]34[/C][C]661.76347457323[/C][/ROW]
[ROW][C]35[/C][C]700.119631348815[/C][/ROW]
[ROW][C]36[/C][C]776.399749666492[/C][/ROW]
[ROW][C]37[/C][C]819.077449205668[/C][/ROW]
[ROW][C]38[/C][C]884.3486421227[/C][/ROW]
[ROW][C]39[/C][C]888.740527736141[/C][/ROW]
[ROW][C]40[/C][C]951.123253591491[/C][/ROW]
[ROW][C]41[/C][C]977.954070492173[/C][/ROW]
[ROW][C]42[/C][C]1036.82990526438[/C][/ROW]
[ROW][C]43[/C][C]1238.45529679234[/C][/ROW]
[ROW][C]44[/C][C]1419.08076808223[/C][/ROW]
[ROW][C]45[/C][C]1546.26254543794[/C][/ROW]
[ROW][C]46[/C][C]1563.44263249763[/C][/ROW]
[ROW][C]47[/C][C]1564.31540552602[/C][/ROW]
[ROW][C]48[/C][C]1840.82410075718[/C][/ROW]
[ROW][C]49[/C][C]3012.2511165587[/C][/ROW]
[ROW][C]50[/C][C]3314.90551163345[/C][/ROW]
[ROW][C]51[/C][C]3449.11830834474[/C][/ROW]
[ROW][C]52[/C][C]4014.88742111584[/C][/ROW]
[ROW][C]53[/C][C]4053.76982091904[/C][/ROW]
[ROW][C]54[/C][C]5477.68004552776[/C][/ROW]
[ROW][C]55[/C][C]5798.99947861306[/C][/ROW]
[ROW][C]56[/C][C]7832.78107435285[/C][/ROW]
[ROW][C]57[/C][C]18363.5659534285[/C][/ROW]
[ROW][C]58[/C][C]28767.3356093909[/C][/ROW]
[ROW][C]59[/C][C]129969.051671641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24010&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24010&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
187.4669771971113
2105.373947918829
3123.863046143714
4152.833058596627
5170.917082235802
6198.735264311215
7208.487389546705
8212.447833832213
9219.418814371056
10233.260505229668
11233.406888073167
12234.054187102047
13234.288070759056
14239.997612696460
15251.911787131219
16252.298607407969
17270.829575187704
18311.224834645309
19329.566185530684
20331.782530733612
21336.936049349785
22341.825605974742
23360.061676925846
24389.479811514272
25391.369671533476
26458.804830050968
27471.90916610222
28496.354753578527
29540.725104511714
30586.700014913925
31592.596180716684
32607.716095017763
33616.722805534414
34661.76347457323
35700.119631348815
36776.399749666492
37819.077449205668
38884.3486421227
39888.740527736141
40951.123253591491
41977.954070492173
421036.82990526438
431238.45529679234
441419.08076808223
451546.26254543794
461563.44263249763
471564.31540552602
481840.82410075718
493012.2511165587
503314.90551163345
513449.11830834474
524014.88742111584
534053.76982091904
545477.68004552776
555798.99947861306
567832.78107435285
5718363.5659534285
5828767.3356093909
59129969.051671641


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')
}